PAGANI: A Parallel Adaptive GPU Algorithm for Numerical [article]

Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balša Terzić, Mohammad Zubair
2021 arXiv   pre-print
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
more » ... necks 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.
arXiv:2104.06494v2 fatcat:vzfw7xay6naapm4nutmyzoxagm