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Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

Tsung-Wei Huang, Dian-Lun Lin, Chun-Xun Lin, Yibo Lin

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia GeForce GTX 2080, Package

September 26, 2021 by hgpu

IgNet. A Super-precise Convolutional Neural Network

Igor Mackarov

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Tags: Computer science, Machine learning, Neural networks, OpenCL

September 26, 2021 by hgpu

An Experimental Study of SYCL Task Graph Parallelism for Large-Scale Machine Learning Workloads

Cheng-Hsiang Chiu, Dian-Lun Lin, Tsung-Wei Huang

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 2080, SYCL

September 26, 2021 by hgpu

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather

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Tags: Artificial intelligence, Benchmarking, Compilers, Computer science, CUDA, Machine learning, nVidia, Package, Performance

September 26, 2021 by hgpu

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

Tian Lan, Sunil Srinivasa, Stephan Zheng

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Python, Tesal V100

September 5, 2021 by hgpu

A Survey of Big Data, High Performance Computing, and Machine Learning Benchmarks

Nina Ihde, Paula Marten, Ahmed Eleliemy, Gabrielle Poerwawinata, Pedro Silva, Ilin Tolovski, Florina M. Ciorba, Tilmann Rabl

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Tags: Benchmarking, Computer science, HPC, Machine learning, survey

August 29, 2021 by hgpu

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

Enrico Tabanelli, Giuseppe Tagliavini, Luca Benini

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Tags: Computer science, FPGA, Machine learning, Neural networks, nVidia, Package, SoC, Tesla A100

July 25, 2021 by hgpu

GPTPU: Accelerating Applications using Edge Tensor Processing Units

Kuan-Chieh Hsu, Hung-Wei Tseng

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, OpenCL, TPU

July 18, 2021 by hgpu

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia A100, nVidia DGX-A100, Package, Tesla V100

July 11, 2021 by hgpu

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Dominik Ernst, Georg Hager, Markus Holzer, Matthias Knorr, Gerhard Wellein

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Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, Performance, Stencil computation, Tesla V100

July 11, 2021 by hgpu

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Devi Abhiseshu Adurti, Mohit Battu

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 1050 Ti, Thesis

July 4, 2021 by hgpu

HALF: Holistic Auto Machine Learning for FPGAs

Jonas Ney, Dominik Loroch, Vladimir Rybalkin, Nico Weber, Jens Krüger, Norbert Wehn

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Tags: Computer science, Design space exploration, FPGA, HLS, Machine learning, Neural networks

July 4, 2021 by hgpu

HLS Portability from Intel to Xilinx: A Case Study

HEXL: Intel Homomorphic Encryption Acceleration Library

Accelerating Encrypted Computing on Intel GPUs

Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

CompilerGym: a library performant reinforcement learning environments for compiler tasks

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

HPCToolkit

Measurement and Analysis of GPU-accelerated Applications with HPCToolkit

NVPTX-SPIR-V Translator

Supporting CUDA for an extended RISC-V GPU architecture

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high performance computing on graphics processing units: hgpu.org

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

IgNet. A Super-precise Convolutional Neural Network

An Experimental Study of SYCL Task Graph Parallelism for Large-Scale Machine Learning Workloads

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

A Survey of Big Data, High Performance Computing, and Machine Learning Benchmarks

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

GPTPU: Accelerating Applications using Edge Tensor Processing Units

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

HALF: Holistic Auto Machine Learning for FPGAs

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