high performance computing on graphics processing units: hgpu.org

J. Kocz, L.J Greenhill, B.R. Barsdell, D. Price, G. Bernardi, S. Bourke, M.A. Clark, J. Craig, M. Dexter, J. Dowell, T. Eftekhari, S. Ellingson, G. Hallinan, J. Hartman, A. Jameson, D. MacMahon, G. Taylor, F. Schinzel, D. Werthimer

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Tags: Astrophysics, CUDA, FPGA, GPU cluster, Heterogeneous systems, Instrumentation and Methods for Astrophysics, nVidia, Package, Signal processing, Tesla K20

January 10, 2015 by hgpu

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

Puya Memarzia, Farshad Khunjush

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Tags: Benchmarking, Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 570

January 10, 2015 by hgpu

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang

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Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Package

January 10, 2015 by hgpu

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

Geoffrey Ndu, Mikel Lujan, Javier Navaridas

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Tags: Benchmarking, Computer science, FPGA, OpenCL, Package

January 8, 2015 by hgpu

Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks

Albert Haque

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Tags: CUDA, Machine learning, Medicine, Neural networks, nVidia, nVidia GeForce GTX 750 Ti, Package

January 8, 2015 by hgpu

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

Leonard Truong, Chick Markley, Armando Fox

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Tags: Code generation, Computer science, OpenCL, Python

January 8, 2015 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

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

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high performance computing on graphics processing units: hgpu.org

Applications

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Face Recognition: A Tutorial on Computational Aspects

Dynamic Feature-Adaptive Subdivision

Digital Signal Processing using Stream High Performance Computing: A 512-input Broadband Correlator for Radio Astronomy

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

Image Super-Resolution Using Deep Convolutional Networks

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

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

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

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

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

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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