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Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

C. Jiang, D. Ojika, T. Kurth, Prabhat, S. Vallecorsa, B. Patel, H. Lam

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks

May 26, 2019 by hgpu

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones

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Tags: ARM, Benchmarking, Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia Jetson TX2, OpenCV, Package

May 26, 2019 by hgpu

A Hybrid Framework for Fast and Accurate GPU Performance Estimation through Source-Level Analysis and Trace-Based Simulation

Xiebing Wang, Kai Huang, Alois Knoll, Xuehai Qian

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Tags: Computer science, nVidia, nVidia GeForce GTX 645, nVidia GeForce GTX 940 M, nVidia Quadro K600, nVidia Quadro K620, OpenCL, Performance

May 26, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Yonghae Kim, Hyesoon Kim

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Tags: Code generation, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, OpenCL

May 23, 2019 by hgpu

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Yehia Arafa, Abdel-Hameed Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan Eidenbenz

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Titan RTX, Performance, PTX, Tesla K40, Tesla P100, Tesla V100

May 23, 2019 by hgpu

Neural Query Language: A Knowledge Base Query Language for Tensorflow

William W. Cohen, Matthew Siegler, Alex Hofer

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Tags: Computer science, Databases, Deep learning, Package, Performance

May 19, 2019 by hgpu

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

Karan Aggarwal, Uday Bondhugula, Varsha Sreenivasan, Devarajan Sridharan

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Tags: Algorithms, Computer science, CUDA, Neuroscience, nVidia, nVidia GeForce RTX 2080 Ti, Sparse matrix

May 19, 2019 by hgpu

Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, OpenCL

May 19, 2019 by hgpu

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

Daeyeon Kim

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Tags: Computer science, Deep learning, FPGA, Neural networks, OpenCL, Performance, Thesis

May 19, 2019 by hgpu

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

Benjamin Ferrell, Jun Duan, Kevin W. Hamlen

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, PTX

May 15, 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

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

A Hybrid Framework for Fast and Accurate GPU Performance Estimation through Source-Level Analysis and Trace-Based Simulation

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Neural Query Language: A Knowledge Base Query Language for Tensorflow

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

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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