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PRODID:-//University of Iceland//AI Centre//EN
CALSCALE:GREGORIAN
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BEGIN:VEVENT
UID:666ec97f-0641-4720-ab05-0f3df7874c1d@ai.hi.is
DTSTAMP:20260923T062217Z
DTSTART:20260428T090000Z
DTEND:20260428T100000Z
SUMMARY:RNA-seq analysis in seconds using GPUs
DESCRIPTION:We present a GPU implementation of kallisto for RNA-seq transcr
 ipt quantification. By redesigning the core algorithms: pseudoalignment\, 
 equivalence class intersection\, and the EM algorithm\; for massively para
 llel execution on GPUs\, we achieve a 30– 50× speedup over multithreade
 d CPU kallisto. On a benchmark of 100 Geuvadis samples from Human cell lin
 es the GPU version processes paired-end reads at a rate of 3.6 million per
  second\, completing a typical sample in seconds rather than minutes. For 
 a large dataset of 295 million reads\, run- time drops from 40 minutes to 
 50 seconds. Our implementation demonstrates that careful algorithmic redes
 ign\, rather than naïve porting of software\, is necessary to fully explo
 it the computing power of GPUs in sequence analysis.\n\nhttps://ai.hi.is/i
 s/events/colloquium-in-statistics-and-ai-20/
LOCATION:Íslensk erfðagreining\, Tjarnarsalur
URL:https://ai.hi.is/is/events/colloquium-in-statistics-and-ai-20/
STATUS:CONFIRMED
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