BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260605T154543Z
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:Organizer(s): Gagandeep Singh, Gina Sitaraman, Kristof Denolf,
  Mittul Singh, Yijie Xu (AMD), and Bertil Schmidt (Johannes Gutenberg Univ
 ersity Mainz)\n\nAdvances in genomics, proteomics, and molecular modeling 
 have made computational biology one of the most data- and compute-intensiv
 e fields of modern science. New discoveries in life science increasingly r
 ely on a combination of AI-driven analysis, large-scale numerical simulati
 on, and heterogeneous high-performance computing (HPC) systems to transfor
 m massive datasets into biological insight. At the same time, the HPC ecos
 ystem is undergoing a fundamental shift: hardware accelerators are increas
 ingly optimized for low-precision arithmetic to satisfy dominant AI worklo
 ads, while many traditional life science applications, such as molecular d
 ynamics, biomolecular simulation, and population genetics, continue to req
 uire high numerical precision, stability, and rigorous validation. Reconci
 ling this growing dichotomy is one of the most urgent challenges facing HP
 C today. This minisymposium explores how algorithm-software-hardware co‑de
 sign can build reliability and transparency directly into accelerated biol
 ogical computing. The session brings together four prominent speakers from
  academia, national labs, and leading HPC centers, representing diverse pe
 rspectives and covering topics ranging from large-scale pangenomics to mol
 ecular dynamics and multi-omics. Together, these talks illustrate how co-d
 esign approaches are being applied in practice to reconcile AI acceleratio
 n with high-precision scientific computing, and how these insights are sha
 ping the future of high-performance computing beyond the life sciences.\n\
 nPangenome Alignment at Scale: HPC Challenges and Acceleration Strategies\
 n\nPangenome graph representations are increasingly replacing single linea
 r references, fundamentally changing how genomic analyses are performed at
  the population scale. However, this transition introduces significant com
 putational challenges, particularly in sequence alignment against graph-ba
 sed ref...\n\n\nSantiago Marco-Sola (Barcelona Supercomputing Center)\n---
 ------------------\nCo-Design and Heterogeneous Acceleration of Molecular 
 Dynamics Simulation in GROMACS Using HIP\n\nMolecular dynamics (MD) is a c
 ornerstone of computational biology and an increasingly demanding workload
  for heterogeneous HPC systems. In this talk, I describe how GROMACS is be
 ing co-designed with modern accelerator architectures to deliver scalable,
  high-performance MD across diverse GPU platform...\n\n\nErik Lindahl (KTH
  Royal Institute of Technology; National Academic Infrastructure of Superc
 omputing in Sweden, Linköping University)\n---------------------\nOpen-Sou
 rce GPU Computing Methods for Accelerated Nanopore Sequencing Data Analysi
 s\n\nAcross life sciences, DNA and RNA sequencing have become essential, e
 nabling progress in areas such as precision medicine, agriculture, biosecu
 rity and forensics. Among the latest innovations, third-generation Nanopor
 e sequencing stands out for its ability to produce ultra-long reads and de
 tect epig...\n\n\nHasindu Gamaarachchi (UNSW Sydney, Garvan Institute of M
 edical Research)\n---------------------\nThere’s Plenty of Room in the Dat
 a: Rethinking Genomic File Formats for the AI Decade\n\nGenomic data forma
 ts such as FASTA, FASTQ, BAM, and VCF were designed for early sequencing t
 echnologies with low throughput and short reads. Today, genomics is enteri
 ng an AI-driven decade, where large-scale machine learning models increasi
 ngly consume raw and processed data directly. This talk revi...\n\n\nMoham
 med Alser (Georgia State University)\n\nDomain: Engineering, Life Sciences
 , Computational Methods and Applied Mathematics\n\nSession Chair: Gagandee
 p Singh (AMD)
END:VEVENT
END:VCALENDAR