2020/10/14 by Weile Wei, Wei, Weile, Arghya Chatterjee +7
Computer Science · Physics and Astronomy · #Advanced Data Storage Technologies #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Parallel #Parallel Computing and Optimization Techniques #Physics of Superconductivity and Magnetism #Strongly Correlated Electrons (cond-mat.str-el) #Superconductivity (cond-mat.supr-con) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2010.07098
openalex publication_date 2020/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
This paper describes how we successfully used the HPX programming model to port the DCA++ application on multiple architectures that include POWER9, x86, ARM v8, and NVIDIA GPUs. We describe the lessons we can learn from this experience as well as the benefits of enabling the HPX in the application to improve the CPU threading part of the code, which led to an overall 21% improvement across architectures. We also describe how we used HPX-APEX to raise the level of abstraction to understand performance issues and to identify tasking optimization opportunities in the code, and how these relate to CPU/GPU utilization counters, device memory allocation over time, and CPU kernel-level context switches on a given architecture.