high performance computing on graphics processing units: hgpu.org

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Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Jack Turner, Jose Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey

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Tags: ARM, Compression, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, OpenMP, Package

September 16, 2018 by hgpu

Learning to Optimize Tensor Programs

Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: ARM, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X

May 26, 2018 by hgpu

Multithreaded Dense Linear Algebra on Asymmetric Multi-core Processors

Sandra Catalan Pallares

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Tags: ARM, Benchmarking, Computer science, Factorization, Linear Algebra, Thesis

March 10, 2018 by hgpu

Portable Real-Time DCT Based Steganography Using OpenCL

Ante Poljicak, Guillermo Botella, Carlos Garcia, Luka Kedmenec, Manuel Prieto-Matias

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Tags: APU, ARM, ATI, ATI Radeon HD 6870, Image processing, nVidia, nVidia GeForce GTX 980, OpenCL, real time systems

March 10, 2018 by hgpu

OpenCL Acceleration for TensorFlow

Mehdi Goli, Luke Iwanski, John Lawson, Uwe Dolinsky, Andrew Richards

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Tags: ARM, Computer science, Deep learning, Machine learning, OpenCL, Performance, SYCL, TensorFlow

March 3, 2018 by hgpu

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

Alfonso Rodriguez, Cesar Castanares, Teresa Riesgo, Eduardo de la Torre

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Tags: ARM, Computer science, FPGA, nVidia, nVidia GeForce GTX Titan X, OpenCL

February 17, 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

A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques

Grigori Fursin, Anton Lokhmotov, Dmitry Savenko, Eben Upton

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Tags: ARM, Computer science, Machine learning, OpenCL

February 3, 2018 by hgpu

High Performance Stencil Code Generation with Lift

Bastian Hagedorn, Larisa Stoltzfus, Michel Steuwer, Sergei Gorlatch, Christophe Dubach

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Tags: ARM, ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Linear Algebra, nVidia, OpenCL, Package, performance portability, Scala, Tesla K20

January 13, 2018 by hgpu

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Hsi-Yu Schive, John A. ZuHone, Nathan J. Goldbaum, Matthew J. Turk, Massimo Gaspari, Chin-Yu Cheng

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Tags: ARM, Astrophysics, Chemistry, CUDA, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, nVidia, OpenMP, Package, Tesla K20, Tesla P100

December 24, 2017 by hgpu

Compiling and Optimizing OpenMP 4.X Programs to OpenCL and SPIR

Marcio M. Pereira, Rafael C. F. Sousa, Guido Araujo

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Tags: ARM, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, LLVM, nVidia, OpenCL, OpenMP, Package, Tesla K40

November 21, 2017 by hgpu

Low-power System-on-Chip Processors for Energy Efficient High Performance Computing: The Texas Instruments Keystone II

Gaurav Mitra

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Tags: ARM, Benchmarking, Computer science, DSP, Heterogeneous systems, Matrix multiplication, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, OpenCL, SoC, Thesis

November 12, 2017 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Learning to Optimize Tensor Programs

Multithreaded Dense Linear Algebra on Asymmetric Multi-core Processors

Portable Real-Time DCT Based Steganography Using OpenCL

OpenCL Acceleration for TensorFlow

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques

High Performance Stencil Code Generation with Lift

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Compiling and Optimizing OpenMP 4.X Programs to OpenCL and SPIR

Low-power System-on-Chip Processors for Energy Efficient High Performance Computing: The Texas Instruments Keystone II

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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