high performance computing on graphics processing units: hgpu.org

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Computing Treewidth on the GPU

Tom C. van der Zanden, Hans L. Bodlaender

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Tags: Algorithms, Bloom filter, Computer science, Graph theory, nVidia, nVidia GeForce GTX 1060, OpenCL, Package

October 3, 2017 by hgpu

OpenCL Actors – Adding Data Parallelism to Actor-based Programming with CAF

Raphael Hiesgen, Dominik Charousset, Thomas C. Schmidt

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Tags: Computer science, Data parallelism, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 780 M, OpenCL, Package, Tesla C2075

September 28, 2017 by hgpu

Accelerating Radio Astronomy with Auto-Tuning

Alessio Sclocco

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Tags: AMD FirePro W9100, AMD Radeon R9 Fury X, Astrophysics, ATI, ATI Radeon HD 6970, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 580, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Tesla K20, Thesis

September 21, 2017 by hgpu

Out-of-core Implementation for Accelerator Kernels on Heterogeneous Clouds

Hamidreza Khaleghzadeh, Ziming Zhong, Ravi Reddy, Alexey Lastovetsky

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Tags: BLAS, Cloud, Computer science, CUBLAS, CUDA, FPGA, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Virtualization

September 16, 2017 by hgpu

End-to-end Deep Learning of Optimization Heuristics

Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Compilers, Computer science, Deep learning, Keras, Machine learning, nVidia GeForce GTX 480, OpenCL, Optimization, Package, TensorFlow, Tesla K20, Theano

September 16, 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

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

ParTeCL: parallel testing using OpenCL

Vanya Yaneva, Ajitha Rajan, Christophe Dubach

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

July 25, 2017 by hgpu

A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations

Jan Vanek, Josef Michalek, Josef Psutka

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

July 22, 2017 by hgpu

Termination Analysis for GPU Kernels

Jeroen Ketema, Alastair F. Donaldson

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Tags: Computer science, CUDA, LLVM, OpenCL, Package

July 19, 2017 by hgpu

GPU-acceleration for Large-scale Tree Boosting

Huan Zhang, Si Si, Cho-Jui Hsieh

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Tags: Algorithms, AMD Radeon RX 480, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

July 2, 2017 by hgpu

Snowflake: A Lightweight Portable Stencil DSL

Nathan Zhang, Michael Driscoll, Armando Fox, Charles Markley, Samuel Williams, Protonu Basu

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Tags: Code generation, Computer science, DSL, nVidia, OpenCL, Package, Python, Stencil computation, Tesla K20

July 2, 2017 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

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

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

Programming

Computing Treewidth on the GPU

OpenCL Actors – Adding Data Parallelism to Actor-based Programming with CAF

Accelerating Radio Astronomy with Auto-Tuning

End-to-end Deep Learning of Optimization Heuristics

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

eccCL: parallelized GPU implementation of Ensemble Classifier Chains

ParTeCL: parallel testing using OpenCL

A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations

Termination Analysis for GPU Kernels

GPU-acceleration for Large-scale Tree Boosting

Snowflake: A Lightweight Portable Stencil DSL

Recent source codes

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

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

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