{"id":531,"date":"2023-05-14T12:25:29","date_gmt":"2023-05-14T10:25:29","guid":{"rendered":"https:\/\/sano.empressia.dev\/?post_type=people&p=531"},"modified":"2026-01-31T20:28:59","modified_gmt":"2026-01-31T19:28:59","slug":"maciej-malawski","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/maciej-malawski\/","title":{"rendered":"Maciej Malawski"},"excerpt":{"rendered":"

Director of Sano, Research Team Leader of Extreme-scale Data and Computing, Associate Professor at AGH<\/p>\n","protected":false},"featured_media":16989,"template":"","people_teams":[19,34,41],"class_list":["post-531","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-extreme-scale-data-and-computing","people_teams-governance"],"yoast_head":"\nMaciej Malawski - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Maciej Malawski\" \/>\n<meta property=\"og:description\" content=\"Director of Sano, Research Team Leader of Extreme-scale Data and Computing, Associate Professor at AGH\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/maciej-malawski\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-31T19:28:59+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/Maciek_Malawski_Sano_Science.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"800\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/maciej-malawski\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/maciej-malawski\\\/\",\"name\":\"Maciej Malawski - 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of scientific expertise","content":"<p><span data-contrast=\"auto\">Over 20 years of experience in research in parallel and distributed computing, high performance computing (HPC), grid and cloud technologies, serverless and container-based infrastructures. Interested in innovative applications of these technologies to scientific applications, with a special focus on biomedical research.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Big data analytics, with experience in large-scale processing of scientific data in cloud infrastructures, contributed to use of Apache Spark and serverless processing of data in high energy physics in collaboration with CERN.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Scientific workflows with focus on usage of novel and emerging large-scale computing infrastructures, performance evaluation, resource management, scheduling and cost optimization.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\n"},{"title":"Highlights of accomplishments","content":"<p><span data-contrast=\"auto\">Co-author of over 50 international publications including journal and conference papers, and book chapters. Member of technical program committees of premier conferences on scientific, parallel and distributed computing (SC, ICCS, IPDPS,\u00a0CCGrid, UCC). Leadership positions in major conferences in the field: general co-chair of Euro-Par 2020 and member of Steering Committee, Area Co-chair IEEE Cluster 2021,\u00a0BoF\u00a0Vice-Chair at SC18. Member of editorial board of Future Generation Computer Systems Journal.<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\n<p><strong>Prizes and distinctions:<\/strong><\/p>\n<ul>\n<li><span data-contrast=\"auto\">2020<\/span> <span data-contrast=\"auto\">Paper: Performance evaluation of heterogeneous cloud functions published in Concurrency and Computation: Practice and Experience, was among the top most downloaded papers in 2018-2019<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">2018<\/span> <span data-contrast=\"auto\">AGH Rector\u2019s award for organizational work<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">2018<\/span> <span data-contrast=\"auto\">Publons\u00a0Peer Review Award, for placing in the top 1% of reviewers in Computer Science<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"auto\">2011<\/span> <span data-contrast=\"auto\">Executable Paper Grand Challenge \u2013\u00a01st prize<\/span><\/li>\n<\/ul>\n"},{"title":" Professional experience","content":"<ul>\n<li><b><span data-contrast=\"auto\">2019 –\u00a0ongoing<\/span><\/b> <span data-contrast=\"auto\">Associate Professor, Institute of Computer Science AGH, University of Science and Technology, Krak\u00f3w, Poland<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><span data-contrast=\"auto\">Senior Researcher, Sano Centre for Computational Medicine in Krakow, Poland<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">2001 –\u00a0ongoing<\/span><\/b> <span data-contrast=\"auto\">Researcher,\u00a0 employed\u00a0in research projects at the Academic Computer Centre CYFRONET AGH<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">2009 – 2019<\/span><\/b><span data-contrast=\"auto\">\u00a0<\/span> <span data-contrast=\"auto\">Assistant Professor, Department of Computer Science AGH<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">2015<\/span><\/b> <span data-contrast=\"auto\">Adjunct Research Assistant Professor, University of Notre Dame, Center for Research Computing, Notre Dame, USA<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">2013<\/span><\/b> <span data-contrast=\"auto\">Adjunct Research Assistant Professor, University of Notre Dame, Department of Computer Science and Engineering, Notre Dame, USA<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">2011-2012<\/span><\/b> <span data-contrast=\"auto\">Postdoctoral Research Associate, University of Notre Dame, Center for Research Computing, Notre Dame, USA<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">2001 – 2009<\/span><\/b><span data-contrast=\"auto\">\u00a0<\/span> <span data-contrast=\"auto\">Teaching and Research Assistant, Department of Computer Science AGH<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<\/ul>\n"},{"title":"Education","content":"<ul>\n<li><strong>2009<\/strong> <span data-contrast=\"auto\">Ph.D., Computer Science, AGH University of Science and Technology, Krak\u00f3w, Poland\u00a0<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><strong>2004<\/strong> <span data-contrast=\"auto\">MSc, Physics, Jagiellonian University, Krak\u00f3w, Poland<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<li><strong>2001<\/strong> <span data-contrast=\"auto\">MSc, Computer Science, AGH University of Science and Technology, Krak\u00f3w, Poland<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/li>\n<\/ul>\n"},{"title":"Contact","content":"<p><b><span data-contrast=\"auto\">Sano Centre for Computational Medicine<\/span><\/b><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Czarnowiejska 36\u00a0building\u00a0C5, 30-054,\u00a0Cracow, Poland<\/span><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><strong>Email:<\/strong>\u00a0<\/span><a class=\"mail-link\" data-enc-email=\"z.znynjfxv[at]fnabfpvrapr.bet\" data-wpel-link=\"ignore\"><span id=\"eeb-616959-129864\"><span data-contrast=\"none\">m.malawski@sanoscience.org<\/span><\/span><\/a><span data-ccp-props=\"{"201341983":0,"335559739":160,"335559740":259}\">\u00a0<\/span><\/p>\n"}],"email":"","publications":[{"ID":28615,"post_author":"8","post_date":"2026-01-31 20:24:45","post_date_gmt":"2026-01-31 19:24:45","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-QsRcd7\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Piotr Kica, Sabina Licho\u0142ai, Micha\u0142 Orzechowski, Maciej Malawski<br><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-4mOOA5\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_697e56e4fb8d8\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read\/Download\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/pdf\/2506.12611\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow","post_excerpt":"Conference