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Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

K.A. Hawick and D.P. Playne

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Tags: Code generation, Compilers, Computer science, CUDA, Finite difference, nVidia, Partial differential equations, PDEs

February 14, 2011 by hgpu

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

K.A. Hawick, A. Leist, and D.P. Playne

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Tags: Cell processor, Computer science, CUDA, Data parallelism, Finite difference, nVidia, nVidia GeForce GTX 295, OpenCL, Partial differential equations, PDEs, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

Hypercubic Storage Layout and Transforms in Arbitrary Dimensions using GPUs and CUDA

K.A. Hawick and D.P. Playne

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 295, Storage system

February 14, 2011 by hgpu

Comparing Intra- and Inter-Processor Parallelism on Multi-Core Cell Processors for Scientific Simulations

K.A. Hawick, A. Leist and D.P. Playne and M.J. Johnson

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Tags: Cell processor, Computer science, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

Biomolecular electrostatics using a fast multipole BEM on up to 512 GPUs and a billion unknowns

Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, L. A. Barba, Tsuyoshi Hamada

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Tags: Chemical Physics, Computational Physics, Computer science, CUDA, Electrostatics, Fast multipole method, Integral equations, nVidia, nVidia GeForce GTX 295, Package

February 14, 2011 by hgpu

Speed and Portability issues for Random Number Generation on Graphical Processing Units with CUDA and other Processing Accelerators

K.A. Hawick, A. Leist, D.P. Playne, M.J. Johnson

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Tags: Cell processor, Computer science, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Pseudo-random number generators

February 13, 2011 by hgpu

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

K.A. Hawick and D.P. Playne

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Tags: Code generation, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 295, Partial differential equations, PDEs

February 13, 2011 by hgpu

Data-Parallelism and GPUs for Lattice Gas Fluid Simulations

M.G.B. Johnson, D.P. Playne and K.A. Hawick

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Tags: Cellular automata, Computer science, CUDA, Fluid simulation, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 210, nVidia GeForce GTX 260, nVidia GeForce GTX 295

February 13, 2011 by hgpu

GPU-based Multi-Volume Rendering of Complex Data in Neuroscience and Neurosurgery

Johanna Beyer

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Tags: Computed tomography, Computer science, CT, CUDA, Medicine, Neuroscience, nVidia, nVidia GeForce GTX 280, Rendering, Visualization

February 13, 2011 by hgpu

Comparison of GPU Architectures for Asynchronous Communication with Finite-Differencing Applications

D.P. Playne, K.A. Hawick

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Tags: Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, Partial differential equations, PDEs

February 13, 2011 by hgpu

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

D. P. Playne, K. A. Hawick

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Tags: Code generation, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX 480

February 13, 2011 by hgpu

Copperhead: Compiling an embedded data parallel language

Bryan Catanzaro, Michael Garland and Kurt Keutzer

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Tags: Code generation, Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 480, Package

February 13, 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

Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

Hypercubic Storage Layout and Transforms in Arbitrary Dimensions using GPUs and CUDA

Comparing Intra- and Inter-Processor Parallelism on Multi-Core Cell Processors for Scientific Simulations

Biomolecular electrostatics using a fast multipole BEM on up to 512 GPUs and a billion unknowns

Speed and Portability issues for Random Number Generation on Graphical Processing Units with CUDA and other Processing Accelerators

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

Data-Parallelism and GPUs for Lattice Gas Fluid Simulations

GPU-based Multi-Volume Rendering of Complex Data in Neuroscience and Neurosurgery

Comparison of GPU Architectures for Asynchronous Communication with Finite-Differencing Applications

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

Copperhead: Compiling an embedded data parallel language

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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