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Found 5 papers, 0 papers with code
GPU Cluster Scheduling for Network-Sensitive Deep Learning
We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the anticipated communication-network delays.
Analysis of Distributed Deep Learning in the Cloud
We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud.
Machine Learning: Algorithms, Models, and Applications
The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed.
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs
By exploiting sparsity in both the forward and backward passes, speedup improvements range from 1. 68$\times$ to 3. 30$\times$ over the sparsity-agnostic baseline execution.
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning tasks in text and speech.
