high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

A fast GEMM implementation on the cypress GPU

Naohito Nakasato

View

Download (PDF)

Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5870, Computer science, Linear Algebra, Matrix multiplication, Performance

August 22, 2011 by hgpu

Mind the gap!: bridging the dichotomy of design and implementation

Donna Kaminskyj Long, Liam Kiemele, Celina Gibbs, Andrew Brownsword, Yvonne Coady

View

Download (PDF)

Tags: Computer science, Linear Algebra, OpenCL, Performance, Presentation, Programming techniques

August 21, 2011 by hgpu

Matrix Multiplication on GPUs with On-Line Fault Tolerance

Chong Ding, Christer Karlsson, Hui Liu, Teresa Davies, Zizhong Chen

Tags: Algorithms, Computer science, Linear Algebra, Matrix multiplication

August 9, 2011 by hgpu

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

View

Download (PDF)

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

View

Download (PDF)

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

View

Download (PDF)

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

View

Download (PDF)

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

View

Download (PDF)

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

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Automating Heterogeneous Parallelism in Numerical Differential Equations

Dat3M: Memory Model Aware Verification

Towards Unified Analysis of GPU Consistency

NVIDIA Federated Learning Application Runtime Environment

Supercharging Federated Learning with Flower and NVIDIA FLARE

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

General-purpose Polyhedral Compilers

A Survey of General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A fast GEMM implementation on the cypress GPU

Mind the gap!: bridging the dichotomy of design and implementation

Matrix Multiplication on GPUs with On-Line Fault Tolerance

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

Recent source codes

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Dat3M: Memory Model Aware Verification

NVIDIA Federated Learning Application Runtime Environment

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Most viewed papers (last 30 days)