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From smart phones, to multi-core CPUs and GPUs, to the world's largest supercomputers and web sites, parallel processing is ubiquitous in modern computing. The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines. Because writing good parallel programs requires an understanding of key machine performance characteristics, this course will cover both parallel hardware and software design. Todd Mowry created the original version of and much of the structure of his innovative course persists today. Instructors: Kayvon Fatahalian and Randy Bryant.

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This is a class focusing on various of techniques of writing parallel programs. It consists of 4 labs that use different parallel strategies, including:. The first lecture discusses the history of parallel programming and some performance advances in this field, such as:. On the hardware side, a transion has been happening from supercomputer to cloud computing, where the former focuses on lower latency and the latter more focuses on high throughput. You can think latency means finishing a single task quickly whereas throughput means finishing more tasks in a period of time.

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From smart phones, to multi-core CPUs and GPUs, to the world's largest supercomputers and web sites, parallel processing is ubiquitous in modern computing. The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines. Because writing good parallel programs requires an understanding of key machine performance characteristics, this course will cover both parallel hardware and software design. Todd Mowry created the original version of and much of the structure of his innovative course persists today. Instructors: Kayvon Fatahalian and Randy Bryant. See the course info page for more info on policies and logistics. Why Parallelism? Why Efficiency?

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Why Efficiency? Statistics cookies collect information anonymously. Recently Viewed Products. Show Cookie Information. Essential 1 Essential. Essential cookies enable basic functions and are necessary for the proper function of the website. Efficiently Evaluating Deep Neural Networks. Interconnection Networks. Accept All Save Back. This information helps us to understand how our visitors use our website. Google Analytics. Exam 2 evening exam. Batch NO. Statistics 1 Statistics Statistics cookies collect information anonymously. In-Memory Distributed Computing in Spark.

From smart phones, to multi-core CPUs and GPUs, to the world's largest supercomputers and web sites, parallel processing is ubiquitous in modern computing. The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines.

The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines. Assignment 1 out. If External Media cookies are accepted, access to those contents no longer requires manual consent. Efficiently Evaluating Deep Neural Networks. Exam I. Cookie Details Privacy Policy. Directory-Based Cache Coherence. Parallel Deep Neural Network Training. Addressing the Memory Wall. Generates statistical data on how the visitor uses the website. Statistics 1 Statistics Statistics cookies collect information anonymously.