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An error correction solver for linear systems: Evaluation of mixed precision implementations

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker

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Tags: Benchmarking, Computer science, CUDA, Mixed precision, nVidia, Tesla S1070

December 2, 2011 by hgpu

Architecture-Aware Algorithms and Software for Peta and Exascale Computing

Jack J. Dongarra

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Tags: Algorithms, Computer science, Mixed precision, MPI, Review

November 30, 2011 by hgpu

Multi GPU Performance of Conjugate Gradient Solver with Staggered Fermions in Mixed Precision

Yong-Chull Jang, Hyung-Jin Kim, Weonjong Lee

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Tags: Algorithms, Cg, Computational Physics, Conjugate gradient solver, CUDA, High Energy Physics - Lattice, Mixed precision, nVidia, nVidia GeForce GTX 480, Physics

November 2, 2011 by hgpu

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Wang Lei, Zhang Yunquan, Zhang Xianyi, Liu Fangfang

Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia GeForce GTX 295, Tesla C1060

April 20, 2011 by hgpu

Implementation of Variable Preconditioned GCR with mixed precision on GPU using CUDA

Soichiro Ikuno, Norihisa Fujita, Susumu Yamamoto, Susumu Nakata

Tags: CUDA, Electrodynamics, Mixed precision, nVidia

March 5, 2011 by hgpu

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (Part 2: Double Precision GPUs)

Dominik Goddeke and Robert Strzodka

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Tags: Computer science, Conjugate gradient solver, CUDA, Extended precision, Mixed precision, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Partial differential equations, PDEs, Sparse linear iterative solvers

March 2, 2011 by hgpu

Mixed-Precision GPU-Multigrid Solvers with Strong Smoothers

Dominik Goddeke, Robert Strzodka

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Tags: CUDA, Mathematics, Mixed precision, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs

March 2, 2011 by hgpu

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations

Dominik Goddeke, Robert Strzodka, Stefan Turek

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Tags: Cg, Computer science, Conjugate gradient solver, Mixed precision, nVidia, nVidia GeForce 7800 GTX, nVidia Quadro FX 4500, Sparse linear iterative solvers

March 1, 2011 by hgpu

Using GPUs to Improve Multigrid Solver Performance on a Cluster

Dominik Goddeke, Robert Strzodka, Jamaludin Mohd-Yusof, Patrick McCormick, Hilmar Wobker, Christian Becker, Stefan Turek

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Tags: Cg, Computer science, Finite element method, Mixed precision, nVidia, nVidia Quadro FX 4500, OpenGL

March 1, 2011 by hgpu

Accelerating the ANSYS Direct Sparse Solver with GPUs

Geraud P. Krawezik, Gene Poole

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Tags: Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, Sparse direct solvers, Tesla C1060

February 22, 2011 by hgpu

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms

Hartwig Anzt, Bjorn Rocker, Vincent Heuveline

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Tags: Computer science, Energy-efficient computing, Linear Algebra, Mixed precision, nVidia, Tesla C1060, Tesla S1070

February 22, 2011 by hgpu

Using GPU VSIPL & CUDA to Accelerate RF Clutter Simulation

Dan Campbell, Mark McCans, Mike Davis, Mike Brinkmann

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Tags: Computer science, CUDA, Mixed precision, nVidia, nVidia GeForce 9800 GX2, Signal processing

February 19, 2011 by hgpu

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

An error correction solver for linear systems: Evaluation of mixed precision implementations

Architecture-Aware Algorithms and Software for Peta and Exascale Computing

Multi GPU Performance of Conjugate Gradient Solver with Staggered Fermions in Mixed Precision

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Implementation of Variable Preconditioned GCR with mixed precision on GPU using CUDA

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (Part 2: Double Precision GPUs)

Mixed-Precision GPU-Multigrid Solvers with Strong Smoothers

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations

Using GPUs to Improve Multigrid Solver Performance on a Cluster

Accelerating the ANSYS Direct Sparse Solver with GPUs

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms

Using GPU VSIPL & CUDA to Accelerate RF Clutter Simulation

Recent source codes

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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