high performance computing on graphics processing units: hgpu.org

Programming

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DeepPy: Pythonic deep learning

Anders Boesen Lindbo Larsen

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

September 3, 2016 by hgpu

Deep Learning on FPGAs

Griffin James Lacey

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Tags: Computer science, Deep learning, FPGA, nVidia, nVidia Quadro K600, OpenCL, Thesis

August 31, 2016 by hgpu

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package

August 31, 2016 by hgpu

Optimization of RAID Erasure Coding Algorithms for Intel Xeon Phi

Aleksei Marov, Andrey Fedorov

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Tags: Algorithms, Computer science, Error recovery, Intel Xeon Phi

August 31, 2016 by hgpu

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 2016 by hgpu

Flux tubes at Finite Temperature

Pedro Bicudo, Nuno Cardoso, Marco Cardoso

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Tags: CUDA, High Energy Physics - Lattice, nVidia, Phase transition, Physics, QCD

August 31, 2016 by hgpu

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Suyang Zhu, Sunita Chandrasekaran, Peng Sun, Barbara Chapman, Marcus Winter, Tobias Schuele

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson TK1

August 28, 2016 by hgpu

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Christopher P. Stone, Kyle E. Niemeyer

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Tags: Computational Physics, Computer science, Differential equations, Intel Xeon Phi, nVidia, ODEs, OpenCL, Ordinary differential equations, Physics, Tesla K20

August 28, 2016 by hgpu

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Dennis Bazow, Ulrich W. Heinz, Michael Strickland

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, High Energy Physics - Phenomenology, Intel Xeon Phi, Nuclear Theory, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan Z, Physics

August 28, 2016 by hgpu

Fast Multidimensional Image Processing with OpenCL

Daniel Oliveira Dantas, Helton Danilo Passos Leal, Davy Oliveira Barros Sousa

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Tags: AMD Radeon R9 270X, ATI, CUDA, GLSL, Image processing, OpenCL, Package

August 23, 2016 by hgpu

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter

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Tags: Algorithms, Artificial intelligence, Bayesian, Computer science, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Optimization, Package, Tesla M2075

August 23, 2016 by hgpu

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Ahmed E. Helal, Paul Sathre, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, Intel Xeon Phi, MPI, nVidia, OpenCL, performance portability, Tesla K20

August 23, 2016 by hgpu

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

DeepPy: Pythonic deep learning

Deep Learning on FPGAs

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Optimization of RAID Erasure Coding Algorithms for Intel Xeon Phi

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Flux tubes at Finite Temperature

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Fast Multidimensional Image Processing with OpenCL

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Recent source codes

Awesome LLM-Driven Kernel Generation

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

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