high performance computing on graphics processing units: hgpu.org

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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

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

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

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

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

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

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

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

Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

Hamid Reza Zohouri, Artur Podobas, Satoshi Matsuoka

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Tags: Computer science, FPGA, OpenCL, Stencil computation

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

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Programming

Transforming and Optimizing Irregular Applications for Parallel Architectures

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

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

Improving Locality of Unstructured Mesh Algorithms on GPUs

GPU Accelerated Finite Element Assembly with Runtime Compilation

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

Running Financial Risk Management Applications on FPGA in the Amazon Cloud

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

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

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

Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

Interactive GPU active contours for segmenting inhomogeneous objects

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