Manycore Performance-Portability: Kokkos Multidimensional Array Library

Edwards, H. Carter; Sunderland, Daniel; Porter, Vicki; Amsler, Chris; Mish, Sam

doi:10.1155/2012/917630

Title: Manycore Performance-Portability: Kokkos Multidimensional Array Library

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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].

Authors:

Edwards, H. Carter ^[1]; Sunderland, Daniel ^[2]; Porter, Vicki ^[2]; Amsler, Chris ^[3]; Mish, Sam ^[4]

Computing Research Center, Sandia National Laboratories, Livermore, CA, USA
Engineering Sciences Center, Sandia National Laboratories, Albuquerque, NM, USA
Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
Department of Mathematics, California State University, Los Angeles, CA, USA

Publication Date:: Sun Jan 01 00:00:00 EST 2012

Sponsoring Org.:: USDOE

OSTI Identifier:: 1197983

Grant/Contract Number:: AC04-94AL85000; SAND2011-8102J

Resource Type:: Published Article

Journal Name:: Scientific Programming

Additional Journal Information:: Journal Name: Scientific Programming Journal Volume: 20 Journal Issue: 2; Journal ID: ISSN 1058-9244

Publisher:: Hindawi Publishing Corporation

Country of Publication:: Egypt

Language:: English

Citation Formats


                    Edwards, H. Carter, Sunderland, Daniel, Porter, Vicki, Amsler, Chris, and Mish, Sam. Manycore Performance-Portability: Kokkos Multidimensional Array Library.  Egypt: N. p., 2012. 
Web.  doi:10.1155/2012/917630.

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                    Edwards, H. Carter, Sunderland, Daniel, Porter, Vicki, Amsler, Chris, & Mish, Sam. Manycore Performance-Portability: Kokkos Multidimensional Array Library.  Egypt.  https://doi.org/10.1155/2012/917630

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                    Edwards, H. Carter, Sunderland, Daniel, Porter, Vicki, Amsler, Chris, and Mish, Sam. Sun .  
"Manycore Performance-Portability: Kokkos Multidimensional Array Library".  Egypt.  https://doi.org/10.1155/2012/917630.

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@article{osti_1197983,

  title        = {Manycore Performance-Portability: Kokkos Multidimensional Array Library},

  author       = {Edwards, H. Carter and Sunderland, Daniel and Porter, Vicki and Amsler, Chris and Mish, Sam},

  abstractNote = {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].},

  doi          = {10.1155/2012/917630},

  journal      = {Scientific Programming},

  number       = 2,

  volume       = 20,

  place        = {Egypt},

  year         = {Sun Jan 01 00:00:00 EST 2012},

  month        = {Sun Jan 01 00:00:00 EST 2012}

}

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