manuscript: 25th International Conference on Computational Science, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"accelerating-cloud-based-transcriptomics-performance-analysis-and-optimization-of-the-star-aligner-workflow","to_ping":"","pinged":"","post_modified":"2026-01-31 20:24:54","post_modified_gmt":"2026-01-31 19:24:54","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=28615","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":28622,"post_author":"8","post_date":"2026-01-31 20:27:39","post_date_gmt":"2026-01-31 19:27:39","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-2Pi2d5\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">J. Meizner, P. Kica, S. Licho\u0142ai, M. Malawski<br><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-1zPQcu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_697e57c573194\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read\/Download\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/events.plgrid.pl\/event\/70\/attachments\/143\/364\/PROCEEDINGS%202025_na%20www%20bez%20notatek.pdf\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-kY6bTP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><\/p>\n<!-- \/wp:paragraph -->","post_title":"Analysis of Index Access Pattern in STAR RNA-seq Aligner","post_excerpt":"Conference abstract: Seventeenth ACC Cyfronet AGH HPC Users\u2019 Conference (KUKDM 2025), 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"analysis-of-index-access-pattern-in-star-rna-seq-aligner","to_ping":"","pinged":"","post_modified":"2026-02-06 11:42:54","post_modified_gmt":"2026-02-06 10:42:54","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=28622","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":20841,"post_author":"8","post_date":"2025-01-13 09:25:52","post_date_gmt":"2025-01-13 08:25:52","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-RWEf8c\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-karolina-tlalka-nbsp-harry-saxton-ian-halliday-xu-xu-andrew-narracott-daniel-taylor-maciej-malawski\">Karolina Tla\u0142ka , Harry Saxton, Ian Halliday, Xu Xu, Andrew Narracott, Daniel Taylor, Maciej Malawski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-EFq7Vk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-2RHCud\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The baroreflex plays a crucial role in regulating the human cardiovascular system. This study employs a closed-loop in silico model of baroreflex control, integrated with pulsatile mechanical models featuring (i) a single heart chamber with 36 parameters and (ii) four chambers with 51 parameters. For the first time, a global sensitivity analysis is conducted on these closed-loop systems, accounting for both cardiovascular and baroreflex parameters. The results are compared with non-regulated versions of the models. Findings indicate that regulated parameters have a lower impact compared to non-regulated counterparts, and in a physiological resting state, outputs such as pressure, heart rate, and cardiac output are primarily influenced by parasympathetic arc parameters. This research enhances the understanding of regulation effects and parameter influence on clinical metrics, contributing to the development of personalized healthcare models.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-EFq7Vk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-K67hWw\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1371\/journal.pcbi.1012377<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-kzZIS0\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: baroreflex, sensitivity analysis, cardiovascular system<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-EFq7Vk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_6784cd51f8522\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1012377\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Sensitivity analysis of closed-loop one-chamber and four-chamber models with baroreflex","post_excerpt":"Journal paper: journals.plos.org, 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"sensitivity-analysis-of-closed-loop-one-chamber-and-four-chamber-models-with-baroreflex","to_ping":"","pinged":"","post_modified":"2025-01-13 12:27:51","post_modified_gmt":"2025-01-13 11:27:51","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=20841","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14774,"post_author":"5","post_date":"2024-01-05 14:58:04","post_date_gmt":"2024-01-05 13:58:04","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-KqAkIU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Marian Bubak, Maciej Malawski, Renata G. S\u0142ota<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-pteD1F\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65980adb00e39\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.cai.sk\/ojs\/index.php\/cai\/article\/view\/2021_4_729\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Preface to the Special Issue on Software Engineering","post_excerpt":"In: Computing and Informatics, 2021.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"preface-to-the-special-issue-on-software-engineering","to_ping":"","pinged":"","post_modified":"2024-01-05 14:58:16","post_modified_gmt":"2024-01-05 13:58:16","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14774","menu_order":88,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14829,"post_author":"5","post_date":"2024-01-10 20:00:40","post_date_gmt":"2024-01-10 19:00:40","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-uqaeKt\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">K. W\u00f3jcik, A. \u0106miel, <strong>T. Sat\u0142awa, S. Licho\u0142ai, <\/strong>K. Wawrzycka-Adamczyk, G. Biedro\u0144, A. Masiak, Z. Zdrojewski, H. Storoniak, B. Bu\u0142\u0142o-Piontecka, A. D\u0119bska-\u015alizie\u0144, R. Jeleniewicz, M. Majdan, K. Jakuszko, H. Augustyniak-Bartosik, M. Krajewska, I. Brzosko, M. Brzosko, J. Kur-Zalewska, W. T\u0142ustochowicz, M. Madej, A. Hawrot-Kawecka, E. Kucharz, P. G\u0142uszko, M. Wis\u0142owska, J. Mi\u0142kowska-Dymanowska, A. Lewandowska-Polak, J. Makowska, J. Zalewska,<strong> T. Guba\u0142a, M. Malawski<\/strong>, J. Musia\u0142<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-fkALYP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_659ee97b86d3c\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/zenodo.org\/records\/6454077\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Subgrouping and personalized risk evaluation for outcome prediction in ANCA associated vasculitis (AAV)","post_excerpt":"In: Autoimmunity, Autoinflammation and Immunodeficiency in Vasculitis Abstracts of the 20th International Vasculitis & ANCA Workshop, Dublin, Ireland 3-6 April 2022, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"subgrouping-and-personalized-risk-evaluation-for-outcome-prediction-in-anca-associated-vasculitis-aav","to_ping":"","pinged":"","post_modified":"2024-01-10 20:03:07","post_modified_gmt":"2024-01-10 19:03:07","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14829","menu_order":54,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14841,"post_author":"5","post_date":"2024-01-10 20:28:19","post_date_gmt":"2024-01-10 