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AI Benchmark: All About Deep Learning on Smartphones in 2019

Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool

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Tags: Android, Benchmarking, Computer science, CUDA, Deep learning, nVidia, OpenCL, Overview

October 20, 2019 by hgpu

Blink: Fast and Generic Collectives for Distributed ML

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, Machine learning, nVidia, nVidia DGX-1, Tesla V100

October 20, 2019 by hgpu

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

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Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Hamid Reza Zohouri, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, FPGA, HLS, Memory, nVidia, OpenCL, Package, Tesla K20, Tesla V100

October 20, 2019 by hgpu

Characterizing Deep Learning Training Workloads on Alibaba-PAI

Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia

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Tags: Artificial intelligence, Computer science, Computer vision, Deep learning, GPU cluster, nVidia, Tesla V100

October 20, 2019 by hgpu

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Ameer M.S. Abdelhadi, Christos-Savvas Bouganis, George A. Constantinides

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Tags: Computer science, FPGA, Information Retrieval, Machine learning, Nearest neighbour, Package

October 13, 2019 by hgpu

Performance Evaluation of Blocking and NonBlocking Concurrent Queues on GPUs

Hossein Pourmeidani

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Tags: AMD Radeon R7, ATI, Benchmarking, Computer science, OpenCL, Performance, Thesis

October 13, 2019 by hgpu

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Valentin Radu, Kuba Kaszyk, Yuan Wen, Jack Turner, Jose Cano, Elliot J. Crowley, Bjorn Franke, Amos Storkey, Michael O'Boyle

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia Jetson TX2, OpenCL

October 13, 2019 by hgpu

hlslib: Software Engineering for Hardware Design

Johannes de Fine Licht, Torsten Hoefler

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Tags: Computer science, FPGA, OpenCL, Package, Software Engineering

October 13, 2019 by hgpu

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Oded Green, James Fox, Jeffrey Young, Jun Shirako, David Bader

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Performance, Sparse, Tesla P100, Tesla V100

October 13, 2019 by hgpu

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, Frédo Durand

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Tags: 3D Graphics and Realism, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 1080 Ti, Package, Rendering, Tesla V100

October 6, 2019 by hgpu

Verification of GPU Program Optimizations in Lean

Björn Fischer

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Tags: Computer science, High-level Languages, Package, Thesis

October 6, 2019 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

Applications

AI Benchmark: All About Deep Learning on Smartphones in 2019

Blink: Fast and Generic Collectives for Distributed ML

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Characterizing Deep Learning Training Workloads on Alibaba-PAI

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Performance Evaluation of Blocking and NonBlocking Concurrent Queues on GPUs

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

hlslib: Software Engineering for Hardware Design

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Verification of GPU Program Optimizations in Lean

Recent source codes

XaaS containers

microSYCL: SYCL micro-benchmarks repository

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

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