high performance computing on graphics processing units: hgpu.org

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Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

Matthew Moskewicz, Forrest Iandola, Kurt Keutzer

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Tags: ARM, CNN, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, Neural networks, OpenCL

June 7, 2016 by hgpu

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Bo Yang, Hui Liu, Zhangxin Chen

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Tags: Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla C2050, Tesla C2070

June 7, 2016 by hgpu

Solver for Systems of Linear Equations with Infinite Precision on a GPU Cluster

Jiri Khun

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Tags: AMD Radeon HD 8750 M, ATI, Computer science, Extended precision, GPU cluster, Linear Algebra, Modular arithmetic, MPI, OpenCL

June 2, 2016 by hgpu

clSPARSE: A Vendor-Optimized Open-Source Sparse BLAS Library

Joseph L. Greathouse, Kent Knox, Jakub Pola, Kiran Varaganti, Mayank Daga

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Tags: Algorithms, AMD Radeon R9 Fury X, ATI, Computer science, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Sparse matrix

May 30, 2016 by hgpu

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Vi Ngoc-Nha Tran, Phuong Hoai Ha

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Sparse matrix

May 28, 2016 by hgpu

On the design of sparse hybrid linear solvers for modern parallel architectures

Stojce Nakov

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, MPI, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla K40, Tesla M2070, Thesis

April 29, 2016 by hgpu

Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems

M. Abalenkovs, A. Abdelfattah, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, I. Yamazaki, A. YarKhan

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, OpenCL, Programming techniques, Review, Tesla K40

April 19, 2016 by hgpu

Efficiency of general Krylov methods on GPUs – An experimental study

Hartwig Anzt, Jack Dongarra, Moritz Kreutzer, Gerhard Wellein, Martin Kohler

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Tags: Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Package, Sparse matrix, Tesla K40

April 16, 2016 by hgpu

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Steven Eliuk, Cameron Upright, Anthony Skjellum

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, OpenMPI, Tesla K80

April 9, 2016 by hgpu

Dynamic Sparse-Matrix Allocation on GPUs

James King, Thomas Gilray, Robert M. Kirby, Matthew Might

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 3, 2016 by hgpu

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

Guixia He, Jiaquan Gao

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050

March 29, 2016 by hgpu

GPU performance analysis of a nodal discontinuous Galerkin method for acoustic and elastic models

Axel Modave, Amik St-Cyr, Tim Warburton

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Tags: BLAS, Computational Physics, CUBLAS, CUDA, FEM, Finite element method, Geoscience, Linear Algebra, nVidia, nVidia GeForce GTX 980, OCCA, Physics, Profiling, Seismic modeling, Seismology

March 1, 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

Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Solver for Systems of Linear Equations with Infinite Precision on a GPU Cluster

clSPARSE: A Vendor-Optimized Open-Source Sparse BLAS Library

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

On the design of sparse hybrid linear solvers for modern parallel architectures

Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems

Efficiency of general Krylov methods on GPUs – An experimental study

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Dynamic Sparse-Matrix Allocation on GPUs

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

GPU performance analysis of a nodal discontinuous Galerkin method for acoustic and elastic models

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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