19:28:19","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-N27mw0\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">K. W\u00f3jcik, A. \u0106miel,<strong> T. Sat\u0142awa, S. Licho\u0142ai, <\/strong>K. Wawrzycka-Adamczyk, G. Biedro\u0144, A. Masiak, Z. Zdrojewski, H. Storoniak, B. Bu\u0142\u0142o-Piontecka, A. D\u0119bska-\u015alizie\u0144, R. Jeleniewicz, M. Majdan, K. Jakuszko, H. Augustyniak-Bartosik, M. Krajewska, I. Brzosko, M. Brzosko, J. Kur-Zalewska, W. T\u0142ustochowicz, M. Madej, A. Hawrot-Kawecka, E. Kucharz, P. G\u0142uszko, M. Wis\u0142owska, J. Mi\u0142kowska-Dymanowska, A. Lewandowska-Polak, J. Makowska, J. Zalewska,<strong> T. Guba\u0142a, M. Malawski, <\/strong>J. Musia\u0142<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-eJavZq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_659eefbce7052\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/ard.bmj.com\/content\/81\/Suppl_1\/367.1\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Personalized risk evaluation for outcome prediction in anca associated vasculitis (aav) using latent class analysis and machine learning","post_excerpt":"In: Annals of the Rheumatic Diseases The EULAR Journal \u2013 European Congress of Rheumatology 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"personalized-risk-evaluation-for-outcome-prediction-in-anca-associated-vasculitis-aav-using-latent-class-analysis-and-machine-learning","to_ping":"","pinged":"","post_modified":"2024-01-26 14:19:34","post_modified_gmt":"2024-01-26 13:19:34","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14841","menu_order":53,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14844,"post_author":"5","post_date":"2024-01-10 20:31:01","post_date_gmt":"2024-01-10 19:31:01","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-bsCTNu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">M. Malawski, B.\u00a0Balis<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-evaUrQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-zdhfJu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Serverless computing has become an important model in cloud computing and influenced the design of many applications. Here, we provide our perspective on how the recent landscape of serverless computing for scientific applications looks like. We discuss the advantages and problems with serverless computing for scientific applications, and based on the analysis of existing solutions and approaches, we propose a science-oriented architecture for a serverless computing framework that is based on the existing designs. Finally, we provide an outlook of current trends and future directions.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-evaUrQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_659ef063da8b0\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/abs\/2309.01681\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Serverless Computing for Scientific Applications","post_excerpt":"In: Internet Computing, IEEE, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"serverless-computing-for-scientific-applications","to_ping":"","pinged":"","post_modified":"2024-01-10 20:31:02","post_modified_gmt":"2024-01-10 19:31:02","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14844","menu_order":52,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14927,"post_author":"5","post_date":"2024-01-16 13:02:09","post_date_gmt":"2024-01-16 12:02:09","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-Ds2atf\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Bart\u0142omiej Przybylski, Maciej Pawlik, Pawe\u0142 \u017buk, Bart\u0142omiej \u0141agosz, Maciej Malawski, Krzysztof Rzadca<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-czzMWU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-wM6Nk9\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Modern HPC workload managers and their careful tuning contribute to the high utilization of HPC clusters. However, due to inevitable uncertainty it is impossible to completely avoid node idleness. Although such idle slots are usually too short for any HPC job, they are too long to ignore them. Function-as-a-Service (FaaS) paradigm promisingly fills this gap, and can be a good match, as typical FaaS functions last seconds, not hours. Here we show how to build a FaaS infrastructure on idle nodes in an HPC cluster in such a way that it does not affect the performance of the HPC jobs significantly. We dynamically adapt to a changing set of idle physical machines, by integrating open-source software Slurm and OpenWhisk.<br>We designed and implemented a prototype solution that allowed us to cover up to 90\\% of the idle time slots on a 50k-core cluster that runs production workloads.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-czzMWU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a6702bf2fa4\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/abs\/2211.00717\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Using Unused: Non-Invasive Dynamic FaaS Infrastructure with HPC-Whisk","post_excerpt":"In: International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22), 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"using-unused-non-invasive-dynamic-faas-infrastructure-with-hpc-whisk","to_ping":"","pinged":"","post_modified":"2024-01-16 13:02:09","post_modified_gmt":"2024-01-16 12:02:09","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14927","menu_order":51,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14933,"post_author":"5","post_date":"2024-01-16 13:19:18","post_date_gmt":"2024-01-16 12:19:18","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-1Q726J\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Paulina Adamczyk, Sylwia Marek, Ryszard Precikowski, Maciej Kus, Micha\u0142 Grzeszczyk, Maciej Malawski, Aneta Lisowska<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-CEQTHi\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-eAGB92\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">To monitor patients\u2019 well-being and evaluate the efficacy of digital health intervention, patients are required to regularly respond to standardised surveys. Responding to a large number of questionnaires is effortful and may discourage mHealth app users from engaging with the intervention. Gamification might reduce the burden of self-reporting. However, researchers have adopted various approaches to the personalisation of gamification design: ranking of game elements by the user, Hexad Gamification User Types classification (G) and selection of preferred design mockups (MU) . In this paper we report on a small population study involving 54 healthy participants aged 17 to 60, and investigate if these alternative approaches lead to the same design choices. We find that different evaluation approaches lead to different choices of gamification elements. We suggest to use game element ranking in combination with mockup selection. Hexad player classification might be less useful in the co ntext of mHealth applications design.