NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs

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

“…The performance gap widened with increasing N and reached about 22x and 7.8x compared to Numba and CuPy for N = 2 × 10 9 . These findings are in line with those of previous studies (e.g., [39,41]).…”

“…The generated PRNs are immediately consumed within the kernel without storing them in the global memory. We compared our results to our implementation in CUDA C. Note that the 1D MCRT test problem was implemented with 15, 26, and 37 lines of code in CuPy, Numba, and CUDA C, respectively, reflecting the ease of implementation in CuPy and Numba relative to CUDA C [41].…”

“…Many studies have explored how well GPUs perform in various applications [22][23][24][25]. These investigations compare the performance and highlight the strength and weaknesses of popular programming platforms such as CUDA C [26][27][28], CUDA Fortran [29][30][31], OpenCL [32][33][34], OpenACC [35,36], OpenMP [37,38] and Python-based compilers and libraries like Numba, CuPy, and Python CUDA [39][40][41][42][43][44]. Despite these efforts, a quest for a paradigm that offers simplicity of implementation and portability in combination with high performance remains one of the main goals of scientific computing (e.g., [45][46][47][48]).…”

“…There are a number of works that study these platforms in various contexts. Di Domenico et al [41] observed Numba achieving performance levels comparable to CUDAbased C++ using NAS parallel benchmark kernels. Oden [39] compared Numba with CUDA C, revealing the slower performance of Numba by 50-85% for compute-intensive benchmarks.…”