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Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda

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Tags: Algorithms, ARM, ASIC, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Tegra TX1

July 20, 2016 by hgpu

IODA: an Input/Output Deep Architecture for image labeling

J. Lerouge, R. Herault, Clement Chatelain, F. Jardin, R. Modzelewski

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python, Tesla C1060, Theano

July 18, 2016 by hgpu

HPC on the Intel Xeon Phi: Homomorphic Word Searching

Paulo Martins, Leonel Sousa

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Tags: Computer science, Intel Xeon Phi, Security

July 18, 2016 by hgpu

Heterogeneous Computing for Data Stream Mining

Vladimir Petko

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Tags: Algorithms, Computer science, Data mining, Heterogeneous systems, Machine learning, Nearest neighbour, nVidia, OpenCL, Tesla C2050, Thesis

July 18, 2016 by hgpu

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix decomposition, Mixed precision, Numerical Analysis, nVidia, Performance, Tesla K20

July 16, 2016 by hgpu

Finite Element Integration with Quadrature on the GPU

Matthew G. Knepley, Karl Rupp, Andy R. Terrel

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Tags: AMD FirePro W9100, ATI, Computer science, Differential equations, Mathematical Software, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 750 Ti, OpenCL, Partial differential equations, PDEs, Tesla K20

July 16, 2016 by hgpu

Survey of Domain-Specific Languages for FPGA Computing

Nachiket Kapre, Samuel Bayliss

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Tags: Computer science, DSL, FPGA, survey

July 13, 2016 by hgpu

OpenFace: A general-purpose face recognition library with mobile applications

Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan

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Tags: Computer science, Computer vision, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, Python, Tesla K40

July 13, 2016 by hgpu

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Materials Science, Molecular dynamics, MPI, nVidia, Package, performance portability, Tesla K20, Tesla K40

July 13, 2016 by hgpu

LU, QR, and Cholesky factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi

Azzam Haidar, Stanimire Tomov, Konstantin Arturov, Murat Guney, Shane Story, Jack Dongarra

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Tags: Algorithms, Computer science, Factorization, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, Performance, Task scheduling

July 13, 2016 by hgpu

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Tobias Graf, Marco Platzner

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Tags: Algorithms, CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, Search, Tesla K20

July 11, 2016 by hgpu

Comparing Parallel Hardware Architectures for Visually Guided Robot Navigation

Wolfram Schenck, Michael Horst, Tim Tiedemann, Sergius Gaulik, Ralf Moller

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Tags: ATI, ATI Radeon HD 7970, Computer science, Computer vision, FPGA, nVidia, nVidia GeForce GTX 570, nVidia Quadro NVS 4200 M, OpenCL, Review, Robotics

July 11, 2016 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

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high performance computing on graphics processing units: hgpu.org

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

IODA: an Input/Output Deep Architecture for image labeling

HPC on the Intel Xeon Phi: Homomorphic Word Searching

Heterogeneous Computing for Data Stream Mining

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Finite Element Integration with Quadrature on the GPU

Survey of Domain-Specific Languages for FPGA Computing

OpenFace: A general-purpose face recognition library with mobile applications

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

LU, QR, and Cholesky factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Comparing Parallel Hardware Architectures for Visually Guided Robot Navigation

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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