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LOCATION:Bldg. 6 - 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:Access the recording\n\nOrganizer(s): Gagandeep Singh, Gina Si
 taraman, Kristof Denolf, Mittul Singh, Yijie Xu (AMD), and Bertil Schmidt 
 (Johannes Gutenberg University Mainz)\n\nAdvances in genomics, proteomics,
  and molecular modeling have made computational biology one of the most da
 ta- and compute-intensive fields of modern science. New discoveries in lif
 e science increasingly rely on a combination of AI-driven analysis, large-
 scale numerical simulation, and heterogeneous high-performance computing (
 HPC) systems to transform massive 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 sa
 tisfy dominant AI workloads, while many traditional life science applicati
 ons, such as molecular dynamics, biomolecular simulation, and population g
 enetics, continue to require high numerical precision, stability, and rigo
 rous validation. Reconciling this growing dichotomy is one of the most urg
 ent challenges facing HPC today. This minisymposium explores how algorithm
 -software-hardware co‑design can build reliability and transparency direct
 ly into accelerated biological computing. The session brings together four
  prominent speakers from academia, national labs, and leading HPC centers,
  representing diverse perspectives and covering topics ranging from large-
 scale pangenomics to molecular dynamics and multi-omics. Together, these t
 alks illustrate how co-design approaches are being applied in practice to 
 reconcile AI acceleration with high-precision scientific computing, and ho
 w these insights are shaping the future of high-performance computing beyo
 nd the life sciences.\n\nCo-Design and Heterogeneous Acceleration of Molec
 ular Dynamics Simulation in GROMACS Using HIP\n\nMolecular dynamics (MD) i
 s a cornerstone of computational biology and an increasingly demanding wor
 kload for heterogeneous HPC systems. In this talk, I describe how GROMACS 
 is being co-designed with modern accelerator architectures to deliver scal
 able, high-performance MD across diverse GPU platform...\n\n\nErik Lindahl
  (KTH Royal Institute of Technology; National Academic Infrastructure of S
 upercomputing in Sweden, Linköping University)\n---------------------\nPan
 genome Alignment at Scale: HPC Challenges and Acceleration Strategies\n\nP
 angenome graph representations are increasingly replacing single linear re
 ferences, fundamentally changing how genomic analyses are performed at the
  population scale. However, this transition introduces significant computa
 tional challenges, particularly in sequence alignment against graph-based 
 ref...\n\n\nSantiago Marco-Sola (Barcelona Supercomputing Center)\n-------
 --------------\nThere’s Plenty of Room in the Data: Rethinking Genomic Fil
 e Formats for the AI Decade\n\nGenomic data formats such as FASTA, FASTQ, 
 BAM, and VCF were designed for early sequencing technologies with low thro
 ughput and short reads. Today, genomics is entering an AI-driven decade, w
 here large-scale machine learning models increasingly consume raw and proc
 essed data directly. This talk revi...\n\n\nMohammed Alser (Georgia State 
 University)\n---------------------\nOpen-Source GPU Computing Methods for 
 Accelerated Nanopore Sequencing Data Analysis\n\nAcross life sciences, DNA
  and RNA sequencing have become essential, enabling progress in areas such
  as precision medicine, agriculture, biosecurity and forensics. Among the 
 latest innovations, third-generation Nanopore sequencing stands out for it
 s ability to produce ultra-long reads and detect epig...\n\n\nHasindu Gama
 arachchi (UNSW Sydney, Garvan Institute of Medical Research)\n\nDomain: En
 gineering, Life Sciences, Computational Methods and Applied Mathematics\n\
 nSession Chair: Gagandeep Singh (AMD)
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