Affiliations: 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
Note: [] Corresponding author: H. Carter Edwards, Computing Research Center, Sandia National Laboratories, Livermore, CA, USA. E-mail: [email protected].
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].
