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Improving Cache Locality for GPU-based Volume Rendering

Y. Sugimoto, F. Ino, K. Hagihara

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Tags: CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Performance, Rendering

March 28, 2014 by hgpu

A pattern recognition system for prostate mass spectra discrimination based on the CUDA parallel programming model

S. Kostopoulos, D. Glotsos, K. Sidiropoulos, P. Asvestas, D. Cavouras, I. Kalatzis

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Tags: CUDA, Medicine, nVidia, nVidia GeForce GTX 580, Pattern recognition, Signal processing

March 20, 2014 by hgpu

An optimized algorithm for discrete element system analysis using CUDA

Zhaosong Ma, Chun Feng, Tianping Liu, Shihai Li

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Tags: Algorithms, Computer science, CUDA, Finite element method, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 460, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Tesla C2070

March 17, 2014 by hgpu

Multi-GPU cluster wave propagation and OpenGL visualization

Jacob Lundgren, Nils Olofsson

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Tags: Computer science, CUDA, Finite difference, GPU cluster, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 670, OpenGL, Visualization

March 15, 2014 by hgpu

Fast Exact Hyper-Graph Matching with Dynamic Programming for Spatio-Temporal Data

Oya Celiktutan, Christian Wolf, Bulent Sankur, Eric Lombardi

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Tags: Algorithms, Computer science, Computer vision, CUDA, Graph theory, nVidia, nVidia GeForce GTS 450, nVidia GeForce GTX 580, Sorting

March 14, 2014 by hgpu

Genetically Improved CUDA kernels for StereoCamera

W. B. Langdon, M. Harman

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Source codes

Tags: Computer science, CUDA, Evolutionary Computations, Genetic programming, nVidia, nVidia GeForce GTX 580, Package, Tesla C2050, Tesla K20, Tesla T10

March 12, 2014 by hgpu

Massively parallel read mapping on GPUs with PEANUT

Johannes Koster, Sven Rahmann

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Source codes

Tags: Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780, OpenCL, Package, Python

March 10, 2014 by hgpu

Parallelization of DNA alignment algorithms using GPUs

Gustavo Encarnacao

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 580, Sequence matching, Suffix trees

March 7, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors

Adam Gudys, Sebastian Deorowicz

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Source codes

Tags: ATI, ATI Radeon HD 7970, Bioinformatics, Biology, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Package, Sequence alignment

March 4, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

Exploitation of GPUs for the Parallelisation of Probably Parallel Legacy Code

Zheng Wang, Daniel Powell, Bjorn Franke, Michael O'Boyle

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Tags: Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

February 27, 2014 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Improving Cache Locality for GPU-based Volume Rendering

A pattern recognition system for prostate mass spectra discrimination based on the CUDA parallel programming model

An optimized algorithm for discrete element system analysis using CUDA

Multi-GPU cluster wave propagation and OpenGL visualization

Fast Exact Hyper-Graph Matching with Dynamic Programming for Spatio-Temporal Data

Genetically Improved CUDA kernels for StereoCamera

Massively parallel read mapping on GPUs with PEANUT

Parallelization of DNA alignment algorithms using GPUs

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Exploitation of GPUs for the Parallelisation of Probably Parallel Legacy Code

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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