high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

Using Graphics Processors to Accelerate the Solution of Out-of-Core Linear Systems

Mercedes Marques, Gregorio Quintana-Orti, Enrique S. Quintana-Orti, Robert van de Geijn

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Tags: Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, nVidia GeForce 9800 GX2

August 4, 2011 by hgpu

Linpack evaluation on a supercomputer with heterogeneous accelerators

Toshio Endo, Akira Nukada, Satoshi Matsuoka, Naoya Maruyama

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla S1070

July 17, 2011 by hgpu

Testing Tesla architecture for scientific computing: The performance of matrix-vector product

Pawel Maciol, Krzysztof Banas

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce 8800 GTX, Performance, Tesla C870

July 10, 2011 by hgpu

Numerical Parallel Processing Based on GPU with CUDA Architecture

Chengming Zou, Chunfen Xia, Guanghui Zhao

Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia

July 9, 2011 by hgpu

Acceleration of Streamed Tensor Contraction Expressions on GPGPU-Based Clusters

Wenjing Ma, Sriram Krishnamoorthy, Oreste Villa, Karol Kowalski

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Tags: Computational chemistry, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Presentation, Tesla C1070, Tesla S1070

July 7, 2011 by hgpu

High Performance Matrix Inversion on a Multi-core Platform with Several GPUs

Pablo Ezzatti, Enrique S. Quintana-Orti, Alfredo Remon

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, Presentation, Tesla C1060

July 5, 2011 by hgpu

Implementing Sparse Matrix-Vector multiplication using CUDA based on a hybrid sparse matrix format

Wei Cao, Lu Yao, Zongzhe Li, Yongxian Wang, Zhenghua Wang

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

June 20, 2011 by hgpu

Auto-tuning Dense Matrix Multiplication for GPGPU with Cache

Xiang Cui, Yifeng Chen, Changyou Zhang, Hong Mei

Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla C2050

June 19, 2011 by hgpu

CUDA-Based Jacobi’s Iterative Method

Zhihui Zhang, Qinghai Miao, Ying Wang

Tags: Computer science, CUDA, Linear Algebra, nVidia

June 18, 2011 by hgpu

FPGA Based High Performance and Scalable Block LU Decomposition Architecture

Manish Kumar Jaiswal, Nitin Chandrachoodan

Tags: Computer science, FPGA, Linear Algebra, Matrix decomposition

June 14, 2011 by hgpu

A multi-platform linear algebra toolbox for finite element solvers on heterogeneous clusters

V. Heuveline, C. Subramanian, D. Lukarski, J.-P. Weiss

Tags: Finite element method, Heterogeneous systems, Linear Algebra, Mathematics, Partial differential equations, PDEs

June 14, 2011 by hgpu

CUDA Based Fast Implementation of Very Large Matrix Computation

Yinghong Sun, Yuanman Tong

Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia

June 10, 2011 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

See all packages

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high performance computing on graphics processing units: hgpu.org

Using Graphics Processors to Accelerate the Solution of Out-of-Core Linear Systems

Linpack evaluation on a supercomputer with heterogeneous accelerators

Testing Tesla architecture for scientific computing: The performance of matrix-vector product

Numerical Parallel Processing Based on GPU with CUDA Architecture

Acceleration of Streamed Tensor Contraction Expressions on GPGPU-Based Clusters

High Performance Matrix Inversion on a Multi-core Platform with Several GPUs

Implementing Sparse Matrix-Vector multiplication using CUDA based on a hybrid sparse matrix format

Auto-tuning Dense Matrix Multiplication for GPGPU with Cache

CUDA-Based Jacobi’s Iterative Method

FPGA Based High Performance and Scalable Block LU Decomposition Architecture

A multi-platform linear algebra toolbox for finite element solvers on heterogeneous clusters

CUDA Based Fast Implementation of Very Large Matrix Computation

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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