high performance computing on graphics processing units: hgpu.org

In this paper, we report a parallel hybrid genetic algorithm (HGA) on consumer-level graphics cards. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number generation procedure are performed in graphics processing unit (GPU) and thus our parallel HGA can […]

As a well known NP-hard problem, SAT problem is widely discussed by computer science society. In this paper, two common algorithms for SAT problems are implemented based on graphic hardware. They are greedy local search and genetic algorithm. After a brief description of the basic algorithm, we give our modification of the algorithm for fitting […]

We propose implementing a parallel EA on consumer graphics cards, which we can find in many PCs. This lets more people use our parallel algorithm to solve large-scale, real-world problems such as data mining. Parallel evolutionary algorithms run on consumer-grade graphics hardware achieve better execution times than ordinary evolutionary algorithms and offer greater accessibility than […]

In this paper we demonstrate the use of the graphics processing unit (GPU) to accelerate evolutionary computation applications, in particular genetic programming approaches. We show that it is possible to get speed increases of several hundred times over a typical CPU implementation, catapulting GPU processing for these applications into the realm of HPC This increase […]

In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community aslow […]

A new class of FPGA-based accelerators is presented for Cartesian Genetic Programming (CGP). The accelerators contain a genetic engine which is reused in all applications. Candidate programs (circuits) are evaluated using application-specific virtual reconfigurable circuit (VRC) and fitness unit. Two types of VRCs are proposed. The first one is devoted for symbolic regression problems over […]

The evolution of image filters using genetic programming is a relatively unexplored task. This is most likely due to the high computational cost of evaluating the evolved programs. The parallel processors available on modern graphics cards can be used to greatly increase the speed of evaluation. Previous papers in this area dealt with tasks such […]

We propose a graphics processor unit (GPU)-accelerated method for real-time computing and rendering cellular automata (CA) that is applied to hexagonal grids.Based on our previous work [9] -which introduced first and second dimensional cases- this paper presents a model for hexagonal grid algorithms. Proposed method is novel and it encodes and transmits large CA key-codes […]

This work is focussed on the development and analysis of a new class of algorithms, called cellular memetic algorithms (cMAs), which will be evaluated here on the satisfiability problem (SAT). For describing a cMA, we study the effects of adding specific knowledge of the problem to the fitness function, the crossover and mutation operators, and […]

This paper presents a massively parallel Evolution Strategy – Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization functions. The algorithm is specifically designed for a graphic processing unit (GPU) hardware platform featuring ‘Single Instruction – Multiple Thread’ (SIMT). GPU computing is an emerging desktop parallel computing platform. The […]

This paper studies the efficiency of using Pattern Search (PS) on bound constrained optimization functions on a Graphics Processing Unit (GPU) computing platform. Pattern Search is a direct search optimization technique that does not require derivative information on non-linear programming problems. Pattern Search is ideally suited to a GPU computing environment due to its low […]

A widely available and economic means of increasing the computing power applied to a problem is to use modern graphics processing units (GPUs) for parallel processing. We present a new, optimized general methodology for deploying genetic programming (GP) to the PC, Xbox 360 video game console, and Zune portable media device. This work describes, for […]