Abstract
As High-Performance Computing (HPC) applications involving massive data sets, including large-scale simulations, data analytics, and machine learning, continue to grow in importance, memory bandwidth has emerged as a critical performance factor in contemporary HPC systems. The rapidly escalating memory performance requirements, which traditional DRAM memories often fail to satisfy, necessitate the use of High-Bandwidth Memory (HBM), which offers high bandwidth, low power consumption, and high integration capacity, making it a promising solution for next-generation platforms. However, despite the notable increase in memory bandwidth on modern systems, no prior work has comprehensively assessed the memory bandwidth requirements of a diverse set of HPC applications and provided sufficient justification for the cost of HBM with potential performance gain. This work presents a performance analysis of a diverse range of scientific applications as well as standard benchmarks on platforms with varying memory bandwidth. The study shows that while the performance improvement of scientific applications varies quite a bit, some applications in CFD, Earth Science, and Physics show significant performance gains with HBM. Furthermore, a cost-effectiveness analysis suggests that the applications exhibiting at least a 30% speedup on the HBM platform would justify the additional cost of the HBM.
This work is supported by the National Science Foundation through the Frontera award (OAC-1854828) and the CSA award (OAC-2139536).
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Wang, Y. et al. (2023). Application Performance Analysis: A Report on the Impact of Memory Bandwidth. In: Bienz, A., Weiland, M., Baboulin, M., Kruse, C. (eds) High Performance Computing. ISC High Performance 2023. Lecture Notes in Computer Science, vol 13999. Springer, Cham. https://doi.org/10.1007/978-3-031-40843-4_25
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