Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

Abstract

This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport in plane-parallel geometry on three GPU cards: NVIDIA Tesla A100, Tesla V100, and GeForce RTX3080. We compared Numba and CuPy to each other and our CUDA C implementation. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks.

Reappraising Double Pendulum Dynamics across Multiple Computational Platforms

“…Numba, a JIT compiler for Python [40], can be leveraged to optimize numerical computations by compiling Python code to efficient machine instructions, potentially improving the runtime performance of the Python-based simulations [e. g. [41][42][43]. Additionally, the utilization of GPU-accelerated computing libraries like CuPy (CUDA for Python) [44] could significantly accelerate the simulations by offloading computationally intensive tasks to the highly parallel architecture of modern graphics processing units (GPUs) [e. g. [45][46][47]. The massive parallelism provided by GPUs can lead to substantial speedups, especially for large-scale simulations or ensemble runs.…”