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Automated Generation of OpenCL Programs Based on Algebra-Algorithmic Approach

Anatoliy Doroshenko, Oleksii Beketov, Mykola Bondarenko, Olena Yatsenko

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 840 M, OpenCL

June 30, 2019 by hgpu

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

Philippe Tillet, H. T. Kung, David Cox

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Tags: Compilers, Computer science, CUDA, Deep learning, High-level Languages, LLVM, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1070, Package

June 30, 2019 by hgpu

ReSYCLator: Transforming CUDA C++ source code into SYCL

Tobias Stauber, Peter Sommerlad

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, SYCL

June 27, 2019 by hgpu

Mirovia: A Benchmarking Suite for Modern Heterogeneous Computing

Bodun Hu, Christopher J. Rossbach

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, Package, Performance, Tesla P100

June 27, 2019 by hgpu

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Zhen Xie, Guangming Tan, Weifeng Liu, Ninghui Sun

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Tags: Algorithms, Auto-Tuning, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

June 23, 2019 by hgpu

RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

Ingo Wald, Will Usher, Nate Morrical, Laura Lediaev, Valerio Pascucci

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Tags: 3D Graphics and Realism, CUDA, nVidia, nVidia Titan RTX, Raytracing

June 16, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Elias Stehle, Hans-Arno Jacobsen

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, nVidia, nVidia GeForce GTX Titan X

June 5, 2019 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla V100

June 5, 2019 by hgpu

A Development Platform for Embedded Domain-Specific Languages

Shigeru Chiba, YungYu Zhuang, Thanh-Chung Dao

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Tags: Computer science, CUDA, DSL, High-level Languages, Java, nVidia, Package

June 2, 2019 by hgpu

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Yunfei Teng, Wenbo Gao, Francois Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080

June 2, 2019 by hgpu

Breadth-First Search using Dynamic Parallelism on the GPU

Dominik Todling

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Tags: Algorithms, Breadth-First Search, Computer science, CUDA, nVidia, Package

May 30, 2019 by hgpu

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

SciDef: Automated Definition Extraction from Scientific Literature

SciDef: Automating Definition Extraction from Academic Literature with Large Language Models

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

Programming

Automated Generation of OpenCL Programs Based on Algebra-Algorithmic Approach

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

ReSYCLator: Transforming CUDA C++ source code into SYCL

Mirovia: A Benchmarking Suite for Modern Heterogeneous Computing

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Dynamic Distribution Pruning for Efficient Network Architecture Search

A Development Platform for Embedded Domain-Specific Languages

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Breadth-First Search using Dynamic Parallelism on the GPU

Recent source codes

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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