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-CEQTHi\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a67432cab12\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.scitepress.org\/PublicationsDetail.aspx?ID=86vOosGEiU4=\\u0026t=1\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Designing Personalised Gamification of mHealth Survey Applications","post_excerpt":"In: Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: BIOSTEC, 224-231, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"designing-personalised-gamification-of-mhealth-survey-applications","to_ping":"","pinged":"","post_modified":"2024-02-28 17:49:03","post_modified_gmt":"2024-02-28 16:49:03","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14933","menu_order":21,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14937,"post_author":"5","post_date":"2024-01-16 13:33:06","post_date_gmt":"2024-01-16 12:33:06","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-G1GWMI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Piotr Kica, Magdalena Otta, Krzysztof Czechowicz, Karol Zaj\u0105c, Piotr Nowakowski, Andrew Narracott, Ian Halliday, Maciej Malawski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-vSoDxU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-sGaBCh\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Digital twins are virtual representations of physical objects or systems used for the purpose of analysis, most often via computer simulations, in many engineering and scientific disciplines. Recently, this approach has been introduced to computational medicine, within the concept of Digital Twin in Healthcare (DTH). Such research requires verification and validation of its models, as well as the corresponding sensitivity analysis and uncertainty quantification (VVUQ). From the computing perspective, VVUQ is a computationally intensive process, as it requires numerous runs with variations of input parameters. Researchers often use high-performance computing (HPC) solutions to run VVUQ studies where the number of parameter combinations can easily reach tens of thousands. However, there is a viable alternative to HPC for a substantial subset of computational models - serverless computing. In this paper we hypothesize that using the serverless computing model can be a practical and efficient approach to selected cases of running VVUQ calculations. We show this on the example of the EasyVVUQ library, which we extend by providing support for many serverless services. The resulting library - CloudVVUQ - is evaluated using two real-world applications from the computational medicine domain adapted for serverless execution. Our experiments demonstrate the scalability of the proposed approach.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-vSoDxU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a6776af10b8\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.computer.org\/csdl\/proceedings-article\/ccgrid\/2023\/011900a627\/1OFrjleAbTy\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Serverless Approach to Sensitivity Analysis of Computational Models","post_excerpt":"In: CCGrid2023, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"serverless-approach-to-sensitivity-analysis-of-computational-models","to_ping":"","pinged":"","post_modified":"2024-01-16 13:33:06","post_modified_gmt":"2024-01-16 12:33:06","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14937","menu_order":27,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14952,"post_author":"8","post_date":"2024-01-16 13:45:59","post_date_gmt":"2024-01-16 12:45:59","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-w6ZXkN\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p id=\"h-\" class=\" eplus-wrapper\">Piotr Nowakowski, Marian Bubak, Krzysztof G\u0105dek, \u200bMarek Kasztelnik, Maciej Malawski, Jan Meizner, \u200bAdam Nowak, Piotr Po\u0142e\u0107, Karol Zaj\u0105c, Taras Zhyhulin\u200b<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-zQZ8SH\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a67a733ad67\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.cyfronet.pl\/zalacznik\/9789\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Leveraging ACC Resources for Medical Research","post_excerpt":"In: KUKDM 2023, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"leveraging-acc-resources-for-medical-research","to_ping":"","pinged":"","post_modified":"2026-01-31 19:31:07","post_modified_gmt":"2026-01-31 18:31:07","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14952","menu_order":26,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15019,"post_author":"5","post_date":"2024-01-18 10:05:36","post_date_gmt":"2024-01-18 09:05:36","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-AM6OG4\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Marek Kasztelnik, Piotr Nowakowski, Jan Meizner, Maciej Malawski, Adam Nowak, Krzysztof Gadek, Karol Zajac, Antonino Amedeo La Mattina & Marian Bubak<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-2j4SBu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-nW3zsl\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The Digital Twin paradigm in medical care has recently gained popularity among proponents of translational medicine, to enable clinicians to make informed choices regarding treatment on the basis of digital simulations. In this paper we present an overview of functional and non-functional requirements related to specific IT solutions which enable such simulations - including the need to ensure repeatability and traceability of results - and propose an architecture that satisfies these requirements. We then describe a computational platform that facilitates digital twin simulations, and validate our approach in the context of a real-life medical use case: the BoneStrength application.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-2j4SBu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a8e9d073e43\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-36021-3_2\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Digital twin simulation development and execution on HPC infrastructures","post_excerpt":"In: ICCS 2023: Computational Science \u2013 ICCS 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"digital-twin-simulation-development-and-execution-on-hpc-infrastructures","to_ping":"","pinged":"","post_modified":"2024-01-18 10:05:37","post_modified_gmt":"2024-01-18 09:05:37","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15019","menu_order":23,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12666,"post_author":"5","post_date":"2023-07-13 13:11:29","post_date_gmt":"2023-07-13 11:11:29","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-i2nvwe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>\u015alazyk F, Jab\u0142ecki P, Malawski M, P\u0142otka P., A. Lisowska<\/strong><\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-l8oSS5\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Khjkm0\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Federated learning enables building a shared model from multicentre data while storing the training data locally for privacy. In this paper, we present an evaluation (called CXR-FL) of deep learning-based models for chest X-ray image analysis using the federated learning method. We examine the impact of federated learning parameters on the performance of central models. Additionally, we show that classification models perform worse if trained on a region of interest reduced to segmentation of the lung compared to the full image. However, focusing training of the classification model on the lung area may result in improved pathology interpretability during inference. We also find that federated learning helps maintain model generalizability. The pre-trained weights and code are publicly available at (<a href=\"https:\/\/github.com\/SanoScience\/CXR-FL\">https:\/\/github.com\/SanoScience\/CXR-FL<\/a>).