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Automatically Selecting Profitable Thread Block Sizes Using Machine Learning

Tiffany A. Connors

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Tags: Computer science, CUDA, Machine learning, nVidia, Performance, Tesla K40, Thesis

July 22, 2017 by hgpu

Cooperative Kernels: GPU Multitasking for Blocking Algorithms

Tyler Sorensen, Hugues Evrard, Alastair F. Donaldson

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Tags: Algorithms, Computer science, OpenCL, Performance, Programming Languages

July 14, 2017 by hgpu

Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor

Chansup Byun, Jeremy Kepner, William Arcand, David Bestor, Bill Bergeron, Vijay Gadepally, Michael Houle, Matthew Hubbell, Michael Jones, Anna Klein, Peter Michaleas, Lauren Milechin, Julie Mullen, Andrew Prout, Antonio Rosa, Siddharth Samsi, Charles Yee, Albert Reuther

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Tags: Benchmarking, Caffe, Computer science, Deep learning, Intel Xeon Phi, Machine learning, Performance

July 14, 2017 by hgpu

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

Jiazhen Gu, Huan Liu, Yangfan Zhou, Xin Wang

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Suffix trees

July 14, 2017 by hgpu

A Similarity Measure for GPU Kernel Subgraph Matching

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K80, Tesla M40, Tesla P100

July 14, 2017 by hgpu

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Siddharth Samsi, Brian Helfer, Jeremy Kepner, Albert Reuther, Darrell O. Ricke

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Tags: Algorithms, Computer science, CUDA, Genomics, Linear Algebra, Matrix multiplication, nVidia, Performance, Tesla K80

July 5, 2017 by hgpu

Parle: parallelizing stochastic gradient descent

Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar

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Tags: Algorithms, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Performance, Torch

July 5, 2017 by hgpu

An Analysis of Variation Between Cores For Intel Xeon Phi Knights Corner And Xeon Phi Knights Landing

Jamar Robinson

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Tags: Computer science, Intel Xeon Phi, Performance, Thesis

June 25, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

Speedup and Parallelization Models for Energy-Efficient Many-Core Systems Using Performance Counters

Mohammed A. Noaman Al-hayanni, Rishad Shafik, Ashur Rafiev, Fei Xia, Alex Yakovlev

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

June 5, 2017 by hgpu

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Konstantinos Krommydas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Performance, Tesla K20, Thesis

May 9, 2017 by hgpu

TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems

Bita Darvish Rouhani, Azalia Mirhoseini, Farinaz Koushanfar

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Tags: ARM, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TK1, Performance

May 2, 2017 by hgpu

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Rodinia Benchmarks for DPC++

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

DCA++: a state of the art implementation of the dynamical cluster approximation

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

tntorch - Tensor Network Learning with PyTorch

tntorch: Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

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