论文标题
在十年的研究软件工程GPU应用程序中汲取的经验教训
Lessons learned in a decade of research software engineering GPU applications
论文作者
论文摘要
经过多年使用图形处理单元（GPU）来加速在层析成像，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，射电天文学和本地化显微镜等领域的科学应用，我们注意到许多技术，社会技术和非技术挑战，这些挑战是研究软件工程师的许多技术，社会技术和非技术挑战。尽管其中一些挑战（例如在项目中管理不同的编程语言，或不得不处理不同的内存空间，这对于所有涉及GPU的软件项目都是常见的，但其他挑战则更为典型。在这些挑战中，我们包括更改决议或量表，随着时间的推移维护应用程序并使其可持续，并评估获得的结果和所达到的绩效。 ％在本文中，我们提出了从研究软件工程GPU应用程序中汲取的挑战和经验教训。
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.
