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DTSTART;TZID=America/Chicago:20251117T161500
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UID:submissions.supercomputing.org_SC25_sess231_misc146@linklings.com
SUMMARY:A first look at Mojo’s MLIR-based Performance Portable GPU Program
 ming for Python Users
DESCRIPTION:William Godoy (Oak Ridge National Laboratory)\n\nMojo is a nov
 el programming language to be open-sourced by 2026 that closes performance
  gaps in the Python ecosystem. We present an initial look of its GPU perfo
 rmance portable capabilities - since June 2025 - for four science workload
 s: the memory-bound Babelstream and Seven-point stencil, the compute-bound
  miniBUDE and Hartree-Fock (including atomic operations). Results indicate
  that memory-bound kernels are on par, while gaps exist on compute-bound k
 ernels when compared to NVIDIA’s CUDA on H100 and AMD’s HIP on MI300A GPUs
 , respectively. Thus, Mojo proposes unifying AI workflows by combining Pyt
 hon interoperability at run-time with MLIR-compiled performant portable co
 de.\n\nRecording: Livestreamed, Recorded\n\nRegistration Category: Technic
 al Program Reg Pass, Workshop Reg Pass\n\nSession Chairs: Pete Mendygral (
 Hewlett Packard Enterprise (HPE)); Sunita Chandrasekaran (University of De
 laware); Davin Potts (Appliomics, LLC); Sam Foreman (Argonne National Labo
 ratory (ANL)); and Daniel Margala (Lawrence Berkeley National Laboratory (
 LBNL))\n\n
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