PARALLEL AND DISTRIBUTED COMPUTING
IVAN PUZYREVSKIY
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The modern computing is all about the distributed. Big Data, Machine Learning, Systems – all these new and fancy subjects require us to leverage the power of many available cores and devices. This course aims to provide a background in parallel programming and concurrent and distributed computing. Students will learn how to structure, write, run and debug multithreaded applications in Java. More importantly, they will also learn how to reason about the code running in parallel to ensure desired safety and liveness properties in the scalable way without sacrificing the performance. As a teaser, by the end of the course students are introduced to the distributed computing.
The course builds from the classical model of computation with a single processor and a memory and iteratively introduces one more bit of the complexity at once. First, the course explores the effects of introducing several processors into the computation model but keeping a shared memory. Then, the course explores the effects of removing the shared memory. And, finally, the course explores the effects of failures on the computation model.
On a practical side, the course includes a lot of labs and programming exercises to ensure enough hands-on experience. Performance matters are covered as well, delivering basic performance analysis capabilities to the students.
Currently Ivan is a technical lead in Yandex, where he supervises and leads the development of Yandex.Travel, an online travel service. Prior to that, he was working for 6+ years on the data storage and processing infrastructure that powers all the services in the company and scales over thousands of machines.
Ivan teaches at Higher School of Economics, where he leads the distributed systems seminar. He also contributed significantly towards the distributed systems programme for the bachelors. Also, Ivan has been teaching at Yandex School of Data Analysis since 2011.
Ivan Puzyrevskiy holds the Specialist Degree in Mathematics from the Moscow State University as of 2011, where he studied the external memory algorithms and the resilient algorithms for his thesis.
The primary objective of the course is to provide students with the practical skills to write parallel and distributed algorithms in Java as well as to provide students with the rigorous framework to reason and analyse the properties of the algorithms.
Upon the completion of the course students will be able to:
-Write a multithreaded application in Java
-Structure the multi-threaded code in a composable and scalable manner
-Understand and formulate the safety and the liveness properties
-Use synchronisation primitives to ensure the correct behaviour of an algorithm
-Identify problems that hinder the performance of a parallel algorithm
-Transform existing programmes to leverage the available parallelism
-Argument when the algorithm will not benefit from the available parallelism
-Write fault tolerant and highly available distributed applications with the provided tooling
SKILLS:
-System programming
-External memory algorithms
-Resilient algorithms
-Distributed Computing
ABOUT IVAN
HARBOUR.SPACE WHAT YOU WILL LEARN
DATE: 8 Apr - 26 Apr, 2019
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
HARBOUR.SPACE UNIVERSITY
RESERVE MY SPOT
DATE: 8 Apr - 26 Apr, 2019
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
All rights reserved. 2017
COURSE OUTLINESession 1
Introduction to the Course
Course syllabus, logistics, grading. Introduction to the concurrency and the parallelism. Hardware support. Real-world environments that exhibit parallelism
Session 4
Higher-level Abstractions
Executors and futures; structure and composability
Session 3
Basic Patterns and Techniques
Data/work-parallelism; load balancing and distribution; synchronisation and communication
Session 2
Multithreaded Programming in Java
Basic primitives; challenges and issues; threading in Java; synchronisation primitives
MACHINE LEARNING
BIBLIOGRAPHY
The modern computing is all about the distributed. Big Data, Machine Learning, Systems – all these new and fancy subjects require us to leverage the power of many available cores and devices. This course aims to provide a background in parallel programming and concurrent and distributed computing. Students will learn how to structure, write, run and debug multithreaded applications in Java. More importantly, they will also learn how to reason about the code running in parallel to ensure desired safety and liveness properties in the scalable way without sacrificing the performance. As a teaser, by the end of the course students are introduced to the distributed computing.
The course builds from the classical model of computation with a single processor and a memory and iteratively introduces one more bit of the complexity at once. First, the course explores the effects of introducing several processors into the computation model but keeping a shared memory. Then, the course explores the effects of removing the shared memory. And, finally, the course explores the effects of failures on the computation model.
On a practical side, the course includes a lot of labs and programming exercises to ensure enough hands-on experience. Performance matters are covered as well, delivering basic performance analysis capabilities to the students.
The primary objective of the course is to provide students with the practical skills to write parallel and distributed algorithms in Java as well as to provide students with the rigorous framework to reason and analyse the properties of the algorithms.
Upon the completion of the course students will be able to:
-Write a multithreaded application in Java
-Structure the multi-threaded code in a composable and scalable manner
-Understand and formulate the safety and the liveness properties
-Use synchronisation primitives to ensure the correct behaviour of an algorithm
-Identify problems that hinder the performance of a parallel algorithm
-Transform existing programmes to leverage the available parallelism
-Argument when the algorithm will not benefit from the available parallelism
-Write fault tolerant and highly available distributed applications with the provided tooling
Currently Ivan is a technical lead in Yandex, where he supervises and leads the development of Yandex.Travel, an online travel service. Prior to that, he was working for 6+ years on the data storage and processing infrastructure that powers all the services in the company and scales over thousands of machines.
Ivan teaches at Higher School of Economics, where he leads the distributed systems seminar. He also contributed significantly towards the distributed systems programme for the bachelors. Also, Ivan has been teaching at Yandex School of Data Analysis since 2011.
Ivan Puzyrevskiy holds the Specialist Degree in Mathematics from the Moscow State University as of 2011, where he studied the external memory algorithms and the resilient algorithms for his thesis.