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Efficient Programming · LearnSpace
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Efficient Programming

Курс от University of Colorado Boulder
Средний≈ 7.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course is targeted to scientists, engineers, scholars, or anyone seeking to solve problems efficiently in high-performance computing environments or in the cloud. Students completing this course will have a basic understanding of how to find bottlenecks in their programs as well as how to address those bottlenecks. The course will provide a high-level introduction to modern compute node architectures of high-performance and cloud computing instances. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Навыки, которые вы освоите

Memory ManagementHardware ArchitectureMicroarchitectureComputing PlatformsCloud DevelopmentOS Process ManagementComputer Programming Tools

Программа курса

5 модулей · 35 учебных материалов

01Finding Performance Bottlenecks10 материалов

Welcome and Course Overview

Course Updates and Accessibility SupportЧтениеEarn Academic Credit for your Work!ЧтениеCourse SupportЧтениеCourse OverviewВидео

Profiling Techniques

Учитесь у экспертов

Shelley Knuth

Associate Director of User Services

Thomas Hauser

Director of Research Computing

Efficient Programming
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Обучение на Coursera

≈ 7.7 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