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arXiv · 2104.06494

PAGANI: A Parallel Adaptive GPU Algorithm for Numerical

Ioannis Sakiotis·Kamesh Arumugam·Marc Paterno·Desh Ranjan·Balša Terzić·Mohammad Zubair
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Abstract

We present a new adaptive parallel algorithm for the challenging problem of multi-dimensional numerical integration on massively parallel architectures. Adaptive algorithms have demonstrated the best performance, but efficient many-core utilization is difficult to achieve because the adaptive work-load can vary greatly across the integration space and is impossible to predict a priori. Existing parallel algorithms utilize sequential computations on independent processors, which results in bottlenecks due to the need for data redistribution and processor synchronization. Our algorithm employs a high-throughput approach in which all existing sub-regions are processed and sub-divided in parallel. Repeated sub-region classification and filtering improves upon a brute-force approach and allows the algorithm to make efficient use of computation and memory resources. A CUDA implementation shows orders of magnitude speedup over the fastest open-source CPU method and extends the achievable accuracy for difficult integrands. Our algorithm typically outperforms other existing deterministic parallel methods.

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Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balša Terzić, Mohammad Zubair. 2021-04-13. PAGANI: A Parallel Adaptive GPU Algorithm for Numerical. https://arxiv.org/abs/2104.06494

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