In this module, we will learn about threads and locks, which have served as primitive building blocks for concurrent programming for over five decades. All computing platforms today include some form of support for threads and locks, and make them available for use by developers in a wide range of programming languages. We will learn how threads can be created, joined, and synchronized using structured (e.g., synchronized statements/methods) and unstructured (e.g., java.util.concurrent libraries) locks in Java. We will also learn about new classes of bugs that can arise when concurrent programs need to access shared resources. These bugs are referred to as violations of liveness/progress guarantees, and include deadlock, livelock, and starvation. We will conclude this module by studying different solutions to the classical "Dining Philosophers" problem, and use these solutions to illustrate instances of deadlock, livelock and starvation.

In this module, we will learn different approaches to coordinating accesses to shared resources without encountering the deadlock or livelock bugs studied earlier. Critical/isolated sections are higher-level concurrent programming constructs (relative to locks) that simplify the implementation of mutual exclusion by guaranteeing the absence of deadlocks and livelocks. Object-based isolation relaxes the constraints imposed by critical sections by allowing mutual exclusion to be specified on a per-object basis, as illustrated in the Spanning Tree example. Java's atomic variables represent an important, but restricted, case of object-based isolation that is implemented efficiently on all hardware platforms. Finally, we will learn how object-based isolation can be further relaxed with read/write access modes.

Join Professor Vivek Sarkar as he talks with Software Engineer, Dr. Shams Imam, at their downtown Houston, Texas office about threads, locks, deadlocks, high-level and low-level constructs, and the importance of concurrent programming.

In this module, we will learn another high-level approach to concurrent programming called the "Actor" model. A major difference between the Actor model and the Isolated Sections model is that there are no data races possible in the Actor model because it does not allow for any form of shared variables. However, as in all concurrent programming models, higher-level forms of nondeterminism are still possible in the Actor model due to an inherent asynchrony in the order in which messages may be delivered. We will study multiple examples of concurrency using the Actor model, including the classical Sieve of Eratosthenes algorithm to generate prime numbers, as well as producer-consumer patterns with both unbounded and bounded buffers.

In this module, we will study Concurrent Data Structures, which form an essential software layer in all multithreaded programming systems. First, we will learn about Optimistic Concurrency, an important multithreaded pattern in which two threads can "optimistically" make progress on their assigned work without worrying about mutual conflicts, and only checking for conflicts before "committing" the results of their work. We will then study the widely-used Concurrent Queue data structure. Even though the APIs for using concurrent queues are very simple, their implementations using the Optimistic Concurrency model can be complex and error-prone. To that end, we will also learn the formal notion of Linearizability to better understand correctness requirements for concurrent data structures. We will then study Concurrent Hash Maps, another widely-used concurrent data structure. Finally, we discuss a concurrent algorithm for finding a Minimum Spanning Tree of an undirected graph, an algorithm that relies on the use of Concurrent Data Structures under the covers.

The next two videos will showcase the importance of learning about Parallel Programming and Distributed Programming in Java. Professor Vivek Sarkar will speak with industry professionals at Two Sigma about how the topics of our other two courses are utilized in the field.