Lesson 26: Introduction to Algorithms by Mohammad Hajiaghayi: Parallel Algorithms for Massive Data

During this last session of the course, our focus lies on parallel algorithms tailored for handling massive data within frameworks commonly known as Massively Parallel Computation (MPC). These frameworks, such as MapReduce, Apache Spark, Flume, Hadoop, and others, are specifically designed to tackle the challenges of processing vast amounts of data efficiently. To kick off our exploration, we introduce Apache Spark, a widely-used tool in real-world applications, by illustrating simple examples of text processing and counting number of words in a text accomplished through this powerful framework.

Subsequently, we delve deeper into massively parallel algorithms, and our attention turns to some compelling use cases. One such scenario involves matching on very large graphs, where traditional sequential approaches prove inadequate due to the sheer scale of data. With the aid of MPC frameworks, we demonstrate how (maximal) matching algorithms can be executed in parallel to achieve superior performance and handle colossal graph sizes effectively. Additionally, we venture into solving the edit distance problem and finding the largest common subsequence for massive texts. These computational tasks present significant challenges when dealing with huge datasets, but through the application of well-designed parallel algorithms within MPC frameworks, we unveil how these hurdles can be overcome efficiently and reliably.

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All handwritten and typed notes for this course are available through the website of the instructor