768 research outputs found
Latency Analysis of Coded Computation Schemes over Wireless Networks
Large-scale distributed computing systems face two major bottlenecks that
limit their scalability: straggler delay caused by the variability of
computation times at different worker nodes and communication bottlenecks
caused by shuffling data across many nodes in the network. Recently, it has
been shown that codes can provide significant gains in overcoming these
bottlenecks. In particular, optimal coding schemes for minimizing latency in
distributed computation of linear functions and mitigating the effect of
stragglers was proposed for a wired network, where the workers can
simultaneously transmit messages to a master node without interference. In this
paper, we focus on the problem of coded computation over a wireless
master-worker setup with straggling workers, where only one worker can transmit
the result of its local computation back to the master at a time. We consider 3
asymptotic regimes (determined by how the communication and computation times
are scaled with the number of workers) and precisely characterize the total
run-time of the distributed algorithm and optimum coding strategy in each
regime. In particular, for the regime of practical interest where the
computation and communication times of the distributed computing algorithm are
comparable, we show that the total run-time approaches a simple lower bound
that decouples computation and communication, and demonstrate that coded
schemes are times faster than uncoded schemes
Summary of the functions and capabilities of the structural analysis system computer program
Functions and operations of structural analysis system computer progra
Timely-Throughput Optimal Coded Computing over Cloud Networks
In modern distributed computing systems, unpredictable and unreliable
infrastructures result in high variability of computing resources. Meanwhile,
there is significantly increasing demand for timely and event-driven services
with deadline constraints. Motivated by measurements over Amazon EC2 clusters,
we consider a two-state Markov model for variability of computing speed in
cloud networks. In this model, each worker can be either in a good state or a
bad state in terms of the computation speed, and the transition between these
states is modeled as a Markov chain which is unknown to the scheduler. We then
consider a Coded Computing framework, in which the data is possibly encoded and
stored at the worker nodes in order to provide robustness against nodes that
may be in a bad state. With timely computation requests submitted to the system
with computation deadlines, our goal is to design the optimal computation-load
allocation scheme and the optimal data encoding scheme that maximize the timely
computation throughput (i.e, the average number of computation tasks that are
accomplished before their deadline). Our main result is the development of a
dynamic computation strategy called Lagrange Estimate-and Allocate (LEA)
strategy, which achieves the optimal timely computation throughput. It is shown
that compared to the static allocation strategy, LEA increases the timely
computation throughput by 1.4X - 17.5X in various scenarios via simulations and
by 1.27X - 6.5X in experiments over Amazon EC2 clustersComment: to appear in MobiHoc 201
GCSA Codes with Noise Alignment for Secure Coded Multi-Party Batch Matrix Multiplication
A secure multi-party batch matrix multiplication problem (SMBMM) is
considered, where the goal is to allow a master to efficiently compute the
pairwise products of two batches of massive matrices, by distributing the
computation across S servers. Any X colluding servers gain no information about
the input, and the master gains no additional information about the input
beyond the product. A solution called Generalized Cross Subspace Alignment
codes with Noise Alignment (GCSA-NA) is proposed in this work, based on
cross-subspace alignment codes. The state of art solution to SMBMM is a coding
scheme called polynomial sharing (PS) that was proposed by Nodehi and
Maddah-Ali. GCSA-NA outperforms PS codes in several key aspects - more
efficient and secure inter-server communication, lower latency, flexible
inter-server network topology, efficient batch processing, and tolerance to
stragglers. The idea of noise alignment can also be combined with N-source
Cross Subspace Alignment (N-CSA) codes and fast matrix multiplication
algorithms like Strassen's construction. Moreover, noise alignment can be
applied to symmetric secure private information retrieval to achieve the
asymptotic capacity
Recoverable Information and Emergent Conservation Laws in Fracton Stabilizer Codes
We introduce a new quantity, that we term recoverable information, defined
for stabilizer Hamiltonians. For such models, the recoverable information
provides a measure of the topological information, as well as a physical
interpretation, which is complementary to topological entanglement entropy. We
discuss three different ways to calculate the recoverable information, and
prove their equivalence. To demonstrate its utility, we compute recoverable
information for fracton models using all three methods where appropriate. From
the recoverable information, we deduce the existence of emergent
Gauss-law type constraints, which in turn imply emergent conservation
laws for point-like quasiparticle excitations of an underlying topologically
ordered phase.Comment: Added additional cluster model calculation (SPT example) and a new
section discussing the general benefits of recoverable informatio
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