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-l8oSS5\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64c02d6b6c034\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-08754-7_50\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"CXR-FL: Deep Learning-based Chest X-ray Image Analysis Using Federated Learning\u00a0","post_excerpt":"In: 22nd International Conference on Computational Science Lecture Notes in Computer Science, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"cxr-fl-deep-learning-based-chest-x-ray-image-analysis-using-federated-learning-2","to_ping":"","pinged":"","post_modified":"2024-01-09 18:24:36","post_modified_gmt":"2024-01-09 17:24:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12666","menu_order":55,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12620,"post_author":"5","post_date":"2023-07-13 11:43:50","post_date_gmt":"2023-07-13 09:43:50","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-8UEGno\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Malawski, Maciej; Barty\u0144ski, Tomasz; Bubak, Marian<\/h2>\n<!-- \/wp:heading -->","post_title":"Invocation of operations from script-based Grid applications\u00a0","post_excerpt":"In: Future Gener. Comput. Syst., vol. 26, no. 1, pp. 138\u2013146, 2010, ISSN: 0167-739X.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"invocation-of-operations-from-script-based-grid-applications","to_ping":"","pinged":"","post_modified":"2024-01-05 13:56:54","post_modified_gmt":"2024-01-05 12:56:54","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12620","menu_order":99,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12617,"post_author":"5","post_date":"2023-07-13 11:42:45","post_date_gmt":"2023-07-13 09:42:45","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-2KwsTM\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Malawski, Maciej; Figiela, Kamil; Nabrzyski, Jarek<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-huLjRS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64afc6fd4765d\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167739X13000186?via%3Dihub\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Cost minimization for computational applications on hybrid cloud infrastructures\u00a0","post_excerpt":"In: Future Generation Comp. Syst., vol. 29, no. 7, pp. 1786\u20131794, 2013.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"cost-minimization-for-computational-applications-on-hybrid-cloud-infrastructures","to_ping":"","pinged":"","post_modified":"2024-01-05 13:57:17","post_modified_gmt":"2024-01-05 12:57:17","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12617","menu_order":98,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12585,"post_author":"5","post_date":"2023-07-12 15:44:17","post_date_gmt":"2023-07-12 13:44:17","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-OmJe40\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Malawski, Maciej; Juve, Gideon; Deelman, Ewa; Nabrzyski, Jarek<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-ca6pce\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-TPKkWw\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure as a Service (IaaS) clouds. IaaS clouds are characterized by on-demand resource provisioning capabilities and a pay-per-use model. We discuss, develop, and assess novel algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account task granularity, uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. Our results show that an admission procedure based on workflow structure and estimates of task runtimes can significantly improve the quality of solutions.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-syXWPn\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64aead49ae98c\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167739X15000059?via%3Dihub\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds\u00a0","post_excerpt":"In: Future Generation Computer Systems, vol. 48, pp. 1\u201318, 2015, ISSN: 0167739X.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"algorithms-for-cost-and-deadline-constrained-provisioning-for-scientific-workflow-ensembles-in-iaas-clouds","to_ping":"","pinged":"","post_modified":"2024-01-05 13:57:38","post_modified_gmt":"2024-01-05 12:57:38","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12585","menu_order":97,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12582,"post_author":"5","post_date":"2023-07-12 15:39:45","post_date_gmt":"2023-07-12 13:39:45","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-J5dqk1\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Nowakowski, Piotr; Bubak, Marian; Barty\u0144ski, Tomasz; Guba\u0142a, Tomasz; Harce\u017clak, Daniel; Kasztelnik, Marek; Malawski, Maciej; Meizner, Jan<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-s1TArR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-a3R9xa\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">As virtualization technologies mature and become ever more widespread, cloud computing has emerged as a promising paradigm for e-science. In order to facilitate successful application of cloud computing in scientific research \u2013 particularly in a domain as security-minded as medical research \u2013 several technical challenges need to be addressed. This paper reports on the successful deployment and utilization of a cloud computing platform for the Virtual Physiological Human (VPH) research community, originating in the VPH-Share project and continuing beyond the end of this project. The platform tackles technical issues involved in porting existing desktop applications to the cloud environment and constitutes a uniform research space where application services can be developed, stored, accessed and shared using a variety of computational infrastructures. The paper also presents examples of application workflows which make use of the presented infrastructure \u2013 both internal and external to the VPH community.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-12Pdvl\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64aeacf54484e\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/dx.doi.org\/10.1016\/j.jocs.2017.06.012\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Cloud computing infrastructure for the VPH community\u00a0","post_excerpt":"In: Journal of Computational Science, vol. 24, pp. 169\u2013179, 2018, ISSN: 18777503.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"cloud-computing-infrastructure-for-the-vph-community","to_ping":"","pinged":"","post_modified":"2024-01-05 13:58:08","post_modified_gmt":"2024-01-05 12:58:08","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12582","menu_order":96,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12580,"post_author":"5","post_date":"2023-07-12 15:38:06","post_date_gmt":"2023-07-12 13:38:06","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-oma9Pp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Avati, Valentina; Blaszkiewicz, Milosz; Bocchi, Enrico; Canali, Luca; Castro, Diogo; Cervantes, Javier; Grzanka, Leszek; Guiraud, Enrico; Kaspar, Jan; Kothuri, Prasanth; Lamanna, Massimo; Malawski, Maciej; Mnich, Aleksandra; Moscicki, Jakub; Murali, Shravan; Piparo, Danilo; Tejedor, Enric<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-ItVSrY\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-ctAHD5\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The High-Energy Physics community faces new data processing challenges caused by the expected growth of data resulting from the upgrade of LHC accelerator. These challenges drive the demand for exploring new approaches for data analysis. In this paper, we present a new declarative programming model extending the popular ROOT data analysis framework, and its distributed processing capability based on Apache Spark. The developed framework enables high-level operations on the data, known from other big data toolkits, while preserving compatibility with existing HEP data files and software. In our experiments with a real analysis of TOTEM experiment data, we evaluate the scalability of this approach and its prospects for interactive processing of such large data sets. Moreover, we show that the analysis code developed with the new model is portable between a production cluster at CERN and an external cluster hosted in the Helix Nebula Science Cloud thanks to the bundle of services of Science Box.