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Efficient Sparse Matrix-Vector Multiplication on GPUs using the CSR Storage Format

Joseph L. Greathouse, Mayank Daga

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Tags: Algorithms, AMD FirePro W9100, ATI, Computer science, Linear Algebra, OpenCL, Sparse matrix

November 4, 2015 by hgpu

Structural Agnostic SpMV: Adapting CSR-Adaptive for Irregular Matrices

Mayank Daga, Joseph L. Greathouse

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Tags: Algorithms, AMD FirePro W9100, APU, ATI, Computer science, Linear Algebra, OpenCL, Package, Sparse matrix

November 3, 2015 by hgpu

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Linchuan Chen

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, Matrix multiplication, MPI, nVidia, Sparse matrix, Task scheduling, Tesla M2070, Thesis

October 13, 2015 by hgpu

Three storage formats for sparse matrices on GPGPUs

Davide Barbieri, Valeria Cardellini, Alessandro Fanfarillo, Salvatore Filippone

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 660, Package, Sparse matrix, Tesla M2070

September 9, 2015 by hgpu

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla K20

July 30, 2015 by hgpu

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Naveen Anand Subramaniam, Omkar Deshmukh, Vennila Megavannan, Dan Negrut

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20, Tesla K40

June 19, 2015 by hgpu

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla K40

June 16, 2015 by hgpu

Type-safe Runtime Code Generation: Accelerate to LLVM

Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, Ryan R. Newton

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Sparse matrix, Tesla K40

June 14, 2015 by hgpu

Automatic Selection of Sparse Matrix Representation on GPUs

Naser Sedaghati, Te Mu, Louis-Noel Pouchet, Srinivasan Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, Physics, Sparse matrix, Tesla K20, Tesla K40

June 14, 2015 by hgpu

Accelerating parameter synthesis for stochastic models

Petr Pilar

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Probability, Sparse matrix, Thesis

June 14, 2015 by hgpu

Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors

Weifeng Liu, Brian Vinter

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Tags: ATI, Computer science, CUDA, Intel HD 5500, Mathematical Software, Numerical Analysis, nVidia, nVidia Jetson TK1, OpenCL, Package, Sparse matrix

April 27, 2015 by hgpu

Implementation and performance analysis of the AXPY, DOT, and SpMV functions on Intel Xeon Phi and NVIDIA Tesla using OpenCL

E. Coronado-Barrientos, G. Indalecio, A. Garcia-Loureiro

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla S2050

April 27, 2015 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

Efficient Sparse Matrix-Vector Multiplication on GPUs using the CSR Storage Format

Structural Agnostic SpMV: Adapting CSR-Adaptive for Irregular Matrices

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Three storage formats for sparse matrices on GPGPUs

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

Type-safe Runtime Code Generation: Accelerate to LLVM

Automatic Selection of Sparse Matrix Representation on GPUs

Accelerating parameter synthesis for stochastic models

Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors

Implementation and performance analysis of the AXPY, DOT, and SpMV functions on Intel Xeon Phi and NVIDIA Tesla using OpenCL

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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