high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

Adaboost GPU-based Classifier for Direct Volume Rendering

Oscar Amoros, Sergio Escalera, Anna Puig

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Tags: Computer science, CUDA, GLSL, Machine learning, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, OpenCL, OpenGL, Rendering, Visualization

November 17, 2011 by hgpu

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

HyoukJoong Lee, Kevin J. Brown, Arvind K. Sujeeth, Hassan Chafi, Kunle Olukotun, Tiark Rompf, Martin Odersky

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Optimization, Tesla C2050

October 28, 2011 by hgpu

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

Sergei Morozov, Sudhanshu Mathur

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Source codes

Tags: CUDA, Finance, Fortran, Machine learning, nVidia, nVidia GeForce GTX 280, Package

October 21, 2011 by hgpu

OptiML: An implicitly parallel domain-specific language for machine learning

Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Tesla C2050

October 14, 2011 by hgpu

A Heterogeneous Parallel Framework for Domain-Specific Languages

Kevin J. Brown, Arvind K. Sujeeth, HyoukJoong Lee, Tiark Rompf, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Programming techniques, Tesla C2050

October 14, 2011 by hgpu

Fast On-line Statistical Learning on a GPGPU

FangZhou Xiao, Eric McCreath, Christfried Webers

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Tags: Algorithms, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 200, nVidia GeForce GTX 295, Stochastic simulation

September 27, 2011 by hgpu

Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes

S. Escalera, A. Puig, O. Amoros, M. Salamo

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Tags: Algorithms, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX 470, OpenCL, Rendering, Visualization

September 27, 2011 by hgpu

NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce

Amol Ghoting, Prabhanjan Kambadur, Edwin Pednault, Ramakrishnan Kannan

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Tags: Algorithms, Computer science, Data mining, Machine learning, MapReduce, MPI, OpenCL, Performance, Software Engineering

September 16, 2011 by hgpu

Visual Human – Machine Learning

Raphael Fuchs, Jurgen Waser, Meister Eduard Groller

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Tags: Algorithms, Computer science, CUDA, Genetic programming, Machine learning, nVidia, Pattern recognition, Rendering, Visualization

September 4, 2011 by hgpu

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

Marcin Bogdanski, Peter R. Lewis, Tobias Becker, Xin Yao

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Tags: Algorithms, Computer science, Finance, FPGA, Heterogeneous systems, Machine learning, Task scheduling

September 3, 2011 by hgpu

Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise Hashing and Comparisons with Vowpal Wabbit (VW)

Ping Li, Anshumali Shrivastava, Christian Konig

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Tags: Algorithms, Computer science, Machine learning

August 16, 2011 by hgpu

Articulated object tracking by rendering consistent appearance parts

Zachary Pezzementi, Sandrine Voros, Gregory D. Hager

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Tags: Computer science, Filtering, GLSL, Machine learning, nVidia, nVidia GeForce 8600 GT, nVidia GeForce 8800 GTX, OpenGL, Rendering

July 30, 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

Adaboost GPU-based Classifier for Direct Volume Rendering

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

OptiML: An implicitly parallel domain-specific language for machine learning

A Heterogeneous Parallel Framework for Domain-Specific Languages

Fast On-line Statistical Learning on a GPGPU

Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes

NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce

Visual Human – Machine Learning

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise Hashing and Comparisons with Vowpal Wabbit (VW)

Articulated object tracking by rendering consistent appearance parts

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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