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Methods for Accelerating Machine Learning in High Performance Computing

Robert Lim

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla P100, Thesis

March 31, 2019 by hgpu

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

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Tags: Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX 1080, Tesla P100

March 10, 2019 by hgpu

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas, Timo Schneider, Torsten Hoefler

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Tags: Computer science, CUDA, FPGA, nVidia, OpenCL, Optimization, Performance, Tesla P100

March 3, 2019 by hgpu

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes

Peng Sun, Wansen Feng, Ruobing Han, Shengen Yan, Yonggang Wen

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Tags: Computer science, CUDA, Deep learning, GPU cluster, Neural networks, nVidia, OpenMPI, Performance, Tesla P100, Tesla V100

February 24, 2019 by hgpu

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

Peifeng Yu, Mosharaf Chowdhury

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100

February 17, 2019 by hgpu

Performance Evaluation of OpenMP’s Target Construct on GPUs: Exploring Compiler Optimizations

Akihiro Hayashi, Jun Shirako, Etorre Tiotto, Robert Ho, Vivek Sarkar

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Optimization, Tesla K80, Tesla P100

February 10, 2019 by hgpu

Heterogeneous Distributed Big Data Clustering on Sparse Grids

David Pfander, Gregor Daiss, Dirk Pfluger

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Tags: Clustering, Computer science, Data mining, Distributed computing, Heterogeneous systems, Machine learning, MPI, nVidia, OpenCL, Package, performance portability, Tesla P100

February 10, 2019 by hgpu

Hierarchical Matrix Operations on GPUs: Matrix-Vector Multiplication and Compression

Wajih Halim Boukaram, George Turkiyyah, David E. Keyes

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Tags: Compression, Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Tesla P100

February 10, 2019 by hgpu

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Zeyi Wen, Jiashuai Shi, Bingsheng He, Qinbin Li, Jian Chen

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Tags: Computer science, CUDA, Data mining, Machine learning, nVidia, Package, Python, Tesla P100

February 3, 2019 by hgpu

High Performance Algorithms for Counting Collisions and Pairwise Interactions

Matheus Henrique Junqueira Saldanha, Paulo Sérgio Lopes de Souza

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Tags: Collision detection, Computer science, CUDA, nVidia, Package, Tesla P100

February 3, 2019 by hgpu

BitCracker: BitLocker meets GPUs

Elena Agostini, Massimo Bernaschi

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Tags: AES, ATI, ATI Radeon HD 7990, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Security, Tesla K80, Tesla P100, Tesla V100

January 13, 2019 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

Methods for Accelerating Machine Learning in High Performance Computing

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

Performance Evaluation of OpenMP’s Target Construct on GPUs: Exploring Compiler Optimizations

Heterogeneous Distributed Big Data Clustering on Sparse Grids

Hierarchical Matrix Operations on GPUs: Matrix-Vector Multiplication and Compression

ThunderGBM: Fast GBDTs and Random Forests on GPUs

High Performance Algorithms for Counting Collisions and Pairwise Interactions

BitCracker: BitLocker meets GPUs

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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