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Speeding up the evaluation phase of GP classification algorithms on GPUs

Alberto Cano, Amelia Zafra, Sebastian Ventura

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package

December 8, 2011 by hgpu

CUDA and OpenCL-based asynchronous PSO

Youssef S. G. Nashed, Alessandro Bacchini, Stefano Cagnoni, Luca Mussi

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Tags: Computer science, CUDA, Genetic programming, nVidia, OpenCL

November 16, 2011 by hgpu

Variable selection in a GPU cluster using delta test

A. Guillen, M. van Heeswijk, D. Sovilj, M. G. Arenas, L. J. Herrera, H. Pomares, I. Rojas

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Tags: Algorithms, Computer science, Genetic programming, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce 8400 GS, nVidia GeForce 9800 GTX, nVidia GeForce GTS 450, Optimization

November 15, 2011 by hgpu

A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA

Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham

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Tags: Algorithms, Computer science, CUDA, Differential evolution, Genetic programming, Metaheuristics, nVidia, Task scheduling, Tesla C2050

November 2, 2011 by hgpu

Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems

Long Zheng, Yanchao Lu, Mengwei Ding, Yao Shen, Minyi Guo, Song Guo

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 580, Optimization

October 28, 2011 by hgpu

Many-threaded implementation of differential evolution for the CUDA platform

Pavel Kromer, Vaclav Snasel, Jan Platos, Ajith Abraham

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Differential evolution, Genetic programming, nVidia, Optimization, Tesla C2050

September 29, 2011 by hgpu

Evolving CUDA PTX programs by quantum inspired linear genetic programming

Leandro F. Cupertino, Cleomar P. Silva, Douglas M. Dias, Marco Aurelio C. Pacheco, Cristiana Bentes

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Tags: Computer science, CUDA, Databases, Genetic programming, nVidia, Performance, PTX, Tesla C1060

September 29, 2011 by hgpu

Acceleration of genetic algorithms for sudoku solution on many-core processors

Yuji Sato, Naohiro Hasegawa, Mikiko Sato

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Tags: Algorithms, Computer science, CUDA, Games, Genetic programming, nVidia, nVidia GeForce GTX 460, Presentation

September 20, 2011 by hgpu

Generalisation in genetic programming

William B. Langdon

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Tags: Artificial intelligence, Computer science, CUDA, Genetic programming, nVidia, Package

September 19, 2011 by hgpu

Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET

Simon Harding, Wolfgang Banzhaf

Tags: Algorithms, Artificial intelligence, Computer science, Genetic programming, Programming techniques

September 12, 2011 by hgpu

GPU-based asynchronous particle swarm optimization

Luca Mussi, Youssef S.G. Nashed, Stefano Cagnoni

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, Optimization, Performance

September 7, 2011 by hgpu

Visual Human – Machine Learning

Raphael Fuchs, Jurgen Waser, Meister Eduard Groller

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Tags: Algorithms, Computer science, CUDA, Genetic programming, Machine learning, nVidia, Pattern recognition, Rendering, Visualization

September 4, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Speeding up the evaluation phase of GP classification algorithms on GPUs

CUDA and OpenCL-based asynchronous PSO

Variable selection in a GPU cluster using delta test

A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA

Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems

Many-threaded implementation of differential evolution for the CUDA platform

Evolving CUDA PTX programs by quantum inspired linear genetic programming

Acceleration of genetic algorithms for sudoku solution on many-core processors

Generalisation in genetic programming

Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET

GPU-based asynchronous particle swarm optimization

Visual Human – Machine Learning

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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