Sylkan: Towards a Vulkan Compute Target Platform for SYCL

Peter Thoman, Daniel Gogl, T. Fahringer
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引用次数: 4

Abstract

SYCL is a modern high-level C++ programming interface which excels at expressing data parallelism for heterogeneous hardware platforms in a programmer-friendly way, and is standardized by the Khronos Group. The latest version of the standard, SYCL 2020, removes the previous dependence of the specification and its implementations on an underlying OpenCL target, opening the door for compliant alternative implementations. In this paper, we discuss the opportunities and challenges of mapping SYCL to Vulkan, a low-level explicit programming model for GPUs. This includes an analysis of the potential semantic mismatch between each respective standard, as well as approaches to work around some of these issues. Additionally, we present a prototype research implementation of Sylkan, a SYCL compiler and runtime targeting Vulkan. In order to evaluate our prototype qualitatively and quantitatively, we chose a variety of functional tests as well as three performance benchmarks. For the functional tests, we discuss and categorize the failures of the current prototype, noting which semantic mismatch or missing implementation causes them. For the performance benchmarks, we compare execution times against a OpenCL-based SYCL implementation and a native Vulkan version of each benchmark, on two hardware platforms.
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面向SYCL的Vulkan计算目标平台
SYCL是一种现代的高级c++编程接口,它擅长以一种程序员友好的方式表达异构硬件平台的数据并行性,并由Khronos Group进行了标准化。该标准的最新版本SYCL 2020消除了先前对底层OpenCL目标的规范及其实现的依赖,为兼容的替代实现打开了大门。在本文中,我们讨论了将SYCL映射到Vulkan的机遇和挑战,Vulkan是gpu的低级显式编程模型。这包括对每个标准之间潜在的语义不匹配的分析,以及解决其中一些问题的方法。此外,我们还介绍了Sylkan的原型研究实现,Sylkan是针对Vulkan的SYCL编译器和运行时。为了定性和定量地评估我们的原型,我们选择了各种功能测试以及三个性能基准。对于功能测试,我们讨论并对当前原型的失败进行分类,注意是语义不匹配或缺少实现导致了失败。对于性能基准测试,我们在两个硬件平台上比较了基于opencl的SYCL实现和每个基准测试的原生Vulkan版本的执行时间。
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