high performance computing on graphics processing units: hgpu.org

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Customizable Memory Schemes for Data Parallel Accelerators

Chunyang Gou

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques, Thesis

December 17, 2011 by hgpu

A Comparison of Modern GPU and CPU Architectures: And the Common Convergence of Both

Jonathan Palacios, Josh Triska

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Tags: Computer science, Hardware Architecture, nVidia, Overview, Performance

December 17, 2011 by hgpu

A Parallel GPU Version of the Traveling Salesman Problem

Molly A. O'Neil, Dan Tamir, Martin Burtscher

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Package, Tesla C2050

December 16, 2011 by hgpu

Reducing Thread Divergence in GPU-based B and B Applied to the Flow-shop problem

Imen Chakroun, Ahcene Bendjoudi, Nouredine Melab

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, Tesla C2050

December 16, 2011 by hgpu

Algorithms acceleration of pattern-matching in multi-core architectures

David Rodenas Pico

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GT 430, nVidia GeForce GTX 320 M, Thesis

December 16, 2011 by hgpu

Reducing Thread Divergence in GPU-based B&B Applied to the Flow-shop problem

Imen Chakroun, Ahcene Bendjoudi, Nouredine Melab

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, Tesla C2050

December 16, 2011 by hgpu

Efficient XML Path Filtering Using GPUs

Roger Moussalli, Robert Halstead, Mariam Salloum, Walid Najjar, Vassilis J. Tsotras

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Tags: Computer science, CUDA, Data Structures and Algorithms, FPGA, nVidia, Optimization, Tesla C1060

December 16, 2011 by hgpu

A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

Michael J. Anderson, David Sheffield, Kurt Keutzer

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia Quadro FX 6000, Package, Signal processing

December 16, 2011 by hgpu

Improving GPU Robustness by Making Use of Faulty Parts

Artem Durytskyy, Mohamed Zahran, Ramesh Karri

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Tags: Computer science, Fault simulation, Hardware Architecture, nVidia, Performance

December 16, 2011 by hgpu

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Uzi Vishkin, Rajeev Barua

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Tags: Algorithms, Benchmarking, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Prefetch, Rendering, Thesis

December 16, 2011 by hgpu

Affine Vector Cache for memory bandwidth savings

Sylvain Collange, Alexandre Kouyoumdjian

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Tags: Benchmarking, Computer science, CUDA, Memory model, nVidia, Tesla

December 16, 2011 by hgpu

Simultaneous Branch and Warp Interweaving for Sustained GPU Performance

Nicolas Brunie, Sylvain Collange, Gregory Diamos

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Tags: Computer science, CUDA, nVidia, Performance

December 15, 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

Customizable Memory Schemes for Data Parallel Accelerators

A Comparison of Modern GPU and CPU Architectures: And the Common Convergence of Both

A Parallel GPU Version of the Traveling Salesman Problem

Reducing Thread Divergence in GPU-based B and B Applied to the Flow-shop problem

Algorithms acceleration of pattern-matching in multi-core architectures

Reducing Thread Divergence in GPU-based B&B Applied to the Flow-shop problem

Efficient XML Path Filtering Using GPUs

A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

Improving GPU Robustness by Making Use of Faulty Parts

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Affine Vector Cache for memory bandwidth savings

Simultaneous Branch and Warp Interweaving for Sustained GPU Performance

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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