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UID:submissions.supercomputing.org_SC24_sess734_ws_pawatm102@linklings.com
SUMMARY:Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned using
  a Many-Task-Based Approach
DESCRIPTION:Torben Kalkhof and Andreas Koch (Technical University Darmstad
 t)\n\nCurrent programming models face challenges in dealing with modern su
 percomputers' growing parallelism and heterogeneity. Emerging programming 
 models, like the task-based programming model found in the asynchronous ma
 ny-task HPX programming framework, offer new ways to express parallelism, 
 enhance scalability, and mask synchronization and communication latency on
  multi-core and distributed systems.\n\nRegular high-performance computing
  benchmarks are often unsuitable for comparing different programming model
 s due to their limited code complexity. However, real-world scientific app
 lications are usually too complex. As a middle ground, proxy applications 
 model the behavior of actual scientific problems, while reducing code comp
 lexity.\n\nIn our research on using HPX to program machines with heterogen
 eous compute units (e.g., GPU and FPGA/AI Engines), we have also substanti
 ally optimized a pure HPX-based software baseline of the LULESH proxy appl
 ication. This paper discusses the techniques we applied yielding single-no
 de speed-ups of 1.33x to 2.25x for different problem sizes relative to the
  LULESH OpenMP reference implementation.\n\nTag: Heterogeneous Computing, 
 Parallel Programming Methods, Models, Languages and Environments, PAW-Full
 , Task Parallelism\n\nRegistration Category: Workshop Reg Pass\n\nSession 
 Chairs: Engin Kayraklioglu (Hewlett Packard Enterprise (HPE)); Daniele Lez
 zi (Barcelona Supercomputing Center (BSC)); Karla Vanessa Morris Wright (S
 andia National Laboratories); Irene Moulitsas (Cranfield University); Elli
 ott Slaughter (SLAC National Accelerator Laboratory); and Kenjiro Taura (T
 he University of Tokyo, Japan)\n\n
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