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BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

Performance Portable GPU Code Generation for Matrix Multiplication

Toomas Remmelg, Thibaut Lutz, Michel Steuwer, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan Black, OpenCL

February 10, 2016 by hgpu

Portable Programming Models for Heterogeneous Platforms

Deepak Majeti

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7660, Computer science, Heterogeneous systems, nVidia, OpenCL, Package, Tesla M2050, Thesis

February 8, 2016 by hgpu

High performance high-order numerical methods: applications in ocean modeling

Rajesh Gandham

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Tags: ATI, ATI Radeon HD 7970, cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, nVidia GeForce GTX Titan, OCCA, OpenCL, OpenMP, real time systems, Tesla C2050, Tesla K20, Tesla K40, Thesis

February 8, 2016 by hgpu

Workload distribution and balancing in FPGAs and CPUs with OpenCL and TBB

Rafael Asenjo, Angeles Navarro, Andres Rodriguez, Jose Nunez-Yanez

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia, nVidia Quadro K600, OpenCL

February 8, 2016 by hgpu

Collaborative design and optimization using Collective Knowledge

Anton Lokhmotov, Grigori Fursin

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Tags: Computer science, Machine learning, OpenCL

February 8, 2016 by hgpu

FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only

Jinhwan Park, Wonyong Sung

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Tags: Computer science, Deep learning, FPGA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black

February 6, 2016 by hgpu

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

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Tags: Algorithms, Computer science, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla K40

February 6, 2016 by hgpu

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

Gang Mei, Hong Tian

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, Package

February 6, 2016 by hgpu

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

Milan Ceska, Petr Pilar, Nicola Paoletti, Lubos Brim, Marta Kwiatkowska

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Probabilistic model checking

February 6, 2016 by hgpu

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TK1, Sparse matrix

February 6, 2016 by hgpu

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Performance Portable GPU Code Generation for Matrix Multiplication

Portable Programming Models for Heterogeneous Platforms

High performance high-order numerical methods: applications in ocean modeling

Workload distribution and balancing in FPGAs and CPUs with OpenCL and TBB

Collaborative design and optimization using Collective Knowledge

FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only

Asynchronous Methods for Deep Reinforcement Learning

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Recent source codes

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

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