<\/p>\n<!-- \/wp:paragraph -->","post_title":"Declarative Big Data Analysis for High-Energy Physics: TOTEM Use Case\u00a0","post_excerpt":"In: Yahyapour, Ramin (Ed.): Euro-Par 2019: Parallel Processing, pp. 241\u2013255, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-29400-7.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"declarative-big-data-analysis-for-high-energy-physics-totem-use-case","to_ping":"","pinged":"","post_modified":"2024-01-05 13:58:30","post_modified_gmt":"2024-01-05 12:58:30","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12580","menu_order":95,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12578,"post_author":"5","post_date":"2023-07-12 15:37:11","post_date_gmt":"2023-07-12 13:37:11","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-wZ1MIW\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Tomasiewicz, Dawid; Pawlik, Maciej; Malawski, Maciej; Rycerz, Katarzyna<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-kkKdlz\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-pvfvZr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Many scientific processes and applications can be represented in the standardized form of workflows. One of the key challenges related to managing and executing workflows is scheduling. As an NP-hard problem with exponential complexity it imposes limitations on the size of practically solvable problems. In this paper, we present a solution to the challenge of scheduling workflow applications with the help of the D-Wave quantum annealer. To the best of our knowledge, there is no other work directly addressing workflow scheduling using quantum computing. Our solution includes transformation into a Quadratic Unconstrained Binary Optimization (QUBO) problem and discussion of experimental results, as well as possible applications of the solution. For our experiments we choose four problem instances small enough to fit into the annealer's architecture. For two of our instances the quantum annealer finds the global optimum for scheduling. We thus show that it is possible to solve such problems with the help of the D-Wave machine and discuss the limitations of this approach.<\/p>\n<!-- \/wp:paragraph -->","post_title":"Foundations for Workflow Application Scheduling on D-Wave System\u00a0","post_excerpt":"In: Computational Science -- ICCS 2020, pp. 516\u2013530, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-50433-5.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"foundations-for-workflow-application-scheduling-on-d-wave-system","to_ping":"","pinged":"","post_modified":"2024-03-11 11:52:23","post_modified_gmt":"2024-03-11 10:52:23","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12578","menu_order":92,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12575,"post_author":"5","post_date":"2023-07-12 15:36:06","post_date_gmt":"2023-07-12 13:36:06","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-FfpoBR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Malawski, Maciej; Rzadca, Krzysztof (Ed.)<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-aPkrED\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64aeac136f852\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/book\/10.1007\/978-3-030-57675-2\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Euro-Par 2020: Parallel Processing\u00a0","post_excerpt":"In: Springer International Publishing, Cham, 2020, ISBN: 978-3-030-57674-5.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"euro-par-2020-parallel-processing","to_ping":"","pinged":"","post_modified":"2024-01-05 13:59:05","post_modified_gmt":"2024-01-05 12:59:05","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12575","menu_order":93,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12571,"post_author":"5","post_date":"2023-07-12 15:23:30","post_date_gmt":"2023-07-12 13:23:30","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-rBLpf0\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Malawski, Maciej; Gajek, Adam; Zima, Adam; Balis, Bartosz; Figiela, Kamil<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-2jtdDr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-AIUAip\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Scientific workflows consisting of a high number of interdependent tasks represent an important class of complex scientific applications. Recently, a new type of serverless infrastructures has emerged, represented by such services as Google Cloud Functions and AWS Lambda, also referred to as the Function-as-a-Service model. In this paper we take a look at such serverless infrastructures, which are designed mainly for processing background tasks of Web and Internet of Things applications, or event-driven stream processing. We evaluate their applicability to more compute- and data-intensive scientific workflows and discuss possible ways to repurpose serverless architectures for execution of scientific workflows. We have developed prototype workflow executor functions using AWS Lambda and Google Cloud Functions, coupled with the HyperFlow workflow engine. These functions can run workflow tasks in AWS and Google infrastructures, and feature such capabilities as data staging to\/from S3 or Google Cloud Storage and execution of custom application binaries. We have successfully deployed and executed the Montage astronomy workflow, often used as a benchmark, and we report on initial results of its performance evaluation. Our findings indicate that the simple mode of operation makes this approach easy to use, although there are costs involved in preparing portable application binaries for execution in a remote environment.While our solution is an early prototype, we find the presented approach highly promising. We also discuss possible future steps related to execution of scientific workflows in serverless infrastructures. Finally, we perform a cost analysis and discuss implications with regard to resource management for scientific applications in general.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-fcLpYe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64aea9358a913\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167739X1730047X?via%3Dihub\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud Functions\u00a0","post_excerpt":"In: Future Generation Computer Systems, vol. 110, pp. 502\u2013514, 2020, ISSN: 0167739X.