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Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

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An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

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high performance computing on graphics processing units: hgpu.org

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Recent source codes

EASYPAP: a Framework for Learning Parallel Programming

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

StarNEig: A Task-based Library for Solving Nonsymmetric Eigenvalue Problems

FLAMEGPU: a high performance GPU extension to the FLAME framework

CommAnalyzer

elevate: implementation of the Elevate language

reproducibility of TensorFlow with ResNet

SCHNAPS

Interoperable GPU Kernels as Latency Improver for MEC

MNN: a lightweight deep neural network inference engine

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Featured events

8th International Workshop on OpenCL, including SYCLCon, 2019

7th International Workshop on OpenCL, 2019

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019