220 research outputs found
Some Applications of Coding Theory in Computational Complexity
Error-correcting codes and related combinatorial constructs play an important
role in several recent (and old) results in computational complexity theory. In
this paper we survey results on locally-testable and locally-decodable
error-correcting codes, and their applications to complexity theory and to
cryptography.
Locally decodable codes are error-correcting codes with sub-linear time
error-correcting algorithms. They are related to private information retrieval
(a type of cryptographic protocol), and they are used in average-case
complexity and to construct ``hard-core predicates'' for one-way permutations.
Locally testable codes are error-correcting codes with sub-linear time
error-detection algorithms, and they are the combinatorial core of
probabilistically checkable proofs
Improved Lower Bounds for Locally Decodable Codes and Private Information Retrieval
We prove new lower bounds for locally decodable codes and private information
retrieval. We show that a 2-query LDC encoding n-bit strings over an l-bit
alphabet, where the decoder only uses b bits of each queried position of the
codeword, needs code length m = exp(Omega(n/(2^b Sum_{i=0}^b {l choose i})))
Similarly, a 2-server PIR scheme with an n-bit database and t-bit queries,
where the user only needs b bits from each of the two l-bit answers, unknown to
the servers, satisfies t = Omega(n/(2^b Sum_{i=0}^b {l choose i})). This
implies that several known PIR schemes are close to optimal. Our results
generalize those of Goldreich et al. who proved roughly the same bounds for
linear LDCs and PIRs. Like earlier work by Kerenidis and de Wolf, our classical
lower bounds are proved using quantum computational techniques. In particular,
we give a tight analysis of how well a 2-input function can be computed from a
quantum superposition of both inputs.Comment: 12 pages LaTeX, To appear in ICALP '0
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
Complexity Theory
Computational Complexity Theory is the mathematical study of the intrinsic power and limitations of computational resources like time, space, or randomness. The current workshop focused on recent developments in various sub-areas including arithmetic complexity, Boolean complexity, communication complexity, cryptography, probabilistic proof systems, pseudorandomness and randomness extraction. Many of the developments are related to diverse mathematical fields such as algebraic geometry, combinatorial number theory, probability theory, representation theory, and the theory of error-correcting codes
A Study on the Impact of Locality in the Decoding of Binary Cyclic Codes
In this paper, we study the impact of locality on the decoding of binary
cyclic codes under two approaches, namely ordered statistics decoding (OSD) and
trellis decoding. Given a binary cyclic code having locality or availability,
we suitably modify the OSD to obtain gains in terms of the Signal-To-Noise
ratio, for a given reliability and essentially the same level of decoder
complexity. With regard to trellis decoding, we show that careful introduction
of locality results in the creation of cyclic subcodes having lower maximum
state complexity. We also present a simple upper-bounding technique on the
state complexity profile, based on the zeros of the code. Finally, it is shown
how the decoding speed can be significantly increased in the presence of
locality, in the moderate-to-high SNR regime, by making use of a quick-look
decoder that often returns the ML codeword.Comment: Extended version of a paper submitted to ISIT 201
Deterministic voting in distributed systems using error-correcting codes
Distributed voting is an important problem in reliable computing. In an N Modular Redundant (NMR) system, the N computational modules execute identical tasks and they need to periodically vote on their current states. In this paper, we propose a deterministic majority voting algorithm for NMR systems. Our voting algorithm uses error-correcting codes to drastically reduce the average case communication complexity. In particular, we show that the efficiency of our voting algorithm can be improved by choosing the parameters of the error-correcting code to match the probability of the computational faults. For example, consider an NMR system with 31 modules, each with a state of m bits, where each module has an independent computational error probability of 10^-3. In, this NMR system, our algorithm can reduce the average case communication complexity to approximately 1.0825 m compared with the communication complexity of 31 m of the naive algorithm in which every module broadcasts its local result to all other modules. We have also implemented the voting algorithm over a network of workstations. The experimental performance results match well the theoretical predictions
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