EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Manuel de Castro, Inmaculada Santamaria-Valenzuela, Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos
Departamento de Informática, Escuela de Ingeniería Informática, Universidad de Valladolid, Campus Miguel Delibes s/n, 47011 Valladolid, Spain
The Journal of Supercomputing, 286, 2023


   title={EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs},

   author={de Castro, Manuel and Santamaria-Valenzuela, Inmaculada and Torres, Yuri and Gonzalez-Escribano, Arturo and Llanos, Diego R},

   journal={The Journal of Supercomputing},





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.
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