4,387 research outputs found
OverSketch: Approximate Matrix Multiplication for the Cloud
We propose OverSketch, an approximate algorithm for distributed matrix
multiplication in serverless computing. OverSketch leverages ideas from matrix
sketching and high-performance computing to enable cost-efficient
multiplication that is resilient to faults and straggling nodes pervasive in
low-cost serverless architectures. We establish statistical guarantees on the
accuracy of OverSketch and empirically validate our results by solving a
large-scale linear program using interior-point methods and demonstrate a 34%
reduction in compute time on AWS Lambda.Comment: Published in Proc. IEEE Big Data 2018. Updated version provides
details of distributed sketching and highlights other advantages of
OverSketc
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
For distributed computing environment, we consider the empirical risk
minimization problem and propose a distributed and communication-efficient
Newton-type optimization method. At every iteration, each worker locally finds
an Approximate NewTon (ANT) direction, which is sent to the main driver. The
main driver, then, averages all the ANT directions received from workers to
form a {\it Globally Improved ANT} (GIANT) direction. GIANT is highly
communication efficient and naturally exploits the trade-offs between local
computations and global communications in that more local computations result
in fewer overall rounds of communications. Theoretically, we show that GIANT
enjoys an improved convergence rate as compared with first-order methods and
existing distributed Newton-type methods. Further, and in sharp contrast with
many existing distributed Newton-type methods, as well as popular first-order
methods, a highly advantageous practical feature of GIANT is that it only
involves one tuning parameter. We conduct large-scale experiments on a computer
cluster and, empirically, demonstrate the superior performance of GIANT.Comment: Fixed some typos. Improved writin
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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