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Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Akio Hayakawa, Masato Ishii, Yoshiyuki Kobayashi, Akira Nakamura, Takuya Narihira, Yukio Obuchi, Andrew Shin, Takuya Yashima, Kazuki Yoshiyama

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, Package, Tesla V100

March 7, 2021 by hgpu

Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware

Eva Papadogiannaki, Sotiris Ioannidis

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Tags: Computer science, Filtering, Heterogeneous systems, Intel UHD 630, nVidia, nVidia GeForce GTX 980, OpenCL, Security

February 28, 2021 by hgpu

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

Jaemin Choi, Zane Fink, Sam White, Nitin Bhat, David F. Richards, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Python, Tesla V100

February 28, 2021 by hgpu

Using hardware performance counters to speed up autotuning convergence on GPUs

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, OpenCL, Package, Performance, Vulkan

February 23, 2021 by hgpu

Optimal program variant generation for hybrid manycore systems

Cristian Urlea

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, OpenCL, performance portability, Thesis

February 21, 2021 by hgpu

A Newcomer In The PGAS World – UPC++ vs UPC: A Comparative Study

Jérémie Lagravière, Johannes Langguth, Martina Prugger, Phuong H. Ha, Xing Cai

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Tags: Computer science, Differential equations, Distributed data structures, Intel Xeon Phi, Package

February 21, 2021 by hgpu

A Survey of Machine Learning for Computer Architecture and Systems

Nan Wu, Yuan Xie

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Tags: Computer science, Machine learning, Review, Security

February 21, 2021 by hgpu

Serverless Computing Strategies on Cloud Platforms

Diana María Naranjo Delgado

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Tags: Cloud, Computer science, CUDA, Machine learning, nVidia, Tesla V100, Thesis, Virtualization

February 14, 2021 by hgpu

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Tomasz Boiński, Paweł Czarnul

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Tesla K20

February 14, 2021 by hgpu

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 2021 by hgpu

Transparent FPGA Acceleration with TensorFlow

Simon Pfenning, Philipp Holzinger, Marc Reichenbach

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Tags: Computer science, Deep learning, FPGA, Hardware Architecture, HSA, OpenCL, Package, TensorFlow

February 14, 2021 by hgpu

Dependable Embedded Systems

Jörg Henkel, Nikil Dutt (Editors)

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Tags: Book, Computer science, FPGA, OpenCL

February 7, 2021 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

Applications

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

Using hardware performance counters to speed up autotuning convergence on GPUs

Optimal program variant generation for hybrid manycore systems

A Newcomer In The PGAS World – UPC++ vs UPC: A Comparative Study

A Survey of Machine Learning for Computer Architecture and Systems

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Transparent FPGA Acceleration with TensorFlow

Dependable Embedded Systems

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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