We introduce a new, high-throughput, synchronous, distributed, data-parallel,
stochastic-gradient-descent learning algorithm. This algorithm uses amortized
inference in a compute-cluster-specific, deep, generative, dynamical model to
perform joint posterior predictive inference of the mini-batch gradient
computation times of all worker-nodes in a parallel computing cluster. We show
that a synchronous parameter server can, by utilizing such a model, choose an
optimal cutoff time beyond which mini-batch gradient messages from slow workers
are ignored that maximizes overall mini-batch gradient computations per second.
In keeping with earlier findings we observe that, under realistic conditions,
eagerly discarding the mini-batch gradient computations of stragglers not only
increases throughput but actually increases the overall rate of convergence as
a function of wall-clock time by virtue of eliminating idleness. The principal
novel contribution and finding of this work goes beyond this by demonstrating
that using the predicted run-times from a generative model of cluster worker
performance to dynamically adjust the cutoff improves substantially over the
static-cutoff prior art, leading to, among other things, significantly reduced
deep neural net training times on large computer clusters.
%0 Generic
%1 TenWoo18High
%A Teng, Michael
%A Wood, Frank
%D 2018
%K HPC SGD distributed workload_balance
%T High Throughput Synchronous Distributed Stochastic Gradient Descent
%U http://arxiv.org/abs/1803.04209
%X We introduce a new, high-throughput, synchronous, distributed, data-parallel,
stochastic-gradient-descent learning algorithm. This algorithm uses amortized
inference in a compute-cluster-specific, deep, generative, dynamical model to
perform joint posterior predictive inference of the mini-batch gradient
computation times of all worker-nodes in a parallel computing cluster. We show
that a synchronous parameter server can, by utilizing such a model, choose an
optimal cutoff time beyond which mini-batch gradient messages from slow workers
are ignored that maximizes overall mini-batch gradient computations per second.
In keeping with earlier findings we observe that, under realistic conditions,
eagerly discarding the mini-batch gradient computations of stragglers not only
increases throughput but actually increases the overall rate of convergence as
a function of wall-clock time by virtue of eliminating idleness. The principal
novel contribution and finding of this work goes beyond this by demonstrating
that using the predicted run-times from a generative model of cluster worker
performance to dynamically adjust the cutoff improves substantially over the
static-cutoff prior art, leading to, among other things, significantly reduced
deep neural net training times on large computer clusters.
@misc{TenWoo18High,
abstract = {We introduce a new, high-throughput, synchronous, distributed, data-parallel,
stochastic-gradient-descent learning algorithm. This algorithm uses amortized
inference in a compute-cluster-specific, deep, generative, dynamical model to
perform joint posterior predictive inference of the mini-batch gradient
computation times of all worker-nodes in a parallel computing cluster. We show
that a synchronous parameter server can, by utilizing such a model, choose an
optimal cutoff time beyond which mini-batch gradient messages from slow workers
are ignored that maximizes overall mini-batch gradient computations per second.
In keeping with earlier findings we observe that, under realistic conditions,
eagerly discarding the mini-batch gradient computations of stragglers not only
increases throughput but actually increases the overall rate of convergence as
a function of wall-clock time by virtue of eliminating idleness. The principal
novel contribution and finding of this work goes beyond this by demonstrating
that using the predicted run-times from a generative model of cluster worker
performance to dynamically adjust the cutoff improves substantially over the
static-cutoff prior art, leading to, among other things, significantly reduced
deep neural net training times on large computer clusters.},
added-at = {2018-08-24T08:52:55.000+0200},
author = {Teng, Michael and Wood, Frank},
biburl = {https://www.bibsonomy.org/bibtex/297a75e8839f7bdc81c4e5cd5d61d1ac2/loroch},
interhash = {097f76b511fef2d6ca3a31268cd0678a},
intrahash = {97a75e8839f7bdc81c4e5cd5d61d1ac2},
keywords = {HPC SGD distributed workload_balance},
timestamp = {2018-08-24T08:52:55.000+0200},
title = {High Throughput Synchronous Distributed Stochastic Gradient Descent},
url = {http://arxiv.org/abs/1803.04209},
year = 2018
}