high performance computing on graphics processing units: hgpu.org

hgpu.org » CUBLAS

A Mixed-Precision Algorithm for the Solution of Lyapunov Equations on Hybrid CPU-GPU Platforms

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

View

Download (PDF)

Tags: CUBLAS, CUDA, Mathematics, Mixed precision, nVidia, Tesla C1060

January 16, 2011 by hgpu

Neville elimination on multi- and many-core systems: OpenMP, MPI and CUDA

P. Alonso, R. Cortina, F. Martinez-Zaldivar, J. Ranilla

Tags: Computer science, CUBLAS, CUDA, MPI, nVidia, OpenMP

January 10, 2011 by hgpu

Evaluation and tuning of the Level 3 CUBLAS for graphics processors

S. Barrachina, M. Castillo, F. D. Igual, R. Mayo, E. S. Quintana-Orti

View

Download (PDF)

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

January 9, 2011 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

A GPGPU compiler for memory optimization and parallelism management

Yi Yang, Ping Xiang, Jingfei Kong, Huiyang Zhou

View

Download (PDF)

Source codes

Tags: Computer science, CUBLAS, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Package

November 18, 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

Parallel GPU Implementation of Iterative PCA Algorithms

M. Andrecut

View

Download (PDF)

Tags: Algorithms, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280

November 13, 2010 by hgpu

GPU-based Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

Zhen Tian, Xun Jia, Kehong Yuan, Tinsu Pan, Steve B. Jiang

View

Download (PDF)

Tags: Computed tomography, CT, CUBLAS, CUDA, Image reconstruction, Medical Physics, Medicine, nVidia, Physics, Tesla C1060

November 9, 2010 by hgpu

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography

Bo Zhang, Xiang Yang, Fei Yang, Xin Yang, Chenghu Qin, Dong Han, Xibo Ma, Kai Liu, Jie Tian

View

Download (PDF)

Tags: CUBLAS, CUDA, CULA, Image processing, Image reconstruction, Medicine, nVidia GeForce GT 240

November 8, 2010 by hgpu

Accelerating Resolution-of-the-Identity Second-Order Moller-Plesset Quantum Chemistry Calculations with Graphical Processing Units

Leslie Vogt, Roberto Olivares-Amaya, Sean Kermes, Yihan Shao, Carlos Amador-Bedolla, Alan Aspuru-Guzik

Tags: Chemistry, Computational chemistry, CUBLAS, CUDA, Molecular modeling, nVidia, nVidia Quadro FX 5600

November 2, 2010 by hgpu

Graphics Processing Units and High-Dimensional Optimization

Hua Zhou, Kenneth Lange, Marc A. Suchard

View

Download (PDF)

Tags: Block relaxation, CUBLAS, CUDA, EM and MM algorithms, Mathematics, Multidimensional scaling, Nonnegative matrix factorization, nVidia, nVidia GeForce GTX 280, PET scanning, Statistics

October 30, 2010 by hgpu

Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

Nguyen-Khang Pham, Annie Morin, Patrick Gros

Tags: CUBLAS, CUDA, Factorial correspondence analysis, Image processing, nVidia, SIFT

October 29, 2010 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A Mixed-Precision Algorithm for the Solution of Lyapunov Equations on Hybrid CPU-GPU Platforms

Neville elimination on multi- and many-core systems: OpenMP, MPI and CUDA

Evaluation and tuning of the Level 3 CUBLAS for graphics processors

BLAS Comparison on FPGA, CPU and GPU

A GPGPU compiler for memory optimization and parallelism management

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

Parallel GPU Implementation of Iterative PCA Algorithms

GPU-based Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography

Accelerating Resolution-of-the-Identity Second-Order Moller-Plesset Quantum Chemistry Calculations with Graphical Processing Units

Graphics Processing Units and High-Dimensional Optimization

Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

LLM.Q: Quantized LLM training in pure CUDA/C++

Most viewed papers (last 30 days)