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Development and evaluation of a GPU-optimized N-body term for the simulation of biomolecules

Timo Strunk, Moritz Wolf, Wolfgang Wenzel

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Biology, Biomolecules, Monte Carlo simulation, N-body simulation, nVidia, nVidia GeForce GTX 285, OpenCL, Optimization

October 30, 2011 by hgpu

CPU and GPU Co-processing for Sound

Aleksander Gjermundsen

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Computer science, Filtering, Linear Algebra, nVidia, nVidia GeForce GTX 280, OpenCL, Package, Signal processing, Tesla C2050, Thesis

October 30, 2011 by hgpu

Jit4OpenCL: a compiler from Python to OpenCL

Xunhao Li

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Tags: ATI, ATI Radeon HD 5850, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, OpenCL, Python, Thesis

October 29, 2011 by hgpu

Using OpenCL for image analysis

Niklas Frisk

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Tags: Algorithms, Benchmarking, Image processing, Java, nVidia, nVidia GeForce 8600 M, OpenCL, Thesis

October 29, 2011 by hgpu

Using GPUs to Accelerate Installed Antenna Performance Simulations

Tod Courtney, John E. Stone, Bob Kipp

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Tags: CUDA, Electrodynamics, nVidia, nVidia GeForce GTX 480, Raytracing

October 29, 2011 by hgpu

An Exploration of OpenCL on Multiple Hardware Platforms for a Numerical Relativity Application

Niket K. Choudhary, Sandeep Navada, Rakesh Ginjupalli, Gaurav Khanna

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Tags: Astrophysics, ATI, ATI Radeon HD 5870, Differential equations, nVidia, OpenCL, Partial differential equations, PDEs, Tesla C2050

October 29, 2011 by hgpu

An Adaptive Framework for Managing Heterogeneous Many-Core Clusters

Muhammad Mustafa Rafique

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, Prefetch, Software Engineering, Tesla C2050, Thesis

October 28, 2011 by hgpu

Compiling Stream Applications for Heterogeneous Architectures

Amir H. Hormati

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Tags: Compilers, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Optimization, Tesla C2050, Thesis

October 28, 2011 by hgpu

Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems

Long Zheng, Yanchao Lu, Mengwei Ding, Yao Shen, Minyi Guo, Song Guo

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 580, Optimization

October 28, 2011 by hgpu

Matrix inversion speed up with CUDA

Jorge Soriano Pinedo

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Tags: Algorithms, Computer science, CUDA, Image reconstruction, Matrix inversion, Matrix multiplication, nVidia, Tesla C1060, Thesis

October 28, 2011 by hgpu

Parallel Random Numbers: As Easy as 1, 2, 3

John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw

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Tags: AES, ATI, ATI Radeon HD 6970, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Performance, Pseudo-random number generators, Security

October 28, 2011 by hgpu

Programming Massively Parallel Processors with CUDA (audio course)

David B. Kirk, Wen-Mei W. Hwu

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Tags: Book, Computer science, CUDA, nVidia, OpenCL, Package, Programming techniques, Review, Tutorial

October 28, 2011 by hgpu

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

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

Development and evaluation of a GPU-optimized N-body term for the simulation of biomolecules

CPU and GPU Co-processing for Sound

Jit4OpenCL: a compiler from Python to OpenCL

Using OpenCL for image analysis

Using GPUs to Accelerate Installed Antenna Performance Simulations

An Exploration of OpenCL on Multiple Hardware Platforms for a Numerical Relativity Application

An Adaptive Framework for Managing Heterogeneous Many-Core Clusters

Compiling Stream Applications for Heterogeneous Architectures

Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems

Matrix inversion speed up with CUDA

Parallel Random Numbers: As Easy as 1, 2, 3

Programming Massively Parallel Processors with CUDA (audio course)

Recent source codes

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

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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