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This paper describes how we successfully used the HPX programming model toport the DCA++ application on multiple architectures that include POWER9, x86,ARM v8, and NVIDIA GPUs. We describe the lessons we can learn from thisexperience as well as the benefits of enabling the HPX in the application toimprove the CPU threading part of the code, which led to an overall 21%improvement across architectures. We also describe how we used HPX-APEX toraise the level of abstraction to understand performance issues and to identifytasking optimization opportunities in the code, and how these relate to CPU/GPUutilization counters, device memory allocation over time, and CPU kernel-levelcontext switches on a given architecture.

Performance Analysis of a Quantum Monte Carlo Application on Multiple Hardware Architectures Using the HPX Runtime