high performance computing on graphics processing units: hgpu.org

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Ubiquitous Parallel Computing from Berkeley, Illinois, and Stanford

Bryan Catanzaro, Armando Fox, Kurt Keutzer, David Patterson, Bor-Yiing Su, Marc Snir, Kunle Olukotun, Pat Hanrahan, Hassan Chafi

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Tags: Computer science, Programming techniques, Review

January 5, 2011 by hgpu

Introduction to GPGPU programming

Jay E. Steele, Robert Cochran

Tags: Computer science, High-level Languages, Programming techniques, Review

December 29, 2010 by hgpu

Novel Architectures: Solving Computational Problems with GPU Computing

Jonathan Cohen, Michael Garland

Tags: Computer science, CUDA, nVidia, Review

December 27, 2010 by hgpu

OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems

John E. Stone, David Gohara, Guochun Shi

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Tags: ATI, Computer science, nVidia, nVidia GeForce GTX 285, OpenCL, Review

December 27, 2010 by hgpu

Programming massively parallel processors : A Hands – on approach

David B Kirk and Wen-Mei W. Hwu

Tags: Algorithms, Book, Computer science, CUDA, nVidia, OpenCL, Review

December 17, 2010 by hgpu

Fast and Accurate Finite-Element Multigrid Solvers for PDE Simulations on GPU Clusters

Dominik Goddeke

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Tags: CUDA, Finite element method, Fluid dynamics, GPU cluster, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce 6800, nVidia GeForce 7600 GT, nVidia GeForce 7900 GT, nVidia GeForce 8600 GT, nVidia GeForce 8800 GTX, nVidia GeForce 9600 GT, nVidia GeForce GTX 280, OpenGL, Review, Thesis

December 16, 2010 by hgpu

OpenMP in Multicore Architectures (tech. report)

Venkatesan Packirisamy, Harish Barathvajasankar

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Tags: Computer science, OpenMP, Programming techniques, Review

December 14, 2010 by hgpu

OpenMP on Multicore Architectures

Christian Terboven, Dieter An Mey, Samuel Sarholz

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Tags: Computer science, OpenMP, Programming techniques, Review

December 14, 2010 by hgpu

Future graphics architectures

William Mark

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Tags: Computer science, Hardware, Review

December 11, 2010 by hgpu

The impact of accelerator processors for high-throughput molecular modeling and simulation

G. Giupponi, M. Harvey, G. Defabritiis

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Tags: Bioinformatics, Cell processor, Chemistry, Medicine, Molecular modeling, nVidia, Review

December 11, 2010 by hgpu

Overview of implementation of DARPA GPU program in SAIC

Dennis Braunreiter, Jeremy Furtek, Hai W. Chen, Dennis Healy

Tags: ATI, nVidia, Optics, Physics, Review

December 8, 2010 by hgpu

A survey of medical image registration on graphics hardware

O. Fluck, C. Vetter, W. Wein, A. Kamen, B. Preim, R. Westermann

Tags: Image processing, Image registration, Medicine, Review

December 8, 2010 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

Ubiquitous Parallel Computing from Berkeley, Illinois, and Stanford

Introduction to GPGPU programming

Novel Architectures: Solving Computational Problems with GPU Computing

OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems

Programming massively parallel processors : A Hands – on approach

Fast and Accurate Finite-Element Multigrid Solvers for PDE Simulations on GPU Clusters

OpenMP in Multicore Architectures (tech. report)

OpenMP on Multicore Architectures

Future graphics architectures

The impact of accelerator processors for high-throughput molecular modeling and simulation

Overview of implementation of DARPA GPU program in SAIC

A survey of medical image registration on graphics hardware

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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