high performance computing on graphics processing units: hgpu.org

Scan and segmented scan algorithms are crucial building blocks for a great many data-parallel algorithms. Segmented scan and related primitives also provide the necessary support for the flattening transform, which allows for nested data-parallel programs to be compiled into flat data-parallel languages. In this paper, we describe the design of efficient scan and segmented scan […]

The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations presents additional challenges. Given its role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-vector multiplication (SpMV) is of […]

Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. This paper describes how fine-grained parallel genetic algorithms can be mapped to programmable graphics hardware found in commodity PC. Our approach stores chromosomes and their fitness values in texture memory on graphics card. Both fitness evaluation and genetic operations are implemented entirely with […]

Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In […]

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 […]