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Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

Robert Dietrich

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Tags: Computer science, CUDA, Data acquisition, Heterogeneous systems, MPI, nVidia, OpenACC, OpenCL, Tesla K80, Tesla V100, Thesis

December 1, 2019 by hgpu

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Guoping Long, Jun Yang, Wei Lin

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Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

December 1, 2019 by hgpu

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

November 24, 2019 by hgpu

Compiler-Driven Performance on Heterogeneous Computing Platforms

Artem Chikin

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, Performance, Tesla P100, Tesla V100, Thesis

November 17, 2019 by hgpu

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Aliakbar Panahi, Seyran Saeedi, Tom Arodz

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Tesla V100

November 17, 2019 by hgpu

Performance Evaluation of Advanced Features in CUDA Unified Memory

Steven W. D. Chien, Ivy B. Peng, Stefano Markidis

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1050, Package, Prefetch, Tesla V100

October 27, 2019 by hgpu

Blink: Fast and Generic Collectives for Distributed ML

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, Machine learning, nVidia, nVidia DGX-1, Tesla V100

October 20, 2019 by hgpu

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Hamid Reza Zohouri, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, FPGA, HLS, Memory, nVidia, OpenCL, Package, Tesla K20, Tesla V100

October 20, 2019 by hgpu

Characterizing Deep Learning Training Workloads on Alibaba-PAI

Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia

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Tags: Artificial intelligence, Computer science, Computer vision, Deep learning, GPU cluster, nVidia, Tesla V100

October 20, 2019 by hgpu

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Oded Green, James Fox, Jeffrey Young, Jun Shirako, David Bader

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Performance, Sparse, Tesla P100, Tesla V100

October 13, 2019 by hgpu

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, Frédo Durand

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Tags: 3D Graphics and Realism, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 1080 Ti, Package, Rendering, Tesla V100

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

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Tags: Computer science, Deep learning, Neural networks, nVidia, Package, Tesla V100

September 29, 2019 by hgpu

HPCToolkit

Refining HPCToolkit for application performance analysis at exascale

Open Catalog of Best Practices for Modernization and Optimization

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Exploring Scalability in C++ Parallel STL Implementations

GPU-Smash: Compression abstraction library

A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers

Noarr Structures

Abstractions for C++ code optimizations in parallel high-performance applications

$3D-fractal-generators$

HiCCL: A hierarchical collective communications library with portable optimizations

HiCCL: A Hierarchical Collective Communication Library

See all packages

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high performance computing on graphics processing units: hgpu.org

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Compiler-Driven Performance on Heterogeneous Computing Platforms

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Performance Evaluation of Advanced Features in CUDA Unified Memory

Blink: Fast and Generic Collectives for Distributed ML

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Characterizing Deep Learning Training Workloads on Alibaba-PAI

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Exascale Deep Learning for Scientific Inverse Problems

Recent source codes

HPCToolkit

Open Catalog of Best Practices for Modernization and Optimization

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

GPU-Smash: Compression abstraction library

Noarr Structures

3D-fractal-generators

SyncPerformance

VerCors: a verification toolset for verifying parallel and concurrent software

HiCCL: A hierarchical collective communications library with portable optimizations

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