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UID:pretalx-eu-summit-2026-ZBRZ7X@cfp.riscv-europe.org
DTSTART;TZID=CET:20260610T161000
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DESCRIPTION:Many computational problems require the processing of large spa
 rse matrices\, where the vast majority of entries are zero. The irregular 
 distribution of the non-zero elements in these matrices stresses the memor
 y system resulting in performance being bottlenecked by the memory bandwid
 th. On parallel architectures\, workload imbalances also limit performance
 . Graphics Processing Units (GPUs) runnning sparse matrix kernels using st
 ate-of-the-art Basic Linear Algebra Subsystem (BLAS) libraries are central
  in modern HPC systems. Although RISC-V application processors are gaining
  in performance\, RISC-V based GPUs are in an early stage of development. 
 We benchmark sparse kernels both on modern HPC-grade GPUs and on Vortex\, 
 a RISC-V GPU that is gaining adoption. We analyse their performance under 
 memory-bound workloads and report the gaps in software and hardware requir
 ed to enable efficient sparse BLAS processing on RISC-V GPUs.
DTSTAMP:20260522T162343Z
LOCATION:Poster Island B
SUMMARY:Benchmarking the Vortex RISC-V GPU for Sparse Workloads - Jules Dub
 ois
URL:https://cfp.riscv-europe.org/eu-summit-2026/talk/ZBRZ7X/
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