Understanding the landscape of scientific software used on high-performance computing platforms

Abstract

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.

Flash-X: A multiphysics simulation software instrument

“…FLASH's scientific impact is clearly demonstrated by the citation history of the original paper describing the code, as shown in Figure 5. Analysis in [36,37] further quantifies the scientific significance and impact of the code on science. FLASH has not only been used extensively for science, it has also been among the pioneers in giving due importance to software quality and adopting rigorous auditing and productivity practices [38].…”

Simulation Intelligence: Towards a New Generation of Scientific Methods

“…And mainly, most of the available software has grown through accretion, where code addition is driven by the need for producing domain science papers without adequate planning for code robustness and reliability. [599] The Department of Energy's 2019 AI for Science Report states "Today, the coupling of traditional modeling and simulation codes with AI capabilities is largely a one-off capability, replicated with each experiment. The frameworks, software, and data structures are distinct, and APIs do not exist that would enable even simple coupling of simulation and modeling codes with AI libraries and frameworks."…”

“…Some works have stated that silicon photonic interconnects could provide large bandwidth densities at high-energy efficiencies to help solve some issues related to the increased parallelism and data intensity and movement [620,621]. At the software level [599], compilers, libraries, and other middleware, should be adapted to these new platforms -perhaps in automated ways enabled by machine programming and program synthesis (e.g. in "Arbor" for neuronal network simulations on HPC [622]).…”