十年来软件工程GPU应用研究的经验教训
Lessons learned in a decade of research software engineering GPU applications
摘要：
多年来，我们一直使用图形处理器（GPU）来加速断层扫描、计算机视觉、气候建模、数字取证、地理空间数据库、粒子物理学、射电天文学和定位显微镜等领域的科学应用，我们注意到研究软件工程师（RSE）可能会遇到许多技术、社会技术和非技术挑战。虽然其中一些挑战，例如在项目中管理不同的程序设计语言，或必须处理不同的内存空间，是所有涉及GPU的软件项目所共有的，但其他挑战则是科学软件项目的典型挑战。在这些挑战中，我们包括改变分辨率或规模，随着时间的推移维护应用程序并使其可持续发展，以及评估所获得的结果和所实现的性能。在本文中，我们提出了挑战和经验教训，从研究软件工程GPU应用程序。
英文摘要：
After years of using Graphics Processing Units (GPUs) to accelerate scientific applications in fields as varied as tomography, computer vision, climate modeling, digital forensics, geospatial databases, particle physics, radio astronomy, and localization microscopy, we noticed a number of technical, socio-technical, and non-technical challenges that Research Software Engineers (RSEs) may run into. While some of these challenges, such as managing different programming languages within a project, or having to deal with different memory spaces, are common to all software projects involving GPUs, others are more typical of scientific software projects. Among these challenges we include changing resolutions or scales, maintaining an application over time and making it sustainable, and evaluating both the obtained results and the achieved performance. %In this paper, we present the challenges and lessons learned from research software engineering GPU applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
