Publications

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The manycore revolution can be characterized by increasing thread counts, decreasing memory per thread, and diversity of continually evolving manycore architectures. High performance computing (HPC) applications and libraries must exploit increasingly finer levels of parallelism within their codes to sustain scalability on these devices. We found that a major obstacle to performance portability is the diverse and conflicting set of constraints on memory access patterns across devices. Contemporary portable programming models address manycore parallelism (e.g., OpenMP, OpenACC, OpenCL) but fail to address memory access patterns. The Kokkos C++ library enables applications and domain libraries to achieve performance portability on diverse manycore architectures by unifying abstractions for both fine-grain data parallelism and memory access patterns. In this paper we describe Kokkos’ abstractions, summarize its application programmer interface (API), present performance results for unit-test kernels and mini-applications, and outline an incremental strategy for migrating legacy C++ codes to Kokkos. Furthermore, the Kokkos library is under active research and development to incorporate capabilities from new generations of manycore architectures, and to address a growing list of applications and domain libraries.

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The rapidly improving compute capability of contemporary processors and accelerators is providing the opportunity for significant increases in the accuracy and fidelity of scientific calculations. In this paper we present performance studies of a new molecular dynamics (MD) potential called SNAP. The SNAP potential has shown great promise in accurately reproducing physics and chemistry not described by simpler potentials. We have developed new algorithms to exploit high single-node concurrency provided by three different classes of machine: the Titan GPU-based system operated by Oak Ridge National Laboratory, the combined Sequoia and Vulcan BlueGene/Q machines located at Lawrence Livermore National Laboratory, and the large-scale Intel Sandy Bridge system, Chama, located at Sandia. Our analysis focuses on strong scaling experiments with approximately 246,000 atoms over the range 1-122,880 nodes on Sequoia/Vulcan and 40-18,630 nodes on Titan. We compare these machine in terms of both simulation rate and power efficiency. We find that node performance correlates with power consumption across the range of machines, except for the case of extreme strong scaling, where more powerful compute nodes show greater efficiency. This study is a unique assessment of a challenging, scientifically relevant calculation running on several of the world's leading contemporary production supercomputing platforms. © 2014 Springer International Publishing.

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The manycore revolution in computational hardware can be characterized by increasing thread counts, decreasing memory per thread, and architecture specific performance constraints for memory access patterns. High performance computing (HPC) on emerging many core architectures requires codes to exploit every opportunity for thread-level parallelism and satisfy conflicting performance constraints. We developed the Kokkos C++ library to provide scientific and engineering codes with a user accessible many core performance portable programming model. The two foundational abstractions of Kokkos are (1) dispatch work to a many core device for parallel execution and (2) manage multidimensional arrays with polymorphic layouts. The integration of these abstractions enables users' code to satisfy multiple architecture specific memory access pattern performance constraints without having to modify their source code. In this paper we describe the Kokkos abstractions, summarize its application programmer interface (API), and present performance results for a molecular dynamics computational kernel and finite element mini-application. © 2013 IEEE.

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