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International Workshop on OpenCL最新文献

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Unlocking performance portability on LUMI-G supercomputer: A virtual screening case study 释放 LUMI-G 超级计算机的性能可移植性:虚拟筛选案例研究
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648125
Gianmarco Accordi, Davide Gadioli, Gianluca Palermo, Luigi Crisci, Lorenzo Carpentieri, Biagio Cosenza, A. Beccari
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引用次数: 0
SYCLomatic: SYCL Adoption for Everyone - Moving from CUDA to SYCL Gets Progressively Easier: Advanced Migration Considerations SYCLomatic:每个人都能采用 SYCL - 从 CUDA 迁移到 SYCL 变得越来越容易:高级迁移注意事项
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648124
Robert Mueller-Albrecht
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引用次数: 0
Acceleration of Quantum Transport Simulations with OpenCL 利用 OpenCL 加速量子传输模拟
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648127
Yosang Jeong, Hoon Ryu
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引用次数: 0
AdaptiveCpp Stdpar: C++ Standard Parallelism Integrated Into a SYCL Compiler AdaptiveCpp Stdpar:集成到 SYCL 编译器中的 C++ 标准并行性
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648117
Aksel Alpay, Vincent Heuveline
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引用次数: 0
SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs SYCL-Bench 2020:在 AMD、Intel 和 NVIDIA GPU 上对 SYCL 2020 进行基准测试
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648120
Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza
Today, the SYCL standard represents the most advanced programming model for heterogeneous computing, delivering both productivity, portability, and performance in pure C++17. SYCL 2020, in particular, represents a major enhancement that pushes the boundaries of heterogeneous programming by introducing a number of new features. As the new features are implemented by existing compilers, it becomes critical to assess the maturity of the implementation through accurate and specific benchmarking. This paper presents SYCL-Bench 2020, an extended benchmark suite specifically designed to evaluate six key features of SYCL 2020: unified shared memory, reduction kernel, specialization constants, group algorithms, in-order queue, and atomics. We experimentally evaluate SYCL-Bench 2020 on GPUs from the three major vendors, i.e., AMD, Intel, and NVIDIA, and on two different SYCL implementations AdaptiveCPP and oneAPI DPC++.
如今,SYCL 标准代表了异构计算最先进的编程模型,在纯 C++17 中提供了生产率、可移植性和性能。由于新功能是由现有编译器实现的,因此通过准确而具体的基准测试来评估实现的成熟度变得至关重要。本文介绍了 SYCL-Bench 2020,这是一个扩展的基准测试套件,专门用于评估 SYCL 2020 的六个关键特性:统一共享内存、还原内核、特殊化常量、分组算法、无序队列和原子。我们在三大厂商(即 AMD、Intel 和 NVIDIA)的 GPU 以及两种不同的 SYCL 实现 AdaptiveCPP 和 oneAPI DPC++ 上对 SYCL-Bench 2020 进行了实验性评估。
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引用次数: 0
Evaluation of SYCL's Different Data Parallel Kernels 对 SYCL 不同数据并行内核的评估
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648130
Marcel Breyer, Alexander Van Craen, Dirk Pflüger
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引用次数: 0
Enabling RAJA on Intel GPUs with SYCL 使用 SYCL 在英特尔 GPU 上启用 RAJA
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648131
Brian Homerding, Arturo Vargas, Thomas Scogland, Robert Chen, Mike Davis, Rich Hornung
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引用次数: 0
Improving Performance Portability of the Procedurally Generated High Energy Physics Event Generator MadGraph Using SYCL 使用 SYCL 提高程序生成的高能物理事件生成器 MadGraph 的性能可移植性
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648116
Nathan S. Nichols, J. T. Childers, T. Burch, Laurence Field
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引用次数: 0
Migrating CUDA to SYCL: A HEP Case Study with ROOT RDataFrame 将 CUDA 移植到 SYCL:使用 ROOT RDataFrame 的 HEP 案例研究
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648122
Jolly Chen, Monica Dessole, A. Varbanescu
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引用次数: 0
Smoothing the Migration from CUDA to SYCL: SYCLomatic Utility Features 平滑从 CUDA 迁移到 SYCL:SYCLomatic 实用程序功能
Pub Date : 2024-04-08 DOI: 10.1145/3648115.3648132
Ziran Zhang, Zhiming Wang, Chenwei Sun, Andy Huang
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引用次数: 0
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