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UID:submissions.supercomputing.org_SC25_sess238_ws_h2rc107@linklings.com
SUMMARY:Programmer productivity and performance on AMD's AI Engines: Offlo
 ading Fortran intrinsics via MLIR a case-study
DESCRIPTION:Nick Brown (Edinburgh Parallel Computing Centre (EPCC); Univer
 sity of Edinburgh, Scotland) and Gabriel Rodriguez-Canal (Edinburgh Parall
 el Computing Centre (EPCC))\n\nA major challenge the HPC community faces i
 s how to deliver increased performance demanded by scientific programmers,
  whilst addressing an increased emphasis on sustainable operations. Specia
 lised architectures, such as FPGAs and AMD's AI Engines (AIEs), have demon
 strated significant energy efficiency advantages, however a major challeng
 e is that substantial expertise and investment of time is required to gain
  best performance from this hardware which is a major blocker.\n\nFortran 
 in the lingua franca of scientific computing, and in this paper we explore
  the automatic offloading of Fortran intrinsics to the AIEs in AMD's Ryzen
  AI CPU as a case study, demonstrating how the MLIR compiler ecosystem can
  provide performance and programmer productivity. We describe an approach 
 that lowers the MLIR linear algebra dialect to AMD's AIE dialects, and dem
 onstrate that for suitable workloads the AIEs can provide significant perf
 ormance advantages over the CPU without any code modifications required by
  the programmer.\n\nRecording: Livestreamed, Recorded\n\nRegistration Cate
 gory: Technical Program Reg Pass, Workshop Reg Pass\n\nSession Chairs: Jas
 on Bakos (University of South Carolina); Kenneth O'Brien (Advanced Micro D
 evices, Inc. (AMD)); Christian Plessl (Paderborn University, Germany); Tor
 sten Hoefler (ETH Zürich, Microsoft Corporation); Franck Cappello (Argonne
  National Laboratory (ANL), University of Illinois); and Melissa C. Smith 
 (Clemson University)\n\n
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