high performance computing on graphics processing units: hgpu.org

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Anti-parallel Patterns in Fine-grain Data-parallel Programs

Jan Cornelis

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Tags: Benchmarking, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 280, Performance, Thesis

October 10, 2011 by hgpu

Streaming-Oriented Parallelization of Domain-Independent Irregular Kernels

Jacobo Lobeiras, Margarita Amor, Manuel Arenaz, Basilio B. Fraguela

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Tags: Algorithms, ATI, ATI Radeon HD 4850, Brook, Computer science, OpenMP, Performance, Presentation

October 10, 2011 by hgpu

Evaluation of GPU Architectures Using Spiking Neural Networks

Vivek K. Pallipuram, Mohammad A. Bhuiyan, Melissa C. Smith

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Tags: ATI, ATI Radeon HD 5870, Computer science, Neural networks, nVidia, OpenCL, Optimization, Performance, Presentation, Tesla C2050

October 10, 2011 by hgpu

GPUs, a New Tool of Acceleration in CFD: Efficiency and Reliability on Smoothed Particle Hydrodynamics Methods

Alejandro C. Crespo, Jose M. Dominguez, Anxo Barreiro, Moncho Gomez-Gesteira, Benedict D. Rogers

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance, Tesla M1060, Visualization

October 10, 2011 by hgpu

Multifrontal computations on GPUs and their multi-core hosts

Robert F. Lucas, Gene Wagenbreth, Dan M. Davis, Roger Grimes

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Tags: Computer science, CUDA, Factorization, nVidia, Performance, Sparse matrix, Tesla C1060

October 7, 2011 by hgpu

Run-time Reconfigurable Multiprocessors

Madhura Purnaprajna

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Tags: ATI, Computer science, FPGA, Hardware Architecture, nVidia, Performance, Thesis

October 7, 2011 by hgpu

CUDA performance analyzer

Aniruddha Dasgupta

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Tags: Analytical model, Computer science, CUDA, Fast multipole method, nVidia, Optimization, Performance, Tesla C1060, Thesis

October 6, 2011 by hgpu

Static GPU threads and an improved scan algorithm

Jens Breitbart

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 280, OpenCL, Performance, Presentation, Programming techniques

October 6, 2011 by hgpu

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Mark Silberstein, Assaf Schuster, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Optimization, Package, Performance, Probability, Task scheduling, Tesla C2050

October 5, 2011 by hgpu

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Allison S. Gehrke, Ilkyeun Ra, Daniel A. Connors

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Tags: Code generation, Computer science, Numerical simulation, nVidia, Optimization, Performance, PTX

October 5, 2011 by hgpu

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Fawnizu Azmadi Hussin, Anas Mohd Nazlee, Noohul Basheer Zain Ali

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Tags: Algorithms, Computer science, CUDA, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 260, Performance, Programming techniques, Security

October 3, 2011 by hgpu

GPU-Accelerated DNA Distance Matrix Computation

Zhi Ying, Xinhua Lin, Simon Chong-Wee See, Minglu Li

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Tags: ATI, ATI Radeon HD 5850, Bioinformatics, Biology, Genetics, nVidia, OpenCL, Performance, Tesla C2050

October 3, 2011 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

Anti-parallel Patterns in Fine-grain Data-parallel Programs

Streaming-Oriented Parallelization of Domain-Independent Irregular Kernels

Evaluation of GPU Architectures Using Spiking Neural Networks

GPUs, a New Tool of Acceleration in CFD: Efficiency and Reliability on Smoothed Particle Hydrodynamics Methods

Multifrontal computations on GPUs and their multi-core hosts

Run-time Reconfigurable Multiprocessors

CUDA performance analyzer

Static GPU threads and an improved scan algorithm

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

GPU-Accelerated DNA Distance Matrix Computation

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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

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

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