Leonardo Solis-Vasquez, E. Mascarenhas, Andreas Koch
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引用次数: 4
摘要
近年来,英特尔推出了oneAPI,作为基于数据并行c++ (dpc++)语言的统一跨架构编程模型,而dpc++又是基于c++和SYCL标准语言。为了便于迁移原来为NVIDIA gpu编写的遗留CUDA代码,开发人员可以使用英特尔dpc++兼容性工具,该工具旨在自动将代码从CUDA迁移到SYCL。虽然这种工具辅助的代码迁移是利用英特尔oneAPI生态系统的一个很好的起点,但仍然需要手动完成代码和调优的步骤。在本文中,我们介绍了AutoDock-GPU,一个广泛使用的分子对接应用程序,从CUDA迁移到SYCL的经验。我们的讨论主要集中在:(1)使用这种自动源代码迁移工具,(2)为功能和优化所需的手动代码改进,以及(3)在多核cpu和高端gpu(如NVIDIA A100和最近推出的Intel Data Center Max 1550设备)上以这种方式实现的性能比较。
Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study
In recent years, Intel introduced oneAPI as a unified and cross-architecture programming model based on the Data Parallel C++ (DPC++) language, which in turn, is based on the C++ and SYCL standard languages. In order to facilitate the migration of legacy CUDA code originally written for NVIDIA GPUs, developers can employ the Intel DPC++ Compatibility Tool, which aims to automatically migrate code from CUDA to SYCL. While this tool-assisted code migration is a good starting point for leveraging the Intel oneAPI ecosystem, manual steps for code completion and tuning are still required. In this paper, we present our experiences migrating AutoDock-GPU, a widely-used molecular docking application, from CUDA to SYCL. Our discussion focuses on: (1) the use of this automated source-code migration tool, (2) the required manual code refinement for functionality and optimization, and (3) the comparison of the performance achieved in this manner on multi-core CPUs as well as on high-end GPUs, such as NVIDIA A100 and the recently-launched Intel Data Center Max 1550 device.