Evaluating Performance and Energy Efficiency of Parallel Programming Models in Heterogeneous Computing Systems

We provide a detailed evaluation of several parallel programming models, emphasizing both performance and energy efficiency in heterogeneous computing systems. The evaluation employs a diverse array of hardware, including Intel Xeon and AMD Epyc CPUs, along with NVIDIA GPUs featuring Pascal, Turing,...

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Veröffentlicht in:Proceedings of the IEEE International Symposium on Workload Characterization S. 309 - 319
Hauptverfasser: Sevim, Demirhan, Bilgin, Baturalp, Akturk, Ismail
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 15.09.2024
Schlagworte:
Computational modeling
CUDA
Data models
Energy efficiency
Graphics processing units
Hardware
Heterogeneous networks
Heterogeneous Systems
HIP
Memory management
OpenMP
Parallel programming
Performance evaluation
SYCL
ISSN:2835-2238
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Beschreibung
Zusammenfassung:We provide a detailed evaluation of several parallel programming models, emphasizing both performance and energy efficiency in heterogeneous computing systems. The evaluation employs a diverse array of hardware, including Intel Xeon and AMD Epyc CPUs, along with NVIDIA GPUs featuring Pascal, Turing, and Ampere architectures, and an AMD GPU with Vega10 architecture. We utilize SYCL, OpenMP, CUDA, and HIP for implementing benchmarks in 11 varied application domains, offering a comprehensive perspective on the capabilities of these programming models in diverse computing environments.
ISSN:2835-2238
DOI:10.1109/IISWC63097.2024.00035