We present an improved matrix–matrix multiplication routine (General Matrix Multiply [GEMM]) in the MAGMA BLAS library that targets the NVIDIA Fermi graphics processing units (GPUs) using Compute Unified Data Architecture (CUDA). We show how to modify the previous MAGMA GEMM kernels in order to make a more efficient use of the Fermi’s new architectural features, most notably their extended memo...

This paper introduces a parallelization of fuzzy logicbased image processing using Graphics Processor Units (GPUs). Using an NVIDIA 8800 Ultra, a 126 time speed improvement can be made to fuzzy edge extraction making its processing real-time. The GPU can process approximately 42 frames per second at 640x480 image resolution, thus 307,200 inference processes per frame. With a computational speed...

Graphics processing is an application area with high level of parallelism at the data level and at the task level. Therefore, graphics processing units (GPU) are often implemented as multiprocessing systems with high performance floating point processing and application specific hardware stages for maximizing the graphics throughput. In this paper we evaluate the suitability of Transport Trigge...

Physical and engineering practicalities involved in microprocessor design have resulted in flat performance growth for traditional single-core microprocessors. The urgent need for continuing increases in the performance of scientific applications requires the use of many-core processors and accelerators such as graphics processing units (GPUs). This paper discusses GPU acceleration of the multi...

The future of high-performance computing is aligning itself towards the efficient use of highly parallel computing environments. One application where the use of massive parallelism comes instinctively is Monte Carlo simulations, where a large number of independent events have to be simulated. At the core of the Monte Carlo simulation lies the Random Number Generator (RNG). In this paper, the m...

A top end graphics card (GPU) plus a suitable SIMD interpreter, can deliver a several hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI and computational intelligence applications in the new field of general purpose computing on graphics hardware (GPGPU). In particular we survey genetic programming (GP) use with GPU. We give several applications from Bi...

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The paper presents a multi-GPU implementation of the preconditioned conjugate gradient algorithm with an algebraic multigrid preconditioner (PCG-AMG) for an elliptic model problem on a 3D unstructured grid. An efficient parallel sparse matrix-vector multiplication scheme underlying the PCG-AMG algorithm is presented for the manycore GPU architecture. A performance comparison of the parallel sol...

Susan M. Mniszewski, CCS-3; Anders Niklasson, Ed Sanville, Marc J. Cawkwell, T-1 The performance of quantum-based molecular dynamics (MD) simulations using the LANL-developed code LATTE is limited by the time required to compute the density matrix at each time step. The density matrix has been computed historically via the diagonalization of the Hamiltonian matrix. We have investigated the perf...

Lattice spin models are useful for studying critical phenomena and allow the extraction of equilibrium and dynamical properties. Simulations of such systems are usually based on Monte Carlo (MC) techniques, and the main difficulty is often the large computational effort needed when approaching critical points. In this work, it is shown how such simulations can be accelerated with the use of NVI...