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 computa-
tion. However, the development of stencil programs that can work with huge grids in
distributed systems with multiple GPUs is not straightforward, since it requires solv-
ing 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 sup-
ports 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 heterogene-
ous types of GPU
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