Create 2023-10-01-open-sycl-on-heterogeneous-gpu-systems-a-case-of-study.md

Adds, "Open SYCL on heterogeneous GPU systems: A case of study"

Author: codeplaymax

content/research_papers/2023/2023-10-01-open-sycl-on-heterogeneous-gpu-systems-a-case-of-study.md

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+---
+contributor: max
+date: '2023-10-01T08:08:10.490000+00:00'
+title: 'Open SYCL on heterogeneous GPU systems: A case of study'
+external_url: https://arxiv.org/ftp/arxiv/papers/2310/2310.06947.pdf
+authors:
+  - name: Rocío Carratalá-Sáez
+  - name: Francisco J. Andújar
+  - name: Yuri Torres
+  - name: Arturo Gonzalez-Escribano
+  - name: Diego R. Llanos
+tags:
+  - gpu
+  - cuda
+  - hpc
+  - hip
+---
+
+Computational platforms for high-performance scientific applications are becoming more heterogenous, including hardware 
+accelerators such as multiple GPUs. Applications in a wide variety of scientific fields require an efcient and careful 
+management of the computational resources of this type of hardware to obtain the best possible performance. However, 
+there are currently different GPU vendors, architectures and families that can be found in heterogeneous clusters or 
+machines. Programming with the vendor provided languages or frameworks, and optimizing for specific devices, may become 
+cumbersome and compromise porta-bility to other systems. To overcome this problem, several proposals for high-level 
+heterogeneous programming have appeared, trying to reduce the development effort and increase functional and performance 
+portability, specifically when using GPU hardware accelerators. This paper evaluates the SYCL programming model, using the 
+Open SYCL compiler, from two different perspectives: The performance it offers when dealing with single or multiple GPU devices 
+from the same or different vendors, and the development effort required to implement the code. We use as case of study the Finite Time Lyapunov Exponent 
+calculation over two real-world scenarios and compare the performance and the development effort of its Open SYCL-based version against 
+the equivalent versions that use CUDA or HIP. Based on the experimental results, we observe that the use of SYCL does not lead to a 
+remarkable overhead in terms of the GPU kernels execution time. In general terms, the Open SYCL development effort for the host code 
+is lower than that observed with CUDA or HIP. Moreover, the SYCL version can take advantage of both CUDA and AMD GPU devices simultaneously 
+much easier than directly using the vendor-specific programming solutions.