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Confidentiality Issues on a GPU in a Virtualized Environment

Clementine Maurice, Christoph Neumann, Olivier Heen, Aurelien Francillon

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Tags: Cloud, Computer science, CUDA, nVidia, Security, Tesla K20, Virtualization

February 11, 2014 by hgpu

Exploiting GPU Parallelism to Optimize Real-World Problems

Hasan Furhad, Fahmida Ahmed, Faisal Faruque, Iqbal Hasan Sarker

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 580, Optimization

February 11, 2014 by hgpu

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Vivek K. Pallipuram

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Performance, Tesla K20, Tesla M2075, Thesis

February 11, 2014 by hgpu

Point Rendering in CUDA Path Tracer

Hsuan-Yueh Peng

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 780, Rendering

February 11, 2014 by hgpu

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla C2050, Tesla K20, Tesla M2090

February 11, 2014 by hgpu

Implementing the Projected Spatial Rich Features on a GPU

Andrew D. Ker

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Tags: Computer science, CUDA, Machine learning, nVidia, Tesla K20

February 11, 2014 by hgpu

Genetically Improved CUDA C++ Software

W. B. Langdon, M. Harman

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, nVidia Quadro NVS 290, Package, Tesla C2050, Tesla K20, Tesla T10

February 11, 2014 by hgpu

GPU-based high-performance computing for radiation therapy

Xun Jia, Peter Ziegenhein, Steve B Jiang

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Tags: CUDA, Medicine, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 570, nVidia GeForce GTX 580, nVidia GeForce GTX 590, Physics, Radiotherapy, Review, Tesla C1060, Tesla C2050, Tesla C2070

February 9, 2014 by hgpu

Fast 2-D Ultrasound Strain Imaging: The Benefits of Using a GPU

Tim Idzenga, Evghenii Gaburov, Willem Vermin, Jan Menssen, Chris L. de Korte

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Tags: Algorithms, CUDA, Image processing, nVidia, OpenMP, Package, Tesla K20, Ultrasound

February 8, 2014 by hgpu

State-Based Gauss-Seidel Framework for Real-time 2D Ultrasound Image Sequence Denoising on GPUs

Banpot Dolwithayakul, Chantana Chantrapornchai, Noppadol Chumchob

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GTX 480, OpenMP, Ultrasound

February 8, 2014 by hgpu

Fast 3D Graphics Rendering Technique with CUDA Parallel Processing

Ji-Hoon Kang, Syung-Og An, Shin-Jin Kang, Seok-Hun Kim, SooKyun Kim

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Tags: 3D Graphics and Realism, Computer science, CUDA, GLSL, nVidia, nVidia GeForce GTX 285, OpenGL, Raytracing, Rendering

February 8, 2014 by hgpu

Developmental Directions in Parallel Accelerators

K.A. Hawick, D.P. Playne

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan

February 8, 2014 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Exploiting GPU Parallelism to Optimize Real-World Problems

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Point Rendering in CUDA Path Tracer

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Implementing the Projected Spatial Rich Features on a GPU

Genetically Improved CUDA C++ Software

GPU-based high-performance computing for radiation therapy

Fast 2-D Ultrasound Strain Imaging: The Benefits of Using a GPU

State-Based Gauss-Seidel Framework for Real-time 2D Ultrasound Image Sequence Denoising on GPUs

Fast 3D Graphics Rendering Technique with CUDA Parallel Processing

Developmental Directions in Parallel Accelerators

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

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

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