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The past decade has witnessed a dramatic acceleration of lattice quantum chromodynamics calculations in nuclear and particle physics. This has been due to both significant progress in accelerating the iterative linear solvers using multigrid algorithms, and due to the throughput improvements brought by GPUs. Deploying hierarchical algorithms optimally on GPUs is non-trivial owing to the lack of parallelism...
Graphics Processing Units (GPU) are becoming increasingly popular in high performance computing due to their high performance, high power efficiency and low cost. In this paper, we present results of an effort to implement the fatlink computation -- an important component of many lattice quantum chromo dynamics (LQCD) calculations -- on GPU clusters using the QUDA framework. Two implementations, one...
Lattice Quantum Chromo-dynamics (LQCD) is a computationally challenging problem that solves the discretized Dirac equation in the presence of an SU(3) gauge field. Its key operation is a matrix-vector product, known as the Dslash operator. We have developed a novel multicore architecture-friendly implementation of the Wilson-Dslash operator which delivers 75 Gflops (single-precision) on an Intel®...
Over the past five years, graphics processing units (GPUs) have had a transformational effect on numerical lattice quantum chromodynamics (LQCD) calculations in nuclear and particle physics. While GPUs have been applied with great success to the post-Monte Carlo “analysis” phase which accounts for a substantial fraction of the workload in a typical LQCD calculation, the initial Monte Carlo “gauge...
Graphics Processing Units (GPUs) are having a transformational effect on numerical lattice quantum chromo- dynamics (LQCD) calculations of importance in nuclear and particle physics. The QUDA library provides a package of mixed precision sparse matrix linear solvers for LQCD applications, supporting single GPUs based on NVIDIA's Compute Unified Device Architecture (CUDA). This library, interfaced...
Single step growth of crystalline silicon on insulator (c-SOI) substrates based on rare earth oxide (REO) insulator layers are presented. Growth of crystalline REOs on silicon is possible due to their unique lattice matching to twice the lattice spacing of silicon. The single crystal nature of REOs make further silicon overlayer growth with moderate defect densities possible. In this paper we present...
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