RT info:eu-repo/semantics/article
T1 Optimizing an APSP implementation for NVIDIA GPUs using kernel characterization criteria
A1 Ortega-Arranz, Hector
A1 Torres, Yuri
A1 Gonzalez-Escribano, Arturo
A1 Llanos, Diego R.
K1 Informática
K1 APSP
K1 Cache configuration
K1 Concurrent kernel
K1 GPU
K1 Kernel characterization
K1 Threadblock size
K1 1203 Ciencia de Los Ordenadores
K1 3304 Tecnología de Los Ordenadores
AB During the last years, GPU manycore devices have demonstrated their usefulness to accelerate computationally intensive problems. Although arriving at a parallelization of a highly parallel algorithm is an affordable task, the optimization of GPU codes is a challenging activity. The main reason for this is the number of parameters, programming choices, and tuning techniques available, many of them related with complex and sometimes hidden architecture details. A useful strategy to systematically attack these optimization problems is to characterize the different kernels of the application, and use this knowledge to select appropriate configuration parameters. The All-Pair Shortest-Path (APSP) problem is a well-known problem in graph theory whose objective is to find the shortest paths between any pairs of nodes in a graph. This problem can be solved by highly parallel and computational intensive tasks, being a good candidate to be exploited by manycore devices. In this paper, we use kernel characterization criteria to optimize an APSP algorithm implementation for NVIDIA GPUs. Our experimental results show that the combined use of proper configuration policies, and the concurrent kernels capability of new CUDA architectures, leads to a performance improvement of up to 62 % with respect to one of the possible configurations recommended by CUDA, considered as baseline.
PB Springer
SN 0920-8542
YR 2014
FD 2014
LK https://uvadoc.uva.es/handle/10324/70416
UL https://uvadoc.uva.es/handle/10324/70416
LA eng
NO The Journal of Supercomputing, Vol. 70, Issue 2, November 2014, pags. 786-798, ISSN 0920-8542
NO Producción Científica
DS UVaDOC
RD 24-nov-2024