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DTSTAMP:20260421T090512Z
LOCATION:Bldg. 6 - Room 103
DTSTART;TZID=Europe/Stockholm:20260629T160000
DTEND;TZID=Europe/Stockholm:20260629T180000
UID:submissions.pasc-conference.org_PASC26_sess158@linklings.com
SUMMARY:MS2E - AI and Hardware Acceleration for Computational Biology: Co-
 Designing Trustworthy and Scalable Life Science Computing
DESCRIPTION:Advances in genomics, proteomics, and molecular modeling have 
 made computational biology one of the most data- and compute-intensive fie
 lds of modern science. New discoveries in life science increasingly rely o
 n a combination of AI-driven analysis, large-scale numerical simulation, a
 nd heterogeneous high-performance computing (HPC) systems to transform mas
 sive datasets into biological insight. At the same time, the HPC ecosystem
  is undergoing a fundamental shift: hardware accelerators are increasingly
  optimized for low-precision arithmetic to satisfy dominant AI workloads, 
 while many traditional life science applications, such as molecular dynami
 cs, biomolecular simulation, and population genetics, continue to require 
 high numerical precision, stability, and rigorous validation. Reconciling 
 this growing dichotomy is one of the most urgent challenges facing HPC tod
 ay. This minisymposium explores how algorithm-software-hardware co‑design 
 can build reliability and transparency directly into accelerated biologica
 l computing. The session brings together four prominent speakers from acad
 emia, national labs, and leading HPC centers, representing diverse perspec
 tives and covering topics ranging from large-scale pangenomics to molecula
 r dynamics and multi-omics. Together, these talks illustrate how co-design
  approaches are being applied in practice to reconcile AI acceleration wit
 h high-precision scientific computing, and how these insights are shaping 
 the future of high-performance computing beyond the life sciences.\n\nPang
 enome Alignment at Scale: HPC Challenges and Acceleration Strategies\n\nPa
 ngenome graph representations are increasingly replacing single linear ref
 erences, fundamentally changing how genomic analyses are performed at the 
 population scale. However, this transition introduces significant computat
 ional challenges, particularly in sequence alignment against graph-based r
 ef...\n\n\nSantiago Marco-Sola (Barcelona Supercomputing Center)\n--------
 -------------\nOpen-source GPU Computing Methods for Accelerated Nanopore 
 Sequencing Data Analysis\n\nAcross life sciences, DNA and RNA sequencing h
 ave become essential, enabling progress in areas such as precision medicin
 e, agriculture, biosecurity and forensics. Among the latest innovations, t
 hird-generation Nanopore sequencing stands out for its ability to produce 
 ultra-long reads and detect epig...\n\n\nHasindu Gamaarachchi (UNSW Sydney
 , Garvan Institute of Medical Research)\n---------------------\nCo-design 
 and heterogeneous acceleration of molecular dynamics simulation in GROMACS
  using HIP\n\nMolecular dynamics (MD) is a cornerstone of computational bi
 ology and an increasingly demanding workload for heterogeneous HPC systems
 . In this talk, I describe how GROMACS is being co-designed with modern ac
 celerator architectures to deliver scalable, high-performance MD across di
 verse GPU platform...\n\n\nErik Lindahl (KTH Royal Institute of Technology
 ; National Academic Infrastructure of Supercomputing in Sweden, Linköping 
 University)\n---------------------\nThere’s Plenty of Room in the Data: Re
 thinking Genomic File Formats for the AI Decade\n\nGenomic data formats su
 ch as FASTA, FASTQ, BAM, and VCF were designed for early sequencing techno
 logies with low throughput and short reads. Today, genomics is entering an
  AI-driven decade, where large-scale machine learning models increasingly 
 consume raw and processed data directly. This talk revi...\n\n\nMohammed A
 lser (Georgia State University)\n\nDomain: Engineering, Life Sciences, Com
 putational Methods and Applied Mathematics\n\nSession Chairs: Gagandeep Si
 ngh (AMD), Sriranjani Sitaraman (AMD), Bertil Schmidt (Johannes Gutenberg 
 University Mainz), Kristof Denolf (AMD), Mittul Singh (AMD), and Yijie Xu 
 (AMD)
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