high performance computing on graphics processing units: hgpu.org

hgpu.org » Factorization

Performance Portability of a GPU Enabled Factorization with the DAGuE Framework

George Bosilca, Aurelien Bouteiller, Thomas Herault, Pierre Lemarinier, Narapat Ohm Saengpatsa, Stanimire Tomov, Jack J. Dongarra

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Tags: Computer science, Factorization, GPU cluster, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla C1060, Tesla C2050, Tesla S1070

November 3, 2011 by hgpu

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

Li Luo, Chao Yang, Yubo Zhao, Xiao-Chuan Cai

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FFT, Finite difference, GPU cluster, nVidia, Partial differential equations, PDEs, Tesla C1060

October 30, 2011 by hgpu

A High Performance Parallel Sparse Linear Equation Solver Using CUDA

Andrew J. Martin

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia GeForce GTX 570, Thesis

October 17, 2011 by hgpu

A GPU-Accelerated Parallel Preconditioner for the Solution of the Boltzmann Transport Equation for Semiconductors

Karl Rupp, Ansgar Jungel, Tibor Grasser

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Tags: Boltzmann equation, Computer science, Differential equations, Factorization, nVidia, nVidia GeForce GTX 580, OpenCL, Partial differential equations, PDEs

October 10, 2011 by hgpu

Multifrontal computations on GPUs and their multi-core hosts

Robert F. Lucas, Gene Wagenbreth, Dan M. Davis, Roger Grimes

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Tags: Computer science, CUDA, Factorization, nVidia, Performance, Sparse matrix, Tesla C1060

October 7, 2011 by hgpu

Investigation on the Use of GPGPU for Fast Sparse Matrix Factorization

Ye Tian, Bin Zhou, Yutong Zhang, Ka Wing Chan

Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, Sparse matrix

October 7, 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

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

August 4, 2011 by hgpu

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

Emmanuel Agullo, Cedric Augonnet, Jack Dongarra, Mathieu Faverge, Hatem Ltaief, Samuel Thibault, Stanimire Tomov

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Tags: Computer science, CUDA, Factorization, GPU cluster, Linear Algebra, nVidia, nVidia Quadro FX 5800, Tesla S1070

June 5, 2011 by hgpu

A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators

Hatem Ltaief, Stanimire Tomov, Rajib Nath, Peng Du and Jack Dongarra

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla S1070

June 4, 2011 by hgpu

Design Space Exploration for GPU-Based Architecture

Zhenyu Ye

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Tags: Computer science, CUDA, Factorization, Hardware Architecture, Memory model, nVidia, nVidia GeForce 8800 GTX, Review, Thesis

February 20, 2011 by hgpu

Takagi Factorization on GPU using CUDA

Gagandeep S. Sachdev, Vishay Vanjani and Mary W. Hall

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Tags: Computer science, CUDA, Factorization, Matrix decomposition, nVidia, nVidia GeForce GTX 260

February 17, 2011 by hgpu

Scaling LAPACK panel operations using parallel cache assignment

Anthony M. Castaldo, R. Clint Whaley

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Tags: Algorithms, Computer science, Factorization, Linear Algebra, Mathematical Software, Performance

February 10, 2011 by hgpu

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

MaxKernel: Intelligent TPU Kernel Engineering Agents

MaxKernel: Agentic Kernel Generation for TPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Performance Portability of a GPU Enabled Factorization with the DAGuE Framework

A scalable hybrid algorithm based on domain decomposition and algebraic multigrid for solving partial differential equations on a cluster of CPU/GPUs

A High Performance Parallel Sparse Linear Equation Solver Using CUDA

A GPU-Accelerated Parallel Preconditioner for the Solution of the Boltzmann Transport Equation for Semiconductors

Multifrontal computations on GPUs and their multi-core hosts

Investigation on the Use of GPGPU for Fast Sparse Matrix Factorization

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

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators

Design Space Exploration for GPU-Based Architecture

Takagi Factorization on GPU using CUDA

Scaling LAPACK panel operations using parallel cache assignment

Recent source codes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

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