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and return bi to the server. The server only waits for the results from a subset of workers, before
recovering the final output using certain decoding functions.
For this problem, we can find an optimal computation strategy that achieves the minimum
possible recovery threshold, while allowing efficient decoding at the master node. We summarize
this result in the following theorems:
Theorem 5.5. For an n-dimensional distributed Fourier transform problem using N workers, if
each worker can store and process 1m
fraction of the q inputs, we can achieve the following recovery
threshold
K = m. (5.33)
Furthermore, the above recovery threshold can be achieved by a computation strategy, which allows
efficient decoding at the master node, i.e., with a complexity that scales linearly with respect to the
size s of the input data.
Moreover, we prove the optimally of our proposed computation strategy, which is formally stated
in the following theorem.
Theorem 5.6. In an n-dimensional distributed Fourier transform environment with N workers
that each can store and process 1m
fraction of the input vector, the following recovery threshold
K = m (5.34)
is optimal when the base field F is finite.4
5.5.2 General Description of Coded FFT with Multiple Inputs
We prove Theorem 5.5 by proposing an optimal computation strategy that achieves the recovery
threshold K = m. First of all we interleave the q inputs into smaller tensors. Specifically, given that
m|qs, we can find integers ˜ m,m0,m1, ...,mn−1 2 N, such that ˜m|q and mk|sk for each k 2 {0, 1, ..., n}.
For each input tensor th and each tuple (i0, i1, ..., in−1) satisfying ik 2 {0, 1, ...,mk − 1}, we define a
tensor ch,i0i1...in−1 with dimension s0
m0
× s1
m1
× ... × sn−1
mn−1 , with the following elements:
ch,i0i1,...in−1,j0j1,...jn−1 = th,(i0+j0m)(i1+j1m)...(in−1+jn−1m). (5.35)
4Similar to the single input case, this optimally can be generalized to base fields with infinite cardinally, by taking
into account of some practical implementation constrains.
63
Object Description
| Title | Coded computing: a transformative framework for resilient, secure, private, and communication efficient large scale distributed computing |
| Author | Yu, Qian |
| Author email | qyu880@usc.edu;qianyu0929@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2020-03-24 |
| Date submitted | 2020-08-04 |
| Date approved | 2020-08-05 |
| Restricted until | 2020-08-05 |
| Date published | 2020-08-05 |
| Advisor (committee chair) | Avestimehr, Salman |
| Advisor (committee member) |
Luo, HaiPeng Ortega, Antonio Soltanolkotabi, Mahdi |
| Abstract | Modern computing applications often require handling massive amounts of data in a distributed setting, where significant issues on resiliency, security, or privacy could arise. This dissertation presents new computing designs and optimality proofs, that address these issues through coding and information-theoretic approaches. ❧ The first part of this thesis focuses on a standard setup, where the computation is carried out using a set of worker nodes, each can store and process a fraction of the input dataset. The goal is to find computation schemes for providing the optimal resiliency against stragglers given the computation task, the number of workers, and the functions computed by the workers. The resiliency is measured in terms of the recovery threshold, defined as the minimum number of workers to wait for in order to compute the final result. We propose optimal solutions for broad classes of computation tasks, from basic building blocks such as matrix multiplication (entangled polynomial codes), Fourier transform (coded FFT), and convolution (polynomial code), to general functions such as multivariate polynomial evaluation (Lagrange coded computing). We develop optimal computing strategies by introducing a general coding framework called “polynomial coded computing”, to exploit the algebraic structure of the computation task and create computation redundancy in a novel coded form across workers. Polynomial coded computing allows for order-wise improvements over the state of the arts and significantly generalizes classical coding-theoretic results to go beyond linear computations. The encoding and decoding process of polynomial coded computing designs can be mapped to polynomial evaluation and interpolation, which can be computed efficiently. ❧ Then we show that polynomial coded computing can be extended to provide unified frameworks that also enable security and privacy in the computation. We present the optimal designs for three important problems: distributed matrix multiplication, multivariate polynomial evaluation, and gradient-type computation. We prove their optimality by developing information-theoretic and linear-algebraic converse bounding techniques. ❧ Finally, we consider the problem of coding for communication reduction. In the context of distributed computation, we focus on a MapReduce-type framework, where the workers need to shuffle their intermediate results to finish the computation. We aim to understand how to optimally exploit extra computing power to reduce communication, i.e., to establish a fundamental tradeoff between computation and communication. We prove a lower bound on the needed communication load for general allocation of the task assignments, by introducing a novel information-theoretic converse bounding approach. The presented lower bound exactly matches the inverse-proportional coding gain achieved by coded distributed computing schemes, completely characterizing the optimal computation-communication tradeoff. The proposed converse bounding approach strictly improves conventional cut-set bounds and can be widely applied to prove exact optimally results for more general settings, as well as more classical communication problems. We also investigate a problem called coded caching, where a single server is connected to multiple users in a cache network through a shared bottleneck link. Each user has an isolated memory that can be used to prefetch content. Then the server needs to deliver users’ demands efficiently in a following delivery phase. We propose caching and delivery designs that improve the state-of-the-art schemes under both centralized and decentralized settings, for both peak and average communication rates. Moreover, by developing information-theoretic bounds, we prove the proposed designs are exactly optimal among all schemes that use uncoded prefetching, and optimal within a factor of 2.00884 among schemes with coded prefetching. |
| Keyword | coding theory; information theory; distributed computing; security; privacy; matrix multiplication; caching |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Yu, Qian |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-YuQian-8883.pdf |
| Archival file | Volume13/etd-YuQian-8883.pdf |
Description
| Title | Page 78 |
| Full text | and return bi to the server. The server only waits for the results from a subset of workers, before recovering the final output using certain decoding functions. For this problem, we can find an optimal computation strategy that achieves the minimum possible recovery threshold, while allowing efficient decoding at the master node. We summarize this result in the following theorems: Theorem 5.5. For an n-dimensional distributed Fourier transform problem using N workers, if each worker can store and process 1m fraction of the q inputs, we can achieve the following recovery threshold K = m. (5.33) Furthermore, the above recovery threshold can be achieved by a computation strategy, which allows efficient decoding at the master node, i.e., with a complexity that scales linearly with respect to the size s of the input data. Moreover, we prove the optimally of our proposed computation strategy, which is formally stated in the following theorem. Theorem 5.6. In an n-dimensional distributed Fourier transform environment with N workers that each can store and process 1m fraction of the input vector, the following recovery threshold K = m (5.34) is optimal when the base field F is finite.4 5.5.2 General Description of Coded FFT with Multiple Inputs We prove Theorem 5.5 by proposing an optimal computation strategy that achieves the recovery threshold K = m. First of all we interleave the q inputs into smaller tensors. Specifically, given that m|qs, we can find integers ˜ m,m0,m1, ...,mn−1 2 N, such that ˜m|q and mk|sk for each k 2 {0, 1, ..., n}. For each input tensor th and each tuple (i0, i1, ..., in−1) satisfying ik 2 {0, 1, ...,mk − 1}, we define a tensor ch,i0i1...in−1 with dimension s0 m0 × s1 m1 × ... × sn−1 mn−1 , with the following elements: ch,i0i1,...in−1,j0j1,...jn−1 = th,(i0+j0m)(i1+j1m)...(in−1+jn−1m). (5.35) 4Similar to the single input case, this optimally can be generalized to base fields with infinite cardinally, by taking into account of some practical implementation constrains. 63 |
