Advances into exascale computing
Roman Wyrzykowski, Boleslaw Karol Szymanski · Concurrency and Computation Practice and Experience · 2024
The landscape of high-performance computing (HPC) has been expanding with new technologies and increased system complexity. For hardware, this trend is driven by technological inventions increasing computing power capabilities while taming cost metrics. For applications, we are witnessing increasing growth in algorithms' complexity to accommodate constantly expanding data sizes and take full advantage of increasingly complex processing and storage characteristics. Successfully handling this expansion and maintaining performance and efficiency on adequate levels requires software that matches the emerging hardware and system innovations and can address concerns arising from evolving and new paradigms. An example of the evolving paradigms is heterogeneity-one of the most challenging properties of emerging parallel, distributed, and edge computing platforms. An appealing example of the new paradigms is a vigorous expansion of artificial intelligence (AI) and machine learning (ML) methods that have become pervasive in solving the most demanding problems across many science and engineering disciplines. Also, the approaching end of Moore's Law scaling requires exciting but complex innovations and advances in HPC hardware/software environments that need to be matched by advances in algorithms and software systems to meet the demands of the applications.