high performance computing on graphics processing units: hgpu.org

Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha Shet G, Bharat Kaul, Balint Joo, Pradeep Dubey

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Tags: Cluster computing, Computer science, FFT, Intel Xeon Phi, Performance, QCD

August 15, 2014 by hgpu

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

Thomas R. W. Scogland, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel Xeon Phi, nVidia, nVidia GeForce GTX 280, OpenCL, Performance, Tesla K20

August 15, 2014 by hgpu

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Gary Lawson, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

August 15, 2014 by hgpu

GPU Accelerated Computation and Real-time Rendering of Cellular Automata Model for Spatial Simulation

Yuan Zhao, Xinchang Zhang, Zhen Zhang, Lu Wang, Yueming Hu

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Tags: Algorithms, Cellular automata, Computer science, CUDA, nVidia, nVidia GeForce GTS 250, OpenGL, Rendering, Visualization

August 15, 2014 by hgpu

Effect of GPU Communication-Hiding for SpMV Using OpenACC

Olav Aanes Fagerlund, Takeshi Kitayama, Gaku Hashimoto, Hiroshi Okuda

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, FEM, Finite element method, OpenACC, Sparse matrix

August 15, 2014 by hgpu

Numerical Computations with GPUs

Volodymyr Kindratenko

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Tags: Algorithms, ATI, Book, Computer science, CUDA, Differential equations, FFT, Linear Algebra, Mathematics, Numerical simulation, nVidia, OpenCL, Pseudo-random number generators, Software Engineering

August 13, 2014 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

An Efficient Cell List Implementation for Monte Carlo Simulation on GPUs

Accelerated composite distribution function methods for computational fluid dynamics using GPU

An OpenCL implementation of a forward sampling algorithm for CP-logic

Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware

Quantum Boolean Image Denoising

High Level High Performance Computing for Multitask Learning of Time-varying Models

Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

GPU Accelerated Computation and Real-time Rendering of Cellular Automata Model for Spatial Simulation

Effect of GPU Communication-Hiding for SpMV Using OpenACC

Numerical Computations with GPUs

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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