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DTSTAMP:20250822T115807Z
LOCATION:Room 6.0D13
DTSTART;TZID=Europe/Stockholm:20250616T160000
DTEND;TZID=Europe/Stockholm:20250616T163000
UID:submissions.pasc-conference.org_PASC25_sess136_msa104@linklings.com
SUMMARY:Accelerating Protein Homology Search for AlphaFold on GPUs
DESCRIPTION:Bertil Schmidt (Johannes Gutenberg University Mainz)\n\nThe en
 ormous data growth continuously shifts the life sciences from model-driven
  towards data-driven science. The need for efficient processing has led to
  the adoption of massively parallel accelerators such as GPUs. As a conseq
 uence, genomics and proteomics method development nowadays often heavily d
 epends on the effective use of these powerful technologies.  Furthermore, 
 progress in both computational techniques and architectures continues to b
 e highly dynamic including novel deep neural network models  and AI accele
 rators. For example, contemporary groundbreaking AI-tools like AlphaFold c
 an generate highly accurate 3D protein structure predictions. In this talk
 , I present two novel tools for accelerating large-scale protein homology 
 search on modern GPU systems: CUDASW++4.0 and MMseqs2-GPU, which advance t
 he state-of-the-art in this area. For example, MMSeqs2-GPU can be used to 
 significantly  accelerate the computation of multiple sequence alignments 
 in the ColabFold server for protein structure prediction, which is one of 
 the most frequently used bioinformatics tools worldwide.\n\nDomain: Engine
 ering, Life Sciences\n\nSession Chairs: Bertil Schmidt (Johannes Gutenberg
  University Mainz), Gagandeep Singh (AMD), and Sriranjani Sitaraman (AMD)\
 n\n
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