high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Philippe Tillet, David Cox

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980 Ti, OpenCL, Package, Performance, PTX, Tesla P100

February 17, 2018 by hgpu

Transforming and Optimizing Irregular Applications for Parallel Architectures

Jing Zhang

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20, Thesis

February 17, 2018 by hgpu

Accelerating Interpreted Programming Languages on GPUs with Just-In-Time Compilation and Runtime Optimisations

Juan Jose Fumero Alfonso

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Tags: AMD Radeon R9 295X2, ATI, Computer science, Java, nVidia, nVidia GeForce GTX Titan Black, OpenCL, R, Thesis

February 15, 2018 by hgpu

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, performance portability, TensorFlow, Tesla K80

February 15, 2018 by hgpu

Improving Locality of Unstructured Mesh Algorithms on GPUs

Andras Attila Sulyok, Gabor Daniel Balogh, Istvan Zoltan Reguly, Gihan R. Mudalige

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Tags: Algorithms, Computer science, CUDA, Finite element method, Mathematical Software, nVidia, Performance, Tesla P100

February 15, 2018 by hgpu

GPU Accelerated Finite Element Assembly with Runtime Compilation

Tao Cui, Xiaohu Guo, Hui Liu

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Tags: Computer science, CUDA, Differential equations, FEM, Finite element method, Mathematical Software, Numerical Analysis, nVidia, Partial differential equations, PDEs, Symbolic Computation, Tesla K20, Tesla M2090, Tesla V100

February 15, 2018 by hgpu

Running Financial Risk Management Applications on FPGA in the Amazon Cloud

Javier Alejandro Varela, Norbert Wehn

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Tags: Finance, FPGA, OpenCL, Risk Management

February 10, 2018 by hgpu

Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management

Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko, Samira Khan, Ashish Shrestha, Saugata Ghose, Phillip B. Gibbons, Onur Mutlu

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Tags: Computer science, CUDA, Hardware Architecture, nVidia, Virtualization

February 10, 2018 by hgpu

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Manolis Loukadakis, Jose Cano, Michael O'Boyle

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Tags: ARM, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

February 10, 2018 by hgpu

Ikra-Cpp: A C++/CUDA DSL for Object-Oriented Programming with Structure-of-Arrays Layout

Matthias Springer, Hidehiko Masuhara

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Tags: Computer science, CUDA, Data Structures and Algorithms, DSL, nVidia, Package

February 9, 2018 by hgpu

Interactive GPU active contours for segmenting inhomogeneous objects

Chris G. Willcocks, Philip T. G. Jackson, Carl J. Nelson, Amar V. Nasrulloh, Boguslaw Obara

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Tags: Biology, Image processing, Medicine, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

February 9, 2018 by hgpu

MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster

Xin Chen, Hua Zhou, Yuxiang Gao, Yu Zhu, Dongyan Wang

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Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Heterogeneous systems, nVidia, RDMA, Tesla P100

February 9, 2018 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

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Transforming and Optimizing Irregular Applications for Parallel Architectures

Accelerating Interpreted Programming Languages on GPUs with Just-In-Time Compilation and Runtime Optimisations

TVM: End-to-End Optimization Stack for Deep Learning

Improving Locality of Unstructured Mesh Algorithms on GPUs

GPU Accelerated Finite Element Assembly with Runtime Compilation

Running Financial Risk Management Applications on FPGA in the Amazon Cloud

Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Ikra-Cpp: A C++/CUDA DSL for Object-Oriented Programming with Structure-of-Arrays Layout

Interactive GPU active contours for segmenting inhomogeneous objects

MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster

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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