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Parallel programming

About This Course

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.

Learning Outcomes. By the end of this course you will be able to:

  • reason about task and data parallel programs,
  • express common algorithms in a functional style and solve them in parallel
  • competently microbenchmark parallel code,
  • write programs that effectively use parallel collections to achieve performance

Recommended background

You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala.

Course Staff

Course Staff Image #1

Viktor Kuncak

Viktor Kuncak is an associate professor in the EPFL School of Computer and Communication Sciences, where, since 2007, he leads the Laboratory for Automated Reasoning and Analysis (http://lara.epfl.ch). He works in formal methods with emphasis on algorithms and tools, such as Leon tool for verification and synthesis of Scala programs (http://leon.epfl.ch). His community service include co-chairing CAV 2017, SYNT 2015, FMCAD 2014, and VMCAI 2012. He also co-led an international COST Action to establish standardized formats for verification and synthesis (Rich Model Toolkit). His proposal on Implicit Programming, aiming to bridge the gap between human goals and their computational realizations, was funded in 2012 by a European Research Council (ERC) starting grant. Viktor Kuncak received a PhD degree from the Massachusetts Institute of Technology (MIT) in 2007.

Course Staff Image #1

Aleksandar Prokopec

Dr. Aleksandar Prokopec is a software developer and a concurrent and distributed programming researcher, working at Oracle Labs. He obtained a PhD in Computer Science from the Ecole Polytechnique Federale de Lausanne, Switzerland. As a doctoral assistant and member of the Scala team at EPFL, he actively contributed to the Scala programming language, and has worked on programming abstractions for concurrency, data-parallel programming support, and concurrent data structures for Scala. He created the Scala Parallel Collections framework, which is a library for high-level data-parallel programming in Scala, and participated in working groups for Scala concurrency libraries. He previously worked at Google.

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  1. Course Number

    parprog1
  2. Classes Start

  3. Estimated Effort

    ~6 hours per week
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