high performance computing on graphics processing units: hgpu.org

Current consumer-grade computers and game devices incorporate very powerful processors that can be used to accelerate many classes of scientific codes. However, programming multi-core chips, hybrid multi-processors or graphical processing units is not an easy task for those programmers that deal mainly with sequential codes. In this paper, we explore the ability of the Cell […]

In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (GP) can be accelerated on one machine using currently available mid-range Graphics Processing Units (GPUs). Cyclic graphs pose different problems for evaluation than do trees and we describe how our CUDA based, “population parallel” evaluator tackles these problems. Previous […]

Using of a cluster of Graphics Processing Unit (GPU) equipped computers, it is possible to accelerate the evaluation of individuals in Genetic Programming. Program compilation, fitness case data and fitness execution are spread over the cluster of computers, allowing for the efficient processing of very large datasets. Here, the implementation is demonstrated on datasets containing […]

Simultaneous Multithreading (SMT) is a technology aimed at improving the throughput of the processor core by applying Instruction Level Parallelism (ILP) and Thread Level Parallelism (TLP). Nevertheless a good control strategy is required when resources are shared among different threads, so that throughput is optimized. We study the application of evolutionary algorithms to improve the […]

Graphics Processing Units (GPUs) have become a major source of computational power for numerical applications. Originally designed for application of time-consuming graphics operations, GPUs are stream processors that implement the SIMD paradigm. Modern programming tools allow developers to access the parallelism of the GPU in a flexible and convenient way, hiding many low level details […]

This paper deals with the mapping of the parallel island-based genetic algorithm with unidirectional ring migrations to nVidia CUDA software model. The proposed mapping is tested using Rosenbrock’s, Griewank’s and Michalewicz’s benchmark functions. The obtained results indicate that our approach leads to speedups up to seven thousand times higher compared to one CPU thread while […]

In this work, we show that consumer-level $100 GPU can be used to significantly speed-up optimization of 0/1 Knapsack problem. We identify strong and weak points of GPU architecture and propose our parallel genetic algorithm model implemented in CUDA running entirely on the GPU. We show that GPU must be utilized for sufficiently long time […]

This paper introduces the implementation of Koza-style tree-based Genetic Programming on General Purpose Graphic Processing Units (GPGPU) using the EASEA language, and shows how a GP algorithm can be easily implemented using EASEA and CUDA. Performance is first discussed on a classical toy problem taken from one of Koza’s books and then on a real […]

This paper presents the first implementation of a generic memetic algorithm on one of the two GPU (Graphic Processing Unit) chips of a GTX295 gaming card. Observed speedups range between x70 and x120, mainly depending on the population size. An automatic parallelization of a memetic algorithm is provided through an upgrade of the EASEA language, […]

This paper shows that it is possible to use General Purpose Graphic Processing Unit cards for a fast evaluation of different Genetic Programming trees on as few as 32 fitness cases by using the hardware scheduling of NVIDIA cards. Depending on the function set, observed speedup ranges between x50 and x250 on one half of […]

We demonstrate a SIMD C++ genetic programming system on a single 128 node parallel nVidia GeForce 8800 GTX GPU under RapidMind’s GPGPU Linux software by predicting ten year+ outcome of breast cancer from a dataset containing a million inputs. NCBI GEO GSE3494 contains hundreds of Affymetrix HG-U133A and HG-U133B GeneChip biopsies. Multiple GP runs each […]

A GPU is used to datamine five million correlations between probes within Affymetrix HG-U133A probesets across 6685 human tissue samples from NCBIpsilas GEO database. These concordances are used as machine learning training data for genetic programming running on a Linux PC with a RapidMind OpenGL GLSL backend. GPGPU is used to identify technological factors influencing […]