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ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Compilers, Heterogeneous systems, Image processing, Machine learning, nVidia, nVidia GeForce GTX 960, OpenCL, Package, performance portability, Tesla K40

January 13, 2018 by hgpu

ThunderSVM: A Fast SVM Library on GPUs and CPUs

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

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

January 6, 2018 by hgpu

A Generic Inverted Index Framework for Similarity Search on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Lubos Krcal, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Hashing, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, OpenMPI, Package

December 28, 2017 by hgpu

A Survey of FPGA Based Neural Network Accelerator

Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang

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Tags: Computer science, Computer vision, FPGA, Machine learning, Neural networks

December 28, 2017 by hgpu

An MPI-Based Python Framework for Distributed Training with Keras

Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

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Tags: Computer science, CUDA, Deep learning, Keras, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Python, Tesla K80

December 24, 2017 by hgpu

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Benchmarking, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Tela K40, Tesla K80, Tesla P100

December 24, 2017 by hgpu

A survey on graphic processing unit computing for large-scale data mining

Alberto Cano

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Tags: Computer science, CUDA, Data mining, Distributed computing, Machine learning, nVidia

November 21, 2017 by hgpu

Unified Deep Learning with CPU, GPU, and FPGA Technologies

Allen Rush, Ashish Sirasao, Mike Ignatowski

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Machine learning, OpenCL

November 21, 2017 by hgpu

Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning

Peter Boyle, Michael Chuvelev, Guido Cossu, Christopher Kelly, Christoph Lehner, Lawrence Meadows

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, MPI, OpenMP, Package, Performance, Physics

November 16, 2017 by hgpu

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Ole Martin Skafsa

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Tags: CNN, Computer science, Deep learning, FPGA, Machine learning, Neural networks, NLP, nVidia, nVidia Tegra TX1, OpenCL, Thesis

October 3, 2017 by hgpu

IBM Deep Learning Service

Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang

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Tags: Cloud, Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia

September 21, 2017 by hgpu

Empower Sequence Labeling with Task-Aware Neural Language Model

Liyuan Liu, Jingbo Shang, Frank Xu, Xiang Ren, Huan Gui, Jian Peng, Jiawei Han

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Tags: Computer science, Machine learning, Neural networks, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

September 16, 2017 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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