This paper aims to provide GPU acceleration of an decision support for selecting software and hardware architecture for content-based document comparison. We evaluate Java, C, CUDA C and OpenCL implementations of an image characterization algorithm used for content-based document comparison on a CPU and NVIDIA and AMD graphics processing units (GPUs). Based on our experimental results, we conclude that the original Java implementation of the image characterization algorithm running on a CPU-based architecture can be accelerated by a factor of 6 if the Java code is re-implemented in C, or by a factor of almost 16 if the Java code is re-implemented in CUDA C and run on NVIDIA GTX 480 GPU hardware. We also provide a power efficiency analysis.

We present an implementation of the improved staggered quark action lattice QCD computation designed for execution on a GPU cluster. The parallelization strategy is based on dividing the space-time lattice along the time dimension and distributing the sub-lattices among the GPU cluster nodes. We provide a mixed-precision floating-point GPU implementation of the multi-mass conjugate gradient solver. Our single GPU implementation of the conjugate gradient solver achieves a 9x performance improvement over the highly optimized code executed on a state-of-the-art eight-core CPU node. The overall application executes almost six times faster on a GPU-enabled cluster vs. a conventional multi-core cluster. The developed code is currently used for running production QCD calculations with electromagnetic corrections.

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 field generation" phase requires capability-level supercomputing, corresponding to O(100) GPUs or more. Such strong scaling has not been previously achieved. In this contribution, we demonstrate that using a multi-dimensional parallelization strategy and a domain-decomposed preconditioner allows us to scale into this regime. We present results for two popular discretizations of the Dirac operator, Wilson-clover and improved staggered, employing up to 256 GPUs on the Edge cluster at Lawrence Livermore National Laboratory.

We present an implementation of one of the direct self-consistent-field (DSCF) calculation techniques, the restricted Hartree-Fock method, on a high-performance computing cluster outfitted with graphics processing units (GPUs) and demonstrate its effectiveness and scalability up to 128 cluster nodes on molecules of as many as 1,732 atoms. We discuss the overall parallel application architecture that relies on message passing interface for distributing workload among GPU cluster nodes and POSIX threads to manage the use of GPUs internal to each node. This approach of combining coarse and fine-grain parallelism on a distributed memory system allows to perform DSCF calculations on molecules that up until now have been unattainable due to the excessive computational requirements.

Large-scale GPU clusters are gaining popularity in the scientific computing community. However, their deployment and production use are associated with a number of new challenges. In this paper, we present our efforts to address some of the challenges with building and running GPU clusters in HPC environments. We touch upon such issues as balanced cluster architecture, resource sharing in a cluster environment, programming models, and applications for GPU clusters.

We present results of porting an important kernel of a production molecular dynamics simulation program, NAMD, to the Cell/B.E. processor. The non-bonded force-field kernel, as implemented in the NAMD SPEC 2006 CPU benchmark, has been implemented. Both single-precision and double-precision floating-point kernel variations are considered, and performance results obtained on the Cell/B.E., as well as several other platforms, are reported. Our results obtained on a 3.2 GHz Cell/B.E. blade show linear speedups when using multiple synergistic processing elements.