high performance computing on graphics processing units: hgpu.org

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Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Alexander Monakov, Anton Lokhmotov, Arutyun Avetisyan

Tags: Computer science, Partial differential equations, PDEs, Sparse matrix

April 21, 2011 by hgpu

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 9500 GTX, nVidia GeForce GTX 295, Sparse matrix

April 21, 2011 by hgpu

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Noboru Tanabe, Yuuka Ogawa, Masami Takata, Kazuki Joe

Tags: Computer science, Heterogeneous systems, Matrix multiplication, Sparse matrix

April 14, 2011 by hgpu

Efficient PageRank and SpMV Computation on AMD GPUs

Tianji Wu, Bo Wang, Yi Shan, Feng Yan, Yu Wang, Ningyi Xu

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5870, ATI Stream, Computer science, List ranking, OpenCL, Sparse matrix

April 1, 2011 by hgpu

Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

A El Zein, E McCreath, Alistair Rendell, Alex Smola

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce 8800 GTX, Presentation, Sparse matrix

March 20, 2011 by hgpu

FPGA vs. GPU for sparse matrix vector multiply

Yan Zhang, Yasser H. Shalabi, Rishabh Jain, Krishna K. Nagar, Jason D. Bakos

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Tags: Computer science, CUDA, FPGA, Linear Algebra, nVidia, nVidia GeForce GTX 260, Sparse matrix

March 18, 2011 by hgpu

Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining

Xintian Yang, Srinivasan Parthasarathy, Ponnuswamy Sadayappan

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Tags: Computer science, CUDA, Databases, nVidia, Sparse matrix, Tesla C1060

March 15, 2011 by hgpu

Study on GPU-accelerated extraction of interconnects parasitic using CUDA and MPI

Xiaoyu Xu, Guoqiang Liu, Hui Qu, Wei Xu, Yang Zhang

Tags: Computer science, CUDA, GPU cluster, MPI, nVidia, Sparse matrix

March 6, 2011 by hgpu

The sparse matrix vector product on GPUs

F. Vazquez, E. M. Garzon , J. A. Martinez, J. J. Fernandez

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Sparse matrix

March 3, 2011 by hgpu

Sparse systems solving on GPUs with GMRES

Raphael Couturier, Stephane Domas

Tags: Computer science, CUBLAS, CUDA, nVidia, Sparse linear iterative solvers, Sparse matrix

February 18, 2011 by hgpu

Accelerating Algorithms on GPUs in SCIRun: the Conjugate Gradient Case Study

Devon Yablonski, Miriam Leeser, Dana Brooks

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Source codes

Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Package, Sparse matrix

February 17, 2011 by hgpu

Model-driven autotuning of sparse matrix-vector multiply on GPUs

Jee W. Choi, Amik Singh, Richard W. Vuduc

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla C1060, Tesla C870, Tesla T10P

February 5, 2011 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

Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

FPGA vs. GPU for sparse matrix vector multiply

Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining

Study on GPU-accelerated extraction of interconnects parasitic using CUDA and MPI

The sparse matrix vector product on GPUs

Sparse systems solving on GPUs with GMRES

Accelerating Algorithms on GPUs in SCIRun: the Conjugate Gradient Case Study

Model-driven autotuning of sparse matrix-vector multiply on GPUs

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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