high performance computing on graphics processing units: hgpu.org

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 […]

Many scientific simulations and models are based upon one or more coupled field equations. Fields are often modelled as a regular mesh or grid of individual field variables where each degrees of freedom or site variable is a scalar or vector quantity. Visualising such quantities interactively can be a great aid to debugging as well […]

The Cahn-Hilliard-Cook equation continues to be a useful model describing binary phase separation in systems such as alloys and other physical and chemical applications. We describe our investigation of this field equation and report on the various discretisation schemes we used to integrate the system in one-, two- and three-dimensions. We also discuss how the […]

We show how compiler technology can generate fast and efficient yet human-readable data-parallel simulation code for solving certain partial differential equation (PDE) based problems. We present a code parser and generator based on an ANTLR grammar and tree walking approach that transforms a mathematical formulation of an equation such as the Cahn-Hilliard family into simulation […]

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA’s CUDA and Khronos consortium’s open compute language (OpenCL) support […]