high performance computing on graphics processing units: hgpu.org

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A Non-linear GPU Thread Map for Triangular Domains

Cristobal A. Navarro, Benjamin Bustos, Nancy Hitschfeld

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 765 M, Performance, Tesla K40

September 8, 2016 by hgpu

OpenCL/CUDA algorithms for parallel decoding of any irregular LDPC code using GPU

Jan Broulim, Alexander Ayriyan, Vjaceslav Georgiev

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Tags: Algorithms, Computer science, CUDA, Error recovery, nVidia, OpenCL, Tesla K40, Tutorial

September 8, 2016 by hgpu

Parallel Tree Traversal for Nearest Neighbor Query on the GPU

Moohyeon Nam, Jinwoong Kim, Beomseok Nam

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Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Information Retrieval, Nearest neighbour, nVidia, Tesla K40

September 5, 2016 by hgpu

Implementation and Performance Analysis of Many-body Quantum Chemical Methods on the Intel Xeon Phi Coprocessor and NVIDIA GPU Accelerator

Bobo Shi

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, Intel Xeon Phi, nVidia, OpenMP, Tesla K40, Thesis

September 5, 2016 by hgpu

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

Tingxing Dong, Azzam Haidar, Piotr Luszczek, Stanimire Tomov, Ahmad Abdelfattah, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Factorization, Fluid dynamics, Linear Algebra, nVidia, Tesla K40

August 23, 2016 by hgpu

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

A Comparison of Potential Interfaces for Batched BLAS Computations

Samuel D. Relton, Pedro Valero-Lara, Mawussi Zounon

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Tags: BLAS, Computer science, CUDA, Linear Algbera, nVidia, Package, Tesla K40

August 11, 2016 by hgpu

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Glenn Volkema, Gaurav Khanna

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Tags: AMD Radeon R9 Fury X, ATI, Benchmarking, Computer science, nVidia, nVidia Tegra TX1, OpenCL, Package, Performance, Tesla K40

July 20, 2016 by hgpu

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

Hartwig Anzt, Marc Baboulin, Jack Dongarra, Yvan Fournier, Frank Hulsemann, Amal Khabou, Yushan Wang

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Tags: cfd, Conjugate gradient solver, CUDA, Fluid dynamics, nVidia, OpenCL, Tesla K40

July 18, 2016 by hgpu

OpenFace: A general-purpose face recognition library with mobile applications

Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan

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Tags: Computer science, Computer vision, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, Python, Tesla K40

July 13, 2016 by hgpu

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Materials Science, Molecular dynamics, MPI, nVidia, Package, performance portability, Tesla K20, Tesla K40

July 13, 2016 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

A Non-linear GPU Thread Map for Triangular Domains

OpenCL/CUDA algorithms for parallel decoding of any irregular LDPC code using GPU

Parallel Tree Traversal for Nearest Neighbor Query on the GPU

Implementation and Performance Analysis of Many-body Quantum Chemical Methods on the Intel Xeon Phi Coprocessor and NVIDIA GPU Accelerator

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

GPU-Acceleration of In-Memory Data Analytics

Learning Structured Sparsity in Deep Neural Networks

A Comparison of Potential Interfaces for Batched BLAS Computations

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

OpenFace: A general-purpose face recognition library with mobile applications

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

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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