high performance computing on graphics processing units: hgpu.org

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Accelerating Statistical Static Timing Analysis Using Graphics Processing Units

Kanupriya Gulati, Sunil P. Khatri

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Tags: Computer science, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce 8800 GTX

March 2, 2011 by hgpu

ECC2K-130 on NVIDIA GPUs

Daniel J. Bernstein, Hsieh-Chung Chen, Chen-Mou Cheng, Tanja Lange, Ruben Niederhagen, Peter Schwabe, Bo-Yin Yang

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Tags: Computer science, CUDA, Elliptic-curve method of factorization, nVidia, nVidia GeForce GTX 295, Security

March 2, 2011 by hgpu

Application of Assembly of Finite Element Methods on Graphics Processors for Real-Time Elastodynamics

Cris Cecka, Adrian Lew, Eric Darve

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Tags: CUDA, Finite element method, Mathematics, nVidia, nVidia GeForce GTX 480, Partial differential equations, PDEs

March 2, 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

Accelerating Double Precision FEM Simulations with GPUs

Dominik Goddeke, Robert Strzodka, Stefan Turek

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Tags: Computer science, Extended precision, Finite element method, nVidia, nVidia GeForce 6800, OpenGL, Partial differential equations, PDEs

March 2, 2011 by hgpu

Integrating GPUs as fast co-processors into the existing parallel FE package FEAST

Dominik Goddeke, Christian Becker, Stefan Turek

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Tags: Finite element method, Fluid dynamics, nVidia, nVidia Quadro FX 1400, Package, Partial differential equations, PDEs

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

Hardware-Oriented Multigrid Finite Element Solvers on GPU-Accelerated Clusters

Stefan Turek, Dominik Goddeke, Sven H.M. Buijssen, Hilmar Wobker

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Tags: CUDA, Finite element method, Fluid dynamics, nVidia, nVidia GeForce GTX 280, Package

March 2, 2011 by hgpu

Finite Element Integration on GPUs

Matthew G. Knepley, Andy R. Terrel

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Tags: CUDA, Finite element method, Mathematical Software, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX 285

March 2, 2011 by hgpu

UCHPC – UnConventional High Performance Computing for Finite Element Simulations

Stefan Turek, Dominik Goddeke, Christian Becker, Sven H.M. Buijssen, Hilmar Wobker

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Tags: Finite element method, Heterogeneous systems, MPI, nVidia, nVidia Quadro FX 5600, Physics

March 1, 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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

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

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high performance computing on graphics processing units: hgpu.org

Applications

Accelerating Statistical Static Timing Analysis Using Graphics Processing Units

ECC2K-130 on NVIDIA GPUs

Application of Assembly of Finite Element Methods on Graphics Processors for Real-Time Elastodynamics

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

Accelerating Double Precision FEM Simulations with GPUs

Integrating GPUs as fast co-processors into the existing parallel FE package FEAST

Mixed-Precision GPU-Multigrid Solvers with Strong Smoothers

Hardware-Oriented Multigrid Finite Element Solvers on GPU-Accelerated Clusters

Finite Element Integration on GPUs

UCHPC – UnConventional High Performance Computing for Finite Element Simulations

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

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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

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

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

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

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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