Layer-Based Scheduling of Parallel Tasks for Heterogeneous Cluster Platforms

Dümmler, Jörg; Rünger, Gudula

doi:10.1007/978-3-319-03859-9_3

Cited by 2 publications

(1 citation statement)

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“…All applications have been coded combining OpenMP 5.2 and the ROCM-3.5.0 framework and compiled with llvm 12. The CPU code has been implemented combining a C++ NPB-MZ implementation 8 and the original NPB-MZ Fortran implementation 5 to generate a version compatible with the ROCM implementation of the applications. The input mesh sizes correspond to class D using a total 13GB of memory and 1024 zones for SP-MZ and BT-MZ, and 16 zones for LU-MZ.…”

Section: Discussionmentioning

confidence: 99%

Compute units in OpenMP: Extensions for heterogeneous parallel programming

Gonzàlez‐Tallada,

Morancho

2023

Concurrency and Computation

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SummaryThis article evaluates the current support for heterogeneous OpenMP 5.2 applications regarding the simultaneous activation of host and device computing units (e.g., CPUs, GPUs, or FPGAs). The article identifies limitations in the current OpenMP specification and describes the design and implementation of novel OpenMP extensions and runtime support for heterogeneous parallel programming. The Compute Unit (CUs) abstraction is introduced in the OpenMP programming model. The Compute Unit abstraction is defined in terms of an aggregation of computing elements (e.g., CPUs, GPUs, FPGAs). On top of CUs, the article describes dynamic work sharing constructs and schedulers that address the inherent differences in compute power of host and device CUs. New constructs and the corresponding runtime support are described for the new abstractions. The article evaluates the case of a hybrid multilevel parallelization of the NPB‐MZ benchmark suite. The implementation exploits both coarse‐grain and fine‐grain parallelism, mapped to CUs of different nature (GPUs and CPUs). All CUs are activated using the new extensions and runtime support. We compare hybrid and nonhybrid executions under two state‐of‐the‐art work‐distribution schemes (Static and Dynamic Task schedulers). On a computing node composed of one AMD EPYC 7742 @ 2.250GHz (64 cores and 2 threads/core, totalling 128 threads per node) and 2 GPU AMD Radeon Instinct MI50 with 32GB, hybrid executions present speedups from 1.08 up to 3.18 with respect to a nonhybrid GPU implementation, depending on the number of activated CUs.

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Section: Discussionmentioning

confidence: 99%