high performance computing on graphics processing units: hgpu.org

hgpu.org » BLAS

Using Hybrid CPU-GPU Platforms to Accelerate the Computation of the Matrix Sign Function

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

View

Download (PDF)

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

December 25, 2010 by hgpu

BLAS Comparison on FPGA, CPU and GPU

Srinidhi Kestur, John D. Davis, Oliver Williams

View

Download (PDF)

Tags: BLAS, Computer science, CUBLAS, CUDA, Energy-efficient computing, FPGA, Linear Algebra, nVidia, nVidia GeForce 9500 GT, Performance, Tesla C1060

December 6, 2010 by hgpu

Accelerating the reduction to upper Hessenberg, tridiagonal, and bidiagonal forms through hybrid GPU-based computing

Stanimire Tomov, Rajib Nath, Jack Dongarra

View

Download (PDF)

Source codes

Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package

November 17, 2010 by hgpu

Benchmarking GPUs to tune dense linear algebra

Vasily Volkov, James W. Demmel

View

Download (PDF)

Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GTX, nVidia GeForce GTX 280

November 4, 2010 by hgpu

Solving Dense Linear Systems on Graphics Processors

Sergio Barrachina, Maribel Castillo, Francisco D. Igual, Rafael Mayo, Enrique S. Quintana Ort'?

View

Download (PDF)

Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 8800 Ultra

November 4, 2010 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

Using Hybrid CPU-GPU Platforms to Accelerate the Computation of the Matrix Sign Function

BLAS Comparison on FPGA, CPU and GPU

Accelerating the reduction to upper Hessenberg, tridiagonal, and bidiagonal forms through hybrid GPU-based computing

Benchmarking GPUs to tune dense linear algebra

Solving Dense Linear Systems on Graphics Processors

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)