CUBLAS | Page 6 | hgpu.org

hgpu.org » CUBLAS

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

Beata Bylina, Jaroslaw Bylina

View

Download (PDF)

Tags: Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla C2050

September 3, 2012 by hgpu

A Unified Optimizing Compiler Framework for Different GPGPU Architectures

Yi Yang, Ping Xiang, Jingfei Kong, Mike Mantor, Huiyang Zhou

View

Download (PDF)

Source codes

Tags: ATI, ATI Radeon HD 5870, Compilers, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenCL, Optimization, Package, Programming Languages

August 27, 2012 by hgpu

GPU Accelerated Nonlinear Optimization in Radio Interferometric Calibration

Sarod Yatawatta, Sanaz Kazemi, Saleem Zaroubi

View

Download (PDF)

Tags: Algorithms, Astrophysics, CUBLAS, CUDA, Linear Algebra, Nonlinear optimization, nVidia, Optimization, Tesla M1060

August 26, 2012 by hgpu

Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators

Ahmad Abdelfattah, Jack Dongarra, David Keyes, Hatem Ltaief

View

Download (PDF)

Source codes

Tags: Computer science, CUBLAS, CUDA, nVidia, Optimization, Package, Tesla C2070

August 10, 2012 by hgpu

Fast Linear Algebra on GPU

Lukas Polok, Pavel Smrz

View

Download (PDF)

Tags: Computer science, CUBLAS, Linear Algebra, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 590, OpenCL

July 28, 2012 by hgpu

Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs

A. Barberis, G. Danese, F. Leporati, A. Plaza, E. Torti

View

Download (PDF)

Tags: Algorithms, CUBLAS, CUDA, Data acquisition, Earth and Space Sciences, Geoscience, Linear Algebra, nVidia, nVidia GeForce GTX 460

July 13, 2012 by hgpu

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

Federico Raimondo, Juan Kamienkowski, Mariano Sigman, Diego Fernandez Slezak

View

Download (PDF)

Source codes

Tags: Algorithms, Biology, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neuroscience, nVidia, nVidia GeForce GTX 560, Package, Tesla C2070

June 11, 2012 by hgpu

A fight for performance and accuracy of the matrix multiplication routines: CUBLAS on Nvidia Tesla versus MKL and ATLAS on Intel Nehalem

Philippe Estival, Luc Giraud

View

Download (PDF)

Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla T10

May 29, 2012 by hgpu

An MPI-CUDA Implementation and Optimization for Parallel Sparse Equations and Least Squares (LSQR)

He Huang, Liqiang Wang, En-Jui Lee, Po Chen

View

Download (PDF)

Tags: Computer science, CUBLAS, CUDA, Earth and Space Sciences, Geoscience, MPI, nVidia, Optimization, Tesla C2050, Tesla M2070

May 19, 2012 by hgpu

Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

Franz Xaver Bayerl

View

Download (PDF)

Tags: Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 560 Ti, Performance, Thesis

April 19, 2012 by hgpu

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

Hans Henrik Brandenborg Sorensen

View

Download (PDF)

Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050

April 18, 2012 by hgpu

High-Performance Matrix-Vector Multiplication on the GPU

Hans Henrik Brandenborg Sorensen

View

Download (PDF)

Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050

April 18, 2012 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

A Unified Optimizing Compiler Framework for Different GPGPU Architectures

GPU Accelerated Nonlinear Optimization in Radio Interferometric Calibration

Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators

Fast Linear Algebra on GPU

Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

A fight for performance and accuracy of the matrix multiplication routines: CUBLAS on Nvidia Tesla versus MKL and ATLAS on Intel Nehalem

An MPI-CUDA Implementation and Optimization for Parallel Sparse Equations and Least Squares (LSQR)

Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

High-Performance Matrix-Vector Multiplication on the GPU

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Most viewed papers (last 30 days)