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A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

Marcos Amaris, Raphael Y. de Camargo, Mohamed Dyab, Alfredo Goldman, Denis Trystram

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Tags: Analytical model, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 970, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X, nVidia Quadro K5200, Package, Performance, Tesla K20, Tesla K40

December 6, 2016 by hgpu

OpenACC cache Directive: Opportunities and Optimizations

Ahmad Lashgar, Amirali Baniasadi

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Tags: Code generation, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Tesla K20

December 3, 2016 by hgpu

Should I use TensorFlow?

Martin Schrimpf

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, Performance, Python, TensorFlow

December 3, 2016 by hgpu

Hardware thread reordering to boost OpenCL throughput on FPGAs

Amir Momeni, Hamed Tabkhi, Gunar Schirner, David Kaeli

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Tags: Computer science, FPGA, Hardware, OpenCL, Performance

November 30, 2016 by hgpu

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

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Tags: Computer science, CUDA, Deep learning, Intel Xeon Phi, NLP, nVidia, nVidia GeForce GTX Titan X, Performance, Tesla K40

November 30, 2016 by hgpu

A Metric for Performance Portability

S. J. Pennycook, J. D. Sewall, V. W. Lee

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Tags: Computer science, CUDA, OpenACC, OpenCL, Performance, performance portability

November 25, 2016 by hgpu

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Manas Mahapatra

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 525 M, OpenCL, Performance, Security, Thesis

November 23, 2016 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

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Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Neetu Faujdar, SP Ghrera

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Performance, Sorting

November 19, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

Efficient Communications in Training Large Scale Neural Networks

Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Zenglin Xu

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Tags: Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, OpenMPI, Performance

November 16, 2016 by hgpu

Benchmarking performance of a hybrid Xeon/Xeon Phi system for parallel computation of similarity measures between large vectors

Pawel Czarnul

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Tags: Benchmarking, Computer science, Intel Xeon Phi, OpenMP, Performance

November 16, 2016 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

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

OpenACC cache Directive: Opportunities and Optimizations

Should I use TensorFlow?

Hardware thread reordering to boost OpenCL throughput on FPGAs

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

A Metric for Performance Portability

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

How to scale distributed deep learning?

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Efficient Communications in Training Large Scale Neural Networks

Benchmarking performance of a hybrid Xeon/Xeon Phi system for parallel computation of similarity measures between large vectors

Recent source codes

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LLaMA-Inference-Bench

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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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