A Linear Algebraic Approach to Model Parallelism in Deep Learning

• URL: http://arxiv.org/abs/2006.03108v1
• Date: Thu, 4 Jun 2020 19:38:05 GMT
• Title: A Linear Algebraic Approach to Model Parallelism in Deep Learning
• Authors: Russell J. Hewett and Thomas J. Grady II
• Abstract summary: Training deep neural networks (DNNs) in large-cluster computing environments is increasingly necessary, as networks grow in size and complexity. We propose a linear-algebraic approach to model parallelism in deep learning, which allows parallel distribution of any tensor in the DNN. We build distributed DNN layers using these parallel primitives, composed with sequential layer implementations, and demonstrate their application by building and training a distributed DNN using DistDL, a PyTorch and MPI-based distributed deep learning toolkit.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training deep neural networks (DNNs) in large-cluster computing environments is increasingly necessary, as networks grow in size and complexity. Local memory and processing limitations require robust data and model parallelism for crossing compute node boundaries. We propose a linear-algebraic approach to model parallelism in deep learning, which allows parallel distribution of any tensor in the DNN. Rather than rely on automatic differentiation tools, which do not universally support distributed memory parallelism models, we show that parallel data movement operations, e.g., broadcast, sum-reduce, and halo exchange, are linear operators, and by defining the relevant spaces and inner products, we manually develop the adjoint, or backward, operators required for gradient-based training of DNNs. We build distributed DNN layers using these parallel primitives, composed with sequential layer implementations, and demonstrate their application by building and training a distributed DNN using DistDL, a PyTorch and MPI-based distributed deep learning toolkit.

Related papers

• Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
  We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs) Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators. Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
  arXiv  Detail & Related papers  (2024-05-22T17:23:15Z)
• Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability [0.48733623015338234]
  We discuss a parallel extension of a widely used format for efficiently representing sparse matrices, the compressed sparse row (CSR) We contend that organizing additional network information, such as neuron and synapse state, in alignment with its adjacency as dCSR provides a straightforward partition-based distribution of network state. We provide a potential implementation, and put it forward for adoption within the neural computing community.
  arXiv  Detail & Related papers  (2023-04-12T03:19:06Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• On Optimizing the Communication of Model Parallelism [74.15423270435949]
  We study a novel and important communication pattern in large-scale model-parallel deep learning (DL) In cross-mesh resharding, a sharded tensor needs to be sent from a source device mesh to a destination device mesh. We propose two contributions to address cross-mesh resharding: an efficient broadcast-based communication system, and an "overlapping-friendly" pipeline schedule.
  arXiv  Detail & Related papers  (2022-11-10T03:56:48Z)
• An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks [0.3653697742557465]
  We analyze the compute, communication, and memory requirements of Convolutional Neural Networks (CNNs) We leverage our model-driven analysis to be the basis for an oracle utility which can help in detecting the limitations and bottlenecks of different parallelism approaches at scale.
  arXiv  Detail & Related papers  (2021-04-19T06:45:51Z)
• Parareal Neural Networks Emulating a Parallel-in-time Algorithm [1.988145627448243]
  As deep neural networks (DNNs) become deeper, the training time increases. In this paper, we introduce a novel methodology to construct a parallel neural network.
  arXiv  Detail & Related papers  (2021-03-16T02:03:39Z)
• BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training [9.551339069298011]
  BaPipe is a pipeline parallelism training framework for distributed deep learning. It automatically explores pipeline parallelism training methods and balanced partition strategies for distributed training. BaPipe provides up to 3.2x speedup and 4x memory reduction in various platforms.
  arXiv  Detail & Related papers  (2020-12-23T08:57:39Z)
• Parallel Training of Deep Networks with Local Updates [84.30918922367442]
  Local parallelism is a framework which parallelizes training of individual layers in deep networks by replacing global backpropagation with truncated layer-wise backpropagation. We show results in both vision and language domains across a diverse set of architectures, and find that local parallelism is particularly effective in the high-compute regime.
  arXiv  Detail & Related papers  (2020-12-07T16:38:45Z)
• Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference [15.720414948573753]
  We consider the parallel implementation of an already-trained deep model on multiple processing nodes (a.k.a. workers) We propose RePurpose, a layer-wise model restructuring and pruning technique that guarantees the performance of the overall parallelized model. We show that, compared to the existing methods, RePurpose significantly improves the efficiency of the distributed inference via parallel implementation.
  arXiv  Detail & Related papers  (2020-08-19T06:44:41Z)
• Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training [126.49572353148262]
  We study two factors in neural network training: data parallelism and sparsity. Despite their promising benefits, understanding of their effects on neural network training remains elusive.
  arXiv  Detail & Related papers  (2020-03-25T10:49:22Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.