RT info:eu-repo/semantics/article
T1 EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs
A1 Castro Caballero, Manuel De
A1 Santamaria Valenzuela, Inmaculada
A1 Torres de la Sierra, Yuri
A1 González Escribano, Arturo
A1 Llanos Ferraris, Diego Rafael
K1 Informática
K1 Distributed memory, GPU, Heterogeneous, Stencil, Parallel skeletons
K1 1203 Ciencia de Los Ordenadores
K1 3304 Tecnología de Los Ordenadores
AB Iterative stencil computations are widely used in numerical simulations. They present a high degree of parallelism, high locality and mostly-coalesced memory access patterns. Therefore, GPUs are good candidates to speed up their computation. However, the development of stencil programs that can work with huge grids in distributed systems with multiple GPUs is not straightforward, since it requires solving problems related to the partition of the grid across nodes and devices, and the synchronization and data movement across remote GPUs. In this work, we present EPSILOD, a high-productivity parallel programming skeleton for iterative stencil computations on distributed multi-GPUs, of the same or different vendors that supports any type of n-dimensional geometric stencils of any order. It uses an abstract specification of the stencil pattern (neighbors and weights) to internally derive the data partition, synchronizations and communications. Computation is split to better overlap with communications. This paper describes the underlying architecture of EPSILOD, its main components, and presents an experimental evaluation to show the benefits of our approach, including a comparison with another state-of-the-art solution. The experimental results show that EPSILOD is faster and shows good strong and weak scalability for platforms with both homogeneous and heterogeneous types of GPU.
PB Springer Nature
SN 0920-8542
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/83863
UL https://uvadoc.uva.es/handle/10324/83863
LA eng
NO EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs, de Castro, M., Santamaria-Valenzuela, I., Torres, Y. et al. Journal of Supercomputing 79, 9409–9442 (2023).
NO Producción Científica
DS UVaDOC
RD 29-mar-2026