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

2024    Sevim, Demirhan; Bilgin, Baturalp; Akturk, Ismail

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.