high performance computing on graphics processing units: hgpu.org

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High-level GPU computing with jacket for MATLAB and C/C++

Gallagher Pryor, Brett Lucey, Sandeep Maddipatla, Chris McClanahan, John Melonakos, Vishwanath Venugopalakrishnan, Krunal Patel, Pavan Yalamanchili, James Malcolm

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Tags: Computer science, CUDA, FFT, Image processing, Linear Algebra, nVidia, nVidia GeForce 9600, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenGL, Package, PTX, Tesla C1060, Tesla C2070

September 26, 2011 by hgpu

Putting Automatic Polyhedral Compilation for GPGPU to Work

Soufiane Baghdadi, Armin Grosslinger, Albert Cohen

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, Package

September 26, 2011 by hgpu

A portable implementation of the radix sort algorithm in OpenCL

Philippe Helluy

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Tags: Algorithms, ATI, ATI Radeon HD 5850, nVidia, nVidia GeForce GTX 320 M, nVidia GeForce GTX 470, OpenCL, Package, Particle-in-cell methods, Physics, Plasma physics, Sorting

September 24, 2011 by hgpu

Embedding OpenCL in C++ for Expressive GPU Programming

Orion Lawlor

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Tags: ATI, ATI Radeon HD 6850, Benchmarking, Code generation, Computer science, GLSL, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 460 M, nVidia GeForce GTX 580, OpenCL, OpenGL, Package

September 23, 2011 by hgpu

Accelerating reaction-diffusion simulations with general-purpose graphics processing units

Matthias Vigelius, Aidan Lane, Bernd Meyer

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Package, Rendering, Stochastic simulation

September 23, 2011 by hgpu

Mint: realizing CUDA performance in 3D stencil methods with annotated C

Didem Unat, Xing Cai, Scott B. Baden

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, OpenMP, Optimization, Package, Performance, Programming Languages, Tesla C1060

September 21, 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

A GPU-tailored approach for training kernelized SVMs

Andrew Cotter, Nathan Srebro, Joseph Keshet

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Tags: Algorithms, Clustering, Computer science, nVidia, Optimization, Package, Performance, Programming techniques, Tesla C1060

September 19, 2011 by hgpu

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Florian Worsdorfer, Florian Stock, Eduardo Bayro-Corrochano, Dietmar Hildenbrand

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Optimization, Package, Performance

September 16, 2011 by hgpu

Scaling Lattice QCD beyond 100 GPUs

R. Babich, M. A. Clark, B. Joo, G. Shi, R. C. Brower, S. Gottlieb

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Tags: Computational Physics, CUDA, GPU cluster, High Energy Physics - Lattice, nVidia, OpenMPI, Package, Physics, QCD, Tesla M2050

September 15, 2011 by hgpu

Scalable and deterministic timing-driven parallel placement for FPGAs

Chris C. Wang, Guy G.F. Lemieux

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Tags: Algorithms, Computer science, FPGA, Package, Programming techniques, Timing analysis

September 15, 2011 by hgpu

Nikola: embedding compiled GPU functions in Haskell

Geoffrey Mainland, Greg Morrisett

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Programming Languages, Tesla T10

September 14, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

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high performance computing on graphics processing units: hgpu.org

High-level GPU computing with jacket for MATLAB and C/C++

Putting Automatic Polyhedral Compilation for GPGPU to Work

A portable implementation of the radix sort algorithm in OpenCL

Embedding OpenCL in C++ for Expressive GPU Programming

Accelerating reaction-diffusion simulations with general-purpose graphics processing units

Mint: realizing CUDA performance in 3D stencil methods with annotated C

Generalisation in genetic programming

A GPU-tailored approach for training kernelized SVMs

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Scaling Lattice QCD beyond 100 GPUs

Scalable and deterministic timing-driven parallel placement for FPGAs

Nikola: embedding compiled GPU functions in Haskell

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

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