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Mixed precision in Graphics Processing Unit

Quentin Gallouédec

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Tags: Computer science, Machine learning, Mixed precision, Neural networks, nVidia, Tesla V100

October 31, 2021 by hgpu

TorchAudio: Building Blocks for Audio and Speech Processing

Yao-Yuan Yang, Moto Hira, Zhaoheng Ni, Anjali Chourdia, Artyom Astafurov, Caroline Chen, Ching-Feng Yeh, Christian Puhrsch, David Pollack, Dmitriy Genzel, Donny Greenberg, Edward Z. Yang, Jason Lian, Jay Mahadeokar, Jeff Hwang, Ji Chen, Peter Goldsborough, Prabhat Roy, Sean Narenthiran, Shinji Watanabe, Soumith Chintala, Vincent Quenneville-Bélair, Yangyang Shi

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia A100, Package, Python, PyTorch, Speech recognition

October 31, 2021 by hgpu

OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems

Nawras Alnaasan, Arpan Jain, Aamir Shafi, Hari Subramoni, Dhabaleswar K Panda

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Tags: Benchmarking, Computer science, CUDA, Deep learning, HPC, Machine learning, MPI, nVidia, Python, Tesla V100

October 24, 2021 by hgpu

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

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

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