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"serverless-execution-of-scientific-workflows-experiments-with-hyperflow-aws-lambda-and-google-cloud-functions","to_ping":"","pinged":"","post_modified":"2024-01-05 13:59:40","post_modified_gmt":"2024-01-05 12:59:40","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12571","menu_order":94,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12568,"post_author":"5","post_date":"2023-07-12 14:05:16","post_date_gmt":"2023-07-12 12:05:16","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-KUhwAl\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Bubak, M; Czechowicz, K; Guba\u0142a, T; Hose, D R; Kasztelnik, M; Malawski, M; Meizner, J; Nowakowski, P; Wood, S<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-EBnA15\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-N4wtZJ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The goal of this paper is to present a dedicated high-performance computing (HPC) infrastructure which is used in the development of a so-called reduced-order model (ROM) for simulating the outcomes of interventional procedures which are contemplated in the treatment of valvular heart conditions. Following a brief introduction to the problem, the paper presents the design of a model execution environment, in which representative cases can be simulated and the parameters of the ROM fine-tuned to enable subsequent deployment of a decision support system without further need for HPC. The presentation of the system is followed by information concerning its use in processing specific patient cases in the context of the EurValve international collaboration.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-I1PAzT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64ae96e2d28c3\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/royalsocietypublishing.org\/doi\/10.1098\/rsfs.2020.0006\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"The EurValve model execution environment\u00a0","post_excerpt":"In: Interface Focus, vol. 11, no. 1, pp. 20200006, 2021, ISSN: 2042-8898.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"the-eurvalve-model-execution-environment","to_ping":"","pinged":"","post_modified":"2024-01-05 14:00:04","post_modified_gmt":"2024-01-05 13:00:04","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12568","menu_order":79,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12565,"post_author":"5","post_date":"2023-07-12 14:03:52","post_date_gmt":"2023-07-12 12:03:52","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-3oLO4N\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">P., Jab\u0142ecki; F., \u015alazyk; M., Malawski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-PpCvCQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-ssSW11\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Federated Learning (FL) is a novel technique that allows for performing the training of a global model without 0sharing data between entities. This research focused on the analysis of existing solutions for Federated Learning in the context of medical image classification. Selected frameworks: TensorFlow Federated, PySyft and Flower were tested and their usability was assessed. Additionally, experiments on classification of X-ray lung images with the use of the Flower framework were performed in a fully distributed setting using Google Cloud Platform.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-xTjsPo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64ae9679aacda\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-90874-4_11\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Federated Learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks\u00a0","post_excerpt":"In: MICCAI Conference, 2021.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"federated-learning-in-the-cloud-for-analysis-of-medical-images-experience-with-open-source-frameworks","to_ping":"","pinged":"","post_modified":"2024-01-05 14:00:24","post_modified_gmt":"2024-01-05 13:00:24","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12565","menu_order":80,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12562,"post_author":"5","post_date":"2023-07-12 14:01:38","post_date_gmt":"2023-07-12 12:01:38","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-SZyUXA\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Dajda, Jacek; Idzik, Micha\u0142; Sroka, Jakub; Paw\u0142owski, Miko\u0142aj Sikora Wiktor; lka, Maciej Smo; Jab\u0142ecki, Przemys\u0142aw; \u015alazyk, Filip; Malawski, Maciej; Majerz, Emilia; Pasternak, Aleksandra; Dzwinel, Witold<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-qeRz73\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-OUqT1r\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This article presents short analysis and observations on current trends and directions in conducting engineering theses in the field of computer science. This report is based on collected bachelor theses in AGH Computer Science Department for academic year 2020\/2021 as well as the conducted competition for the best engineering theses held during XXII KKIO 2021 Software Engineering Conference. The awarded works are briefly presented as an illustration to the drawn conclusions.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-As67Qq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64ae9610f67b6\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.cai.sk\/ojs\/index.php\/cai\/article\/view\/2021_4_930\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Current Trends in Software Engineering Bachelor Theses\u00a0","post_excerpt":"In: Computing and Informatics, 2021.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"current-trends-in-software-engineering-bachelor-theses","to_ping":"","pinged":"","post_modified":"2024-01-05 14:00:47","post_modified_gmt":"2024-01-05 13:00:47","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12562","menu_order":81,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":18615,"post_author":"8","post_date":"2024-08-13 15:46:49","post_date_gmt":"2024-08-13 13:46:49","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-dxa5g6\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-karolina-tlalka-harry-saxton-ian-halliday-xu-xu-daniel-taylor-andrew-narracott-maciej-malawski\">Karolina Tla\u0142ka, Harry Saxton, Ian Halliday, Xu Xu, Daniel Taylor, Andrew Narracott, Maciej Malawski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-iuxabp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-N12jnw\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">In this study, the authors performed a local sensitivity analysis, a computationally inexpensive method of identifying influential parameters, supplemented with orthogonality analysis. A zero-dimensional closed-loop model with a one-chamber heart was analyzed in two versions - regulated in a rest state (with the representation of baroreflex proposed by Ursino in doi: 10.1152\/ajpheart.1998.275.5.H1733, but without any disturbance) and unregulated. Comparing results for the models, it is clear that the influence of baroreflex parameters is visibly smaller than cardiovascular (regulation set-point is the exception). Vagal activity dominates. The conclusion is that local sensitivity analysis may be successfully used to increase knowledge about the model and to validate it, but one must be aware of its limitations.