Experience Deploying Graph Applications on GPUs with SYCL

被引：1

作者：

Jin, Zheming ^{[1
]}

Vetter, Jeffrey S. ^{[1
]}

机构：

[1] Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA

来源：

PROCEEDINGS OF THE 52ND INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS PROCEEDINGS, ICPP-W 2023 | 2023年

关键词：

SYCL; portability; GPUs; ALGORITHMS;

D O I：

10.1145/3605731.3605744

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

SYCL allows for deployment and use of accelerators across vendors' platforms. In this work, we describe the experience of deploying graph analytics on vendors' GPUs using SYCL. We contrast the CUDA and SYCL application programming interfaces by describing the experience of migrating the applications from CUDA to SYCL, evaluate the performance of the applications on NVIDIA and AMD GPUs, and explore performance improvement with device-level parallelism. The results show that the recent SYCL extensions facilitate functional portability, but improving code optimizations and resource usage for performance portability is needed in the compiler implementation.

引用

页码：30 / 39

页数：10

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