high performance computing on graphics processing units: hgpu.org

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GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

X. Bellekens, C. Tachtatzis, R. C. Atkinson, C. Renfrew, T. Kirkham

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Tags: CUDA, nVidia, Security, Tesla K20

April 9, 2016 by xhgpu

A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

X. J. A. Bellekens, C. Tachtatzis, R. C. Atkinson, C. Renfrew, T. Kirkham

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Tags: CUDA, nVidia, Security, Tesla K20

April 9, 2016 by xhgpu

An Exploratory Study of High Performance Graphics Application Programming Interfaces

Joseph Shiraef

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Tags: AMD Radeon R9 290X, ATI, Computer science, DirectX, nVidia, nVidia GeForce GTX 970, OpenGL, Performance, Thesis, Vulkan

April 6, 2016 by hgpu

gpucc: an open-source GPGPU compiler

Jingyue Wu, Artem Belevich, Eli Bendersky, Mark Heffernan, Chris Leary, Jacques Pienaar, Bjarke Roune, Rob Springer, Xuetian Weng, Robert Hundt

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Tags: Code generation, Compilers, Computer science, CUDA, LLVM, nVidia, Presentation, Tesla K40

April 6, 2016 by hgpu

Automatic test case reduction of randomly generated OpenCL kernels

Moritz Pflanzer

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Tags: Code generation, Computer science, LLVM, nVidia, nVidia GeForce GT 630, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, nVidia Quadro K5200, OpenCL, Thesis

April 6, 2016 by hgpu

Are Very Deep Neural Networks Feasible on Mobile Devices?

S. Rallapalli, H. Qiu, A. J. Bency, S. Karthikeyan, R. Govindan, B.S.Manjunath, R. Urgaonkar

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1

April 6, 2016 by hgpu

GPU-accelerated stochastic predictive control of drinking water networks

Ajay K. Sampathirao, Pantelis Sopasakis, Alberto Bemporad, Panagiotis Patrinos

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Tags: CUDA, Mathematics, nVidia, Tesla C2075

April 6, 2016 by hgpu

Dynamic Sparse-Matrix Allocation on GPUs

James King, Thomas Gilray, Robert M. Kirby, Matthew Might

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 3, 2016 by hgpu

sPEGG: high throughput eco-evolutionary simulations on commodity graphics processors

Kenichi W. Okamoto, Priyanga Amarasekare

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Tags: Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 690, Package

April 3, 2016 by hgpu

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

Guixia He, Jiaquan Gao

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050

March 29, 2016 by hgpu

A generalized GPU-based connected component labeling algorithm

Yukihiro Komura

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX Titan X, Physics

March 29, 2016 by hgpu

Generic Inverted Index on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Databases, Hashing, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Tesla K80

March 29, 2016 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

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

Generating Literature-Driven Scientific Theories at Scale

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

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

An Exploratory Study of High Performance Graphics Application Programming Interfaces

gpucc: an open-source GPGPU compiler

Automatic test case reduction of randomly generated OpenCL kernels

Are Very Deep Neural Networks Feasible on Mobile Devices?

GPU-accelerated stochastic predictive control of drinking water networks

Dynamic Sparse-Matrix Allocation on GPUs

sPEGG: high throughput eco-evolutionary simulations on commodity graphics processors

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

A generalized GPU-based connected component labeling algorithm

Generic Inverted Index 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

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

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

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

Awesome LLM-Driven Kernel Generation

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