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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?

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Domain-Specific Parallel Programming Systems. In-Memory Distributed Computing in Spark.

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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 Randal Bryant. See the course info page for more info on policies and logistics.

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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. The formal definition of Parallel Computing : a collecitons of processing elements that cooperate to solve problems quickly. That means:. Parallelism v. In my understanding, parallelism means, at a moment, multiple processors e.

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Show Cookie Information. If External Media cookies are accepted, access to those contents no longer requires manual consent. Interconnection Networks. Size Price Sale Price Dis. Domain-Specific Parallel Programming Systems. Cookie Details Privacy Policy. Basic Snooping-Based Multiprocessor Implementation. Transactional Memory. Implementing Synchronization. Cart 0. Google Analytics. Todd Mowry created the original version of and much of the structure of his innovative course persists today. Essential cookies enable basic functions and are necessary for the proper function of the website.

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.

Google Analytics. Exam I. Heterogeneous Parallelism and Hardware Specialization. Cart 0. Exam 2 evening exam. Essential 1 Essential. If External Media cookies are accepted, access to those contents no longer requires manual consent. Recently Viewed Products. Some of them are essential, while others help us to improve this website and your experience. Efficiently Evaluating Deep Neural Networks. In-Memory Distributed Computing in Spark. Parallel Deep Neural Network Training.