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

arXiv: https://arxiv.org/abs/2010.07098 · HTML: https://mlsanity.com/paper/2010.07098/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [A Case Study of LLVM-Based Analysis for Optimizing SIMD Code Generation](https://mlsanity.com/paper/2106.14332/) (2021)

## Abstract (arXiv)

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.