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Theoretical and Applied Data Science Lunch-n-learn - Konstantinos Konstantinidis
Presenter: Konstantinos Konstantinidis
Title: Speeding Up Distributed Computing via Coding
Abstract: Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-called Shuffle phase can be a significant part of the overall execution time. Prior work has demonstrated a natural tradeoff between computation and communication whereby running redundant copies of jobs can reduce the Shuffle traffic load, thereby leading to reduced overall execution times. For a single job, the main drawback of this approach is that it requires the original job to be split into a number of files that grows exponentially in the system parameters. When extended to multiple jobs (with specific function types), these techniques suffer from a limitation of a similar flavor, i.e., they require an exponentially large number of jobs to be executed. In practical scenarios, these requirements can significantly reduce the promised gains of the method. In our work, we show that a class of combinatorial structures called resolvable designs can be used to develop efficient coded distributed computing schemes for both the single and multiple job scenarios considered in prior work. We present both theoretical analysis and exhaustive experimental results (on Amazon EC2 clusters) that demonstrate the performance advantages of our method. For the single and multiple job cases, we obtain speed-ups of 4.69x (and 2.6x over prior work) and 4.31x over the baseline approach, respectively.
Bio: Konstantinos Konstantinidis received the Diploma degree in electrical and computer engineering from the Technical University of Crete, Chania, Greece, in 2016. He is currently pursuing the Ph.D. degree in electrical and computer engineering with Iowa State University, under the supervision of Prof. A. Ramamoorthy. His thesis, while being an undergraduate student, focused on blind synchronization and detection of binary frequency-shift keying (BFSK) signals. His current research interests include communication load reduction in distributed systems, network coding, and distributed computing for machine learning applications.
After the presentation, there will be a short time for discussion and questions afterwards.