Evaluating performance and energy efficiency of parallel programming models in heterogeneous computing systems

Name: Evaluating performance and energy efficiency of parallel programming models in heterogeneous computing systems
Author: Akturk, Ismail, Bilgin, Baturalp, Sevim, Demirhan

İsim	Evaluating performance and energy efficiency of parallel programming models in heterogeneous computing systems
Yazar	Akturk, Ismail, Bilgin, Baturalp, Sevim, Demirhan
Basım Tarihi:	2024-01-01
Basım Yeri	- IEEE
Konu	HIP, CUDA, OpenMP, SYCL, Energy efficiency, Heterogeneous systems
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-8-3503-5604-5
Kayıt Numarası	624dfc60-e266-457f-941f-4c52628e5f01
Lokasyon	Computer Science
Tarih	2024-01-01
Notlar	TÜBİTAK
Örnek Metin	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.
DOI	10.1109/IISWC63097.2024.00035

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Evaluating performance and energy efficiency of parallel programming models in heterogeneous computing systems

Yazar Akturk, Ismail, Bilgin, Baturalp, Sevim, Demirhan

Basım Tarihi 2024-01-01

Basım Yeri - IEEE

Konu HIP, CUDA, OpenMP, SYCL, Energy efficiency, Heterogeneous systems

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-8-3503-5604-5

Kayıt Numarası 624dfc60-e266-457f-941f-4c52628e5f01

Lokasyon Computer Science

Tarih 2024-01-01

Notlar TÜBİTAK

Örnek Metin 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.

DOI 10.1109/IISWC63097.2024.00035