Scientific discovery increasingly relies on computation through simulations, analytics, and machine and deep learning. Of these, simulations on high-performance computing (HPC) platforms have been the cornerstone of scientific computing for more than two decades. However, the development of simulation software has, in general, occurred through accretion, with a few exceptions. With an increase in scientific understanding, models have become more complex, rendering an accretion mode untenable to the point where software productivity and sustainability have become active concerns in scientific computing. In this survey paper, we examine a modest set of HPC scientific simulation applications that are already using cutting-edge HPC platforms. Several have been in existence for a decade or more. Our objective in this survey is twofold: first, to understand the landscape of scientific computing on HPC platforms in order to distill the currently scattered knowledge about software practices that have helped both developer and software productivity, and second, to understand the kind of tools and methodologies that need attention for continued productivity.

中文翻译：

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了解用于高性能计算平台的科学软件的前景

科学发现越来越依赖于通过模拟、分析、机器和深度学习进行的计算。其中，高性能计算 (HPC) 平台上的模拟已成为科学计算的基石超过二十年。然而，除了少数例外，模拟软件的开发一般是通过吸积进行的。随着科学理解的增加，模型变得更加复杂，使得吸积模式站不住脚，以至于软件生产力和可持续性已成为科学计算中的积极关注点。在这份调查报告中，我们研究了一组适度的 HPC 科学模拟应用程序，这些应用程序已经在使用尖端的 HPC 平台。有几个已经存在了十年或更长时间。我们在本次调查中的目标有两个：首先，