NDLI: Manycore Performance-Portability: Kokkos Multidimensional Array Library

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Manycore Performance-Portability: Kokkos Multidimensional Array Library

Content Provider	Paperity
Author	Mish, Sam Edwards, H. Carter Sunderland, Daniel Porter, Vicki Amsler, Chris
Abstract	Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel execution performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].
File Format	HTM / HTML
ISSN	10589244
DOI	10.3233/SPR-2012-0343
Journal	Scientific Programming
Volume Number	20
e-ISSN	1875919X
Language	English
Publisher	Hindawi Limited
Access Restriction	Open
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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