14,033 research outputs found
Workload-aware Automatic Parallelization for Multi-GPU DNN Training
Deep neural networks (DNNs) have emerged as successful solutions for variety
of artificial intelligence applications, but their very large and deep models
impose high computational requirements during training. Multi-GPU
parallelization is a popular option to accelerate demanding computations in DNN
training, but most state-of-the-art multi-GPU deep learning frameworks not only
require users to have an in-depth understanding of the implementation of the
frameworks themselves, but also apply parallelization in a straight-forward way
without optimizing GPU utilization. In this work, we propose a workload-aware
auto-parallelization framework (WAP) for DNN training, where the work is
automatically distributed to multiple GPUs based on the workload
characteristics. We evaluate WAP using TensorFlow with popular DNN benchmarks
(AlexNet and VGG-16), and show competitive training throughput compared with
the state-of-the-art frameworks, and also demonstrate that WAP automatically
optimizes GPU assignment based on the workload's compute requirements, thereby
improving energy efficiency.Comment: This paper is accepted in ICASSP201
Importance mixing: Improving sample reuse in evolutionary policy search methods
Deep neuroevolution, that is evolutionary policy search methods based on deep
neural networks, have recently emerged as a competitor to deep reinforcement
learning algorithms due to their better parallelization capabilities. However,
these methods still suffer from a far worse sample efficiency. In this paper we
investigate whether a mechanism known as "importance mixing" can significantly
improve their sample efficiency. We provide a didactic presentation of
importance mixing and we explain how it can be extended to reuse more samples.
Then, from an empirical comparison based on a simple benchmark, we show that,
though it actually provides better sample efficiency, it is still far from the
sample efficiency of deep reinforcement learning, though it is more stable
Efficient and versatile data analytics for deep networks
Deep networks (DN) perform cognitive tasks related with image and text at human-level. To extract and exploit the knowledge coded within these networks we propose a framework which combines state-of-the-art technology in parallelization, storage and analysis. Our goal, to make DN models available to all data scientists
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Deep Neural Networks (DNNs) are becoming an important tool in modern
computing applications. Accelerating their training is a major challenge and
techniques range from distributed algorithms to low-level circuit design. In
this survey, we describe the problem from a theoretical perspective, followed
by approaches for its parallelization. We present trends in DNN architectures
and the resulting implications on parallelization strategies. We then review
and model the different types of concurrency in DNNs: from the single operator,
through parallelism in network inference and training, to distributed deep
learning. We discuss asynchronous stochastic optimization, distributed system
architectures, communication schemes, and neural architecture search. Based on
those approaches, we extrapolate potential directions for parallelism in deep
learning
Single stream parallelization of generalized LSTM-like RNNs on a GPU
Recurrent neural networks (RNNs) have shown outstanding performance on
processing sequence data. However, they suffer from long training time, which
demands parallel implementations of the training procedure. Parallelization of
the training algorithms for RNNs are very challenging because internal
recurrent paths form dependencies between two different time frames. In this
paper, we first propose a generalized graph-based RNN structure that covers the
most popular long short-term memory (LSTM) network. Then, we present a
parallelization approach that automatically explores parallelisms of arbitrary
RNNs by analyzing the graph structure. The experimental results show that the
proposed approach shows great speed-up even with a single training stream, and
further accelerates the training when combined with multiple parallel training
streams.Comment: Accepted by the 40th IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP) 201
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