high performance computing on graphics processing units: hgpu.org

In this paper we describe a successful effort to accelerate the two-point angular correlation function—a basic statistics tool used in the field of cosmology to characterize the distribution of the matter and energy in the Universe—by using an NVIDIA GPU-based system. We demonstrate the use of GPUs to accelerate the calculation of histograms of angular […]

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 […]

Evaluation of electron repulsion integrals (ERIs) takes considerable time in modern quantum chemistry applications and also presents a certain difficulty to be efficiently computed on GPUs. Here, we describe a novel methodology for generating high-arithmetic-density kernels for ERI evaluation of d and higher angular momentum functions, as well as highlight challenges associated with the efficient […]

We present a CUDA C implementation of the Conjugate Gradient (CG) and multi-mass CG solver from the MILC quantum chromodynamics package to speedup improved staggered quarks computations on NVIDIA GPUs. The implementation is built on the QUDA package from Boston University.

Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp ftp://cs.ucl.ac.uk/genetic/gp-code/random-numbers/gpu park-miller.tar.gz

Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp cs.ucl.ac.uk genetic/gp-code/random-numbers/gpu_park-miller.tar.gz.

A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of 1/4 million RPN expressions on graphics cards and nVidia Tesla T10P. Using sub-machine code GP a sustain peak performance of 212 billion GP operations per second (3300 speed up) and an average of 4.5 peta GP ops […]

Rather than attempting to evolve a complete program from scratch we demonstrate genetic interface programming by automatically generating a parallel CUDA kernel with identical functionality to existing highly optimised ancient sequential C code. Generic GPGPU nVidia kernel C++ code is converted into a BNF grammar. Strongly typed genetic programming uses the BNF to generate compilable […]

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) […]

Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly standardised and general models that run in proprietary software packages to ad hoc hand-crafted simulations codes for very specific […]

The Time Dependent Ginzburg Landau(TDGL) equation models a complex scalar field and is used to study a variety of different physical systems and exhibits phase transitional behaviours that necessitate study using numerical simulation methods. We employ fast data-parallel simulation algorithms on Graphical Processing Units (GPUs) and report on performance data and stability tradeoffs from using […]

Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVidia’s CUDA can be used to harness the power of GPUs for purposes other than computer graphics. GPUs are designed for processing twodimensional data. In previous work we have […]