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-iuxabp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-s6PQB6\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Karolina Tla\u0142ka, Harry Saxton, Ian Halliday, Xu Xu, Daniel Taylor, Andrew Narracott, Maciej Malawski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-aKuwMF\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-63772-8_17\">doi.org\/10.1007\/978-3-031-63772-8_17<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-404zct\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: model development, sensitivity analysis, baroreflex<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-iuxabp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_6780e8fbc5551\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-63772-8_17\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Local Sensitivity Analysis of\u00a0a\u00a0Closed-Loop\u00a0in Silico\u00a0Model of\u00a0the\u00a0Human Baroregulation","post_excerpt":"conference manuscript: Springer, 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"local-sensitivity-analysis-of-a-closed-loop-in-silico-model-of-the-human-baroregulation","to_ping":"","pinged":"","post_modified":"2025-02-05 11:51:39","post_modified_gmt":"2025-02-05 10:51:39","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=18615","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":28581,"post_author":"8","post_date":"2026-01-31 20:09:05","post_date_gmt":"2026-01-31 19:09:05","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-OIukPr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><br>Jonathan Bader, Jim Belak, Matthew Bement, Matthew Berry, Robert Carson, Daniela Cassol, Stephen Chan, John Coleman, Kastan Day, Alejandro Duque, Kjiersten Fagnan, Jeff Froula, Shantenu Jha, Daniel S. Katz, Piotr Kica, Volodymyr Kindratenko, Edward Kirton, Ramani Kothadia, Daniel Laney, Fabian Lehmann, Ulf Leser, Sabina Licho\u0142ai, Maciej Malawski, Mario Melara, Elais Player, Matt Rolchigo, Setareh Sarrafan, Seung-Jin Sul, Abdullah Syed, Lauritz Thamsen, Mikhail Titov, Matteo Turilli, Silvina Caino-Lores, and Anirban Mandal<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-vEGtJv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-TIIdxt\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Conference: Proceedings of the SC '23 Workshops<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-vEGtJv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_697e53803e5f8\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read\/Download\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/hal.science\/hal-04385285\/file\/WORKS23_Abstract_Papers.pdf\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments","post_excerpt":"Conference: Proceedings of the SC '23 Workshops, 2023","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"novel-approaches-toward-scalable-composable-workflows-in-hyper-heterogeneous-computing-environments","to_ping":"","pinged":"","post_modified":"2026-01-31 20:10:12","post_modified_gmt":"2026-01-31 19:10:12","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=28581","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":28592,"post_author":"8","post_date":"2026-01-31 20:14:34","post_date_gmt":"2026-01-31 19:14:34","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-0300LX\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Piotr Kica, Sabina Licho\u0142ai, Micha\u0142 Orzechowski, Maciej Malawski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-cotKU2\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Conference:\u00a0IEEE International Conference on Cluster Computing Workshops<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-y0RYas\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_697e545904169\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read\/Download\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/pdf\/2409.05886\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Optimizing STAR Aligner for High Throughput Computing in the Cloud","post_excerpt":"Conference manuscript: IEEE International Conference on Cluster Computing Workshops, 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"optimizing-star-aligner-for-high-throughput-computing-in-the-cloud","to_ping":"","pinged":"","post_modified":"2026-01-31 20:15:39","post_modified_gmt":"2026-01-31 19:15:39","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=28592","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":28602,"post_author":"8","post_date":"2026-01-31 20:17:46","post_date_gmt":"2026-01-31 19:17:46","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-msySbD\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Piotr Kica, Micha\u0142 Orzechowski, Maciej Malawski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-5oCaBe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Aligning RNA sequences to a reference genome is essential for analyzing gene expression, detecting genetic variations, and conducting transcriptomic research. Among the most efficient and accurate tools for this purpose is the STAR aligner , a computationally demanding yet widely adopted solution in modern transcriptomics.<br>Serverless computing models such as Function-as-a-Service (FaaS) and Container-as-a-Service (CaaS) have emerged as flexible frameworks for executing diverse workloads, including batch and event-driven tasks . These architectures offer key advantages over conventional infrastructure\u2014mainly high scalability, minimal maintenance, and cost efficiency through pay-per-use billing. Nonetheless, each service type comes with its own constraints, which may influence their suitability for running STAR-based RNA sequence processing.<br>Previous studies have demonstrated the feasibility of executing lightweight aligners, such as HiSat2, within serverless environments . By partitioning input FASTQ files into smaller chunks, researchers effectively parallelized computations, achieving notable performance improvements. Despite this progress, STAR remains superior in both alignment accuracy and potential processing speed .<br>Deploying STAR efficiently in a serverless setup poses several challenges, primarily due to its reliance on a large prebuilt genome index that must reside in memory during execution. For the human genome, this index typically occupies around 30\u202fGB of space\u2014surpassing memory limits imposed by many cloud services.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-5oCaBe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><br><strong>The objectives of this study include<\/strong>:<br>\u2022 Identifying serverless platforms capable of supporting STAR alignment tasks,<br>\u2022 Conducting performance tests of the STAR aligner in a serverless environment,<br>\u2022 Comparing cost-efficiency between serverless and conventional computing models,<br>\u2022 Exploring optimization strategies for resource management,<br>\u2022 Proposing practical scenarios for deploying STAR in serverless settings.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-ST7ouf\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_697e559d04556\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read\/Download\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/pdf\/2504.05078\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Serverless Approach to Running Resource-Intensive STAR Aligner","post_excerpt":"Conference manuscript: CCGrid2025, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"serverless-approach-to-running-resource-intensive-star-aligner","to_ping":"","pinged":"","post_modified":"2026-01-31 20:21:16","post_modified_gmt":"2026-01-31 19:21:16","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=28602","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}],"position_with_team":{"text_before_link":"Director of Sano, Research Team Leader of","link_text":"Extreme-scale Data and Computing,","text_after_link":"Associate Professor at AGH","link":"https:\/\/sano.science\/research-teams\/extreme-scale-data-and-computing\/"}},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/531","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":38,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/531\/revisions"}],"predecessor-version":[{"id":28627,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/531\/revisions\/28627"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/16989"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=531"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}