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Accelerating Deep Neural Networks implementation: A survey

Meriam Dhouibi, Ahmed Karim Ben Salem, Afef Saidi, Slim Ben Saoud

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Tags: ASIC, Computer science, Deep learning, FPGA, HLS, Neural networks, OpenCL, Optimization, Performance, survey

March 28, 2021 by hgpu

A Survey of System Architectures and Techniques for FPGA Virtualization

Masudul Hassan Quraishi, Erfan Bank Tavakoli, Fengbo Ren

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Tags: Computer science, FPGA, Hardware Architecture, HLS, OpenCL, survey, Virtualization

November 22, 2020 by hgpu

Tools for GPU Computing–Debugging and Performance Analysis of Heterogenous HPC Applications

Michael Knobloch, Bernd Mohr

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, survey

September 13, 2020 by hgpu

Tools for Reduced Precision Computation: A Survey

Stefano Cherubin, Giovanni Agosta

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Tags: Approximate computing and storage, Computer science, CUDA, HPC, Mixed precision, nVidia, OpenCL, Precision, survey

May 3, 2020 by hgpu

Towards Green Computing: A Survey of Performance and Energy Efficiency of Different Platforms using OpenCL

Philip Heinisch, Katharina Ostaszewski, Hendrik Ranocha

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Tags: AMD FirePro S9150, AMD Radeon R9 280X, ARM, ATI, Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX 570, nVidia GRID K520, nVidia Titan RTX, OpenCL, survey, Tesla C2075, Tesla P100, Tesla V100

March 15, 2020 by hgpu

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

Jianhua Gao, Weixing Ji, Zhaonian Tan, Yueyan Zhao

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 1080 Ti, Sparse matrix, survey

March 1, 2020 by hgpu

In-memory database acceleration on FPGAs: a survey

Jian Fang, Yvo T. B. Mulder, Jan Hidders, Jinho Lee, H. Peter Hofstee

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Tags: Computer science, Databases, FPGA, HLS, OpenCL, survey

November 3, 2019 by hgpu

Survey paper on Deep Learning on GPUs

Sparsh Mittal, Shraiysh Vaishay

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Tags: Deep learning, Paper, Research, survey

August 21, 2019 by sparsh0mittal

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

Sparsh Mittal, Osho Anand, Visnu P Kumarr

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Tags: Compilers, Computer science, Intel Xeon Phi, OpenMP, survey

June 9, 2019 by hgpu

A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform

Sparsh Mittal

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TK1, nVidia Jetson TX1, nVidia Jetson TX2, OpenCL, survey

December 30, 2018 by hgpu

A Survey of FPGA-based Accelerators for Convolutional Neural Networks

Sparsh Mittal

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, FPGA, HLS, Neural networks, OpenCL, survey

October 21, 2018 by hgpu

A Survey of ReRAM-based Architectures for Processing-in-memory and Neural Networks

Sparsh Mittal

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Tags: Machine learning, memristor, neural network, processing in memory, survey

May 5, 2018 by sparsh0mittal

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Accelerating Deep Neural Networks implementation: A survey

A Survey of System Architectures and Techniques for FPGA Virtualization

Tools for GPU Computing–Debugging and Performance Analysis of Heterogenous HPC Applications

Tools for Reduced Precision Computation: A Survey

Towards Green Computing: A Survey of Performance and Energy Efficiency of Different Platforms using OpenCL

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

In-memory database acceleration on FPGAs: a survey

Survey paper on Deep Learning on GPUs

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform

A Survey of FPGA-based Accelerators for Convolutional Neural Networks

A Survey of ReRAM-based Architectures for Processing-in-memory and Neural Networks

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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