This course introduces the fundamentals of high-performance and parallel computing. It is targeted to scientists, engineers, scholars, really everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. We will cover the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code.
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Introduction to High-Performance and Parallel Computing
콜로라도 대학교 볼더 캠퍼스이 강좌에 대하여
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콜로라도 대학교 볼더 캠퍼스
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
강의 계획 - 이 강좌에서 배울 내용
Week 1 - High Performance Computing (HPC) for Non-Computer Scientists
Get to know the basics of an HPC system. Users will learn how to work with common high performance computing systems they may encounter in future efforts. This includes navigating filesystems, working with a typical HPC operating system (Linux), and some of the basic concepts of HPC. We will also provide users some key information that is specific to the logistics of this course.
Week 2 - Nuts and Bolts of HPC
During this week we will actually begin to use HPC infrastructure. Some concepts we will learn are - how to load software appropriately onto an HPC system, what the different types of nodes a user can expect to encounter on a system, and how to submit a job to conduct work, such as perform calculations.
Week 3 - Basic Parallelism
In this module, we will introduce users to the nuances of memory on a high performance computing system. We will also cover some ways to conduct work on a system most efficiently. We will also introduce some beginning components of parallel programming.
Week 4: Evaluating Parallel Program Performance
In this module, we will continue to review topics related to using a high performance computing system most efficiently, including scaling your workflow measuring how efficient your work on a system is, and how to utilize as much of the computing resource as possible.
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