The NPB programs (version 3.4.1) were ported to Python based on C++ and CUDA implementations. We employed Numba environment to design the NPB CUDA programs in Python.

Daniel Di Domenico, Gerson G. H. Cavalheiro, João V. F. Lima, NAS Parallel Benchmark Kernels with Python: A performance and programming effort analysis focusing on GPUs, 2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 2022, pp. 26-33, doi: 10.1109/PDP55904.2022.00013

Daniel Di Domenico, João V. F. Lima, & Gerson G. H. Cavalheiro. NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs, The Journal of Supercomputing (2022), doi: 10.1007/s11227-022-04932-3

NPB-SER - This directory contains the sequential version implemented with Python.

NPB-CUDA - This directory contains the GPU version using Numba to enable CUDA support in Python.

Each file contains the implemented version of the kernels/applications:

EP - Embarrassingly Parallel, floating-point operation capacity
MG - Multi-Grid, non-local memory accesses, short- and long-distance communication
CG - Conjugate Gradient, irregular memory accesses and communication
FT - discrete 3D fast Fourier Transform, intensive long-distance communication
IS - Integer Sort, integer computation and communication

BT - Block Tri-diagonal solver
LU - Scalar Penta-diagonal solver
SP - Lower-Upper Gauss-Seidel solver

Warning: our experiments were executed using the following environment

CUDA 11.2
Python 3.8.8
Conda 4.11.0
numpy 1.20.0
numba 0.53.1
cudatoolkit 10.2.89
cpuinfo 8.0.0 

More info about the environment can be found in the reproducibility folder.

Enter the directory from the version desired and execute:

$ python _BENCHMARK.py -c _WORKLOAD_CLASS

_BENCHMARKs are:

BT, CG, EP, FT, IS, LU, MG, SP

_WORKLOAD_CLASS are:

Class S: small for quick test purposes
Class W: workstation size (a 90's workstation; now likely too small)	
Classes A, B, C: standard test problems; ~4X size increase going from one class to the next	
Classes D, E, F: large test problems; ~16X size increase from each of the previous Classes

Command example:

$ python EP.py -c A

NPB-SER offers timers that can be activated aiming to profile the application. To enable these timers, create a dummy file 'timer.flag' in the main directory of the NPB-SER.

NPB-GPU allows configuring the number of threads per block of each GPU kernel in the benchmarks. Also, it is possible to enable additional timers for profiling pursose. The user can define such configurations using the file config/gpu_config.py.