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Integer sorting on multicores: some (experiments and) observations

Alexandros V. Gerbessiotis

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Performance, Sorting, Tesla K40

September 3, 2017 by hgpu

Real-Time Rendering of Molecular Dynamics Simulation Data: A Tutorial

N.Alharbi, M. Chavent, RS. Laramee

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Tags: Computer science, Image generation, Intel HD 6100, Molecular dynamics, OpenCL, OpenGL, Package, Rendering, Tutorial

September 3, 2017 by hgpu

Towards On-Chip Optical FFTs for Convolutional Neural Networks

Jonathan George, Hani Nejadriahi, Volker Sorger

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Tags: CNN, Computer science, Deep learning, FFT, Neural networks, nVidia, Tesla P100

September 3, 2017 by hgpu

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

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Tags: Computer science, CUDA, Deep learning, Image recognition, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Performance, TensorFlow, Tesla K40

September 3, 2017 by hgpu

Accelerate Local Tone Mapping for High Dynamic Range Images Using OpenCL with GPU

Kuo-Feng Liao, Yarsun Hsu

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Tags: Algorithms, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GT 750 M, OpenCL

August 26, 2017 by hgpu

Vulnerability Analysis and Attacks on Intel Xeon Phi Coprocessor

Seungmin Lee, Ju-Won Park

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Tags: Computer science, Intel Xeon Phi, Security

August 26, 2017 by hgpu

Large Integer Arithmetic in GPU for Cryptography

Lee Wen Dick

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, Security, Thesis

August 26, 2017 by hgpu

Dynamic Parallelism in GPU Optimized Barnes Hut Trees for Molecular Dynamics Simulations

Melisa Carranza Zuniga

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Tags: Algorithms, Biology, Biomolecules, Biophysics, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Physics, Thesis

August 26, 2017 by hgpu

eccCL: parallelized GPU implementation of Ensemble Classifier Chains

Mona Riemenschneider, Alexander Herbst, Ari Rasch, Sergei Gorlatch, Dominik Heider

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Tags: Biology, Computer science, Java, Next-Generation sequencing, nVidia, OpenCL, Package, R, Tesla K20

August 26, 2017 by hgpu

Scaling Deep Learning on GPU and Knights Landing clusters

Yang You, Aydin Buluc, James Demmel

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Tags: Cloud, Computer science, Deep learning, GPU cluster, Intel Xeon Phi, Machine learning, Neural networks, nVidia, Package, PC cluster, Tesla K80, Tesla M100

August 17, 2017 by hgpu

Simulating the Cardinal Movements of Childbirth Using Finite Element Analysis on the Graphics Processing Unit

Zelimkhan Gerikhanov

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Tags: AMD Radeon HD 8970 M, ATI, Biology, Biomechanics, FEM, Finite element method, Medicine, MRI, OpenCL, Thesis

August 17, 2017 by hgpu

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

Xu Liu, Langshi Chen, Jesun S. Firoz, Judy Qiu, Lei Jiang

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Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1070

August 17, 2017 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

Integer sorting on multicores: some (experiments and) observations

Real-Time Rendering of Molecular Dynamics Simulation Data: A Tutorial

Towards On-Chip Optical FFTs for Convolutional Neural Networks

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Accelerate Local Tone Mapping for High Dynamic Range Images Using OpenCL with GPU

Vulnerability Analysis and Attacks on Intel Xeon Phi Coprocessor

Large Integer Arithmetic in GPU for Cryptography

Dynamic Parallelism in GPU Optimized Barnes Hut Trees for Molecular Dynamics Simulations

eccCL: parallelized GPU implementation of Ensemble Classifier Chains

Simulating the Cardinal Movements of Childbirth Using Finite Element Analysis on the Graphics Processing Unit

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

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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