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Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Negar Alizadeh, Fernando Castor

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, nVidia, nVidia GeForce RTX 3070, Package, PyTorch, TensorFlow

February 25, 2024 by hgpu

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

Hulin Dai, Xuan Peng, Xuanhua Shi, Ligang He, Qian Xiong, Hai Jin

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, PyTorch, TensorFlow, Tesla P100

January 9, 2022 by hgpu

DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications

Hariharan Devarajan, Huihuo Zheng, Anthony Kougkas, Xian-He Sun, Venkatram Vishwanath

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Tags: Benchmarking, Computer science, Deep learning, MPI, Neural networks, Package, TensorFlow

June 6, 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

FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver

Chirag Modi, Francois Lanusse, Uros Seljak

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Tags: Astrophysics, Cosmology, CUDA, N-body simulation, nVidia, Package, Physics, TensorFlow, Tesla V100

October 25, 2020 by hgpu

Mastering Atari with Discrete World Models

Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba

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Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, nVidia, Package, Python, TensorFlow, Tesla V100

October 11, 2020 by hgpu

Performance benchmarking of deep learning framework on Intel Xeon Phi

Chao-Tung Yang, Jung-Chun Liu, Yu-Wei Chan, Endah Kristiani, Chan-Fu Kuo

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Tags: Benchmarking, Caffe, Computer science, Deep learning, Image recognition, Intel Xeon Phi, MPI, OpenMP, Performance, TensorFlow

June 28, 2020 by hgpu

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

Behnam Pourghassemi, Chenghao Zhang, Joo Hwan Lee, Aparna Chandramowlishwaran

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, TensorFlow, Tesla K40

May 31, 2020 by hgpu

Neural Architecture Search for Lightweight Non-Local Networks

Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan Yuille

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

April 12, 2020 by hgpu

Performance evaluation of deep learning on smartphones

Abhishek Srivastava

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Tags: Benchmarking, Computer science, Deep learning, TensorFlow, Thesis

March 22, 2020 by hgpu

Non-Determinism in TensorFlow ResNets

Miguel Morin, Matthew Willetts

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Tags: Computer science, Deep learning, Machine learning, nVidia, Package, TensorFlow, Tesla M60

February 2, 2020 by hgpu

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Jocelyn Sunseri, David Ryan Koes

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Tags: Chemistry, Computer science, CUDA, Deep learning, Machine learning, Molecular modeling, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Package, PyTorch, TensorFlow

December 15, 2019 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

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

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

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

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

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high performance computing on graphics processing units: hgpu.org

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications

Transparent FPGA Acceleration with TensorFlow

FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver

Mastering Atari with Discrete World Models

Performance benchmarking of deep learning framework on Intel Xeon Phi

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

Neural Architecture Search for Lightweight Non-Local Networks

Performance evaluation of deep learning on smartphones

Non-Determinism in TensorFlow ResNets

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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