43,917 research outputs found
Near-Optimal Straggler Mitigation for Distributed Gradient Methods
Modern learning algorithms use gradient descent updates to train inferential
models that best explain data. Scaling these approaches to massive data sizes
requires proper distributed gradient descent schemes where distributed worker
nodes compute partial gradients based on their partial and local data sets, and
send the results to a master node where all the computations are aggregated
into a full gradient and the learning model is updated. However, a major
performance bottleneck that arises is that some of the worker nodes may run
slow. These nodes a.k.a. stragglers can significantly slow down computation as
the slowest node may dictate the overall computational time. We propose a
distributed computing scheme, called Batched Coupon's Collector (BCC) to
alleviate the effect of stragglers in gradient methods. We prove that our BCC
scheme is robust to a near optimal number of random stragglers. We also
empirically demonstrate that our proposed BCC scheme reduces the run-time by up
to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation
strategies. We also generalize the proposed BCC scheme to minimize the
completion time when implementing gradient descent-based algorithms over
heterogeneous worker nodes
Supersymmetric Quantum Mechanics for String-Bits
We develop possible versions of supersymmetric single particle quantum
mechanics, with application to superstring-bit models in view. We focus
principally on space dimensions , the transverse dimensionalities of
superstring in space-time dimensions. These are the cases for which
``classical'' superstring makes sense, and also the values of for which
Hooke's force law is compatible with the simplest superparticle dynamics. The
basic question we address is: When is it possible to replace such harmonic
force laws with more general ones, including forces which vanish at large
distances? This is an important question because forces between string-bits
that do not fall off with distance will almost certainly destroy cluster
decomposition. We show that the answer is affirmative for , negative for
, and so far inconclusive for .Comment: 17 pages, Late
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