2013 · 128 citations · 12 references
EngineeringComputer EngineeringComputer ScienceComputer ArchitectureParallel AlgorithmsParallel ComputingAccelerator ArchitectureHardware AccelerationMulticore Processor DesignParallel ArchitecturesParallel Computer ArchitectureProcessor Core DesignAccelerator Co-designAcceleratorsLinear AcceleratorsMany-core ProcessorsMulti-core ComputingParallel Sparse Linear AlgebraProcessor Co-design
Dense linear algebra has been traditionally used to evaluate the performance and efficiency of new architectures. This trend has continued for the past half decade with the advent of multi-core processors and hardware accelerators. In this paper we describe how several flavors of the Linpack benchmark are accelerated on Intel's recently released Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Xeon Phi™ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> co-processor (code-named Knights Corner) in both native and hybrid configurations. Our native DGEMM implementation takes full advantage of Knights Corner's salient architectural features and successfully utilizes close to 90% of its peak compute capability. Our native Linpack implementation running entirely on Knights Corner employs novel dynamic scheduling and achieves close to 80% efficiency - the highest published co-processor efficiency. Similarly to native, our single-node hybrid implementation of Linpack also achieves nearly 80% efficiency. Using dynamic scheduling and an enhanced look-ahead scheme, this implementation scales well to a 100-node cluster, on which it achieves over 76% efficiency while delivering the total performance of 107 TFLOPS.
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Dense linear algebra solvers for multicore with GPU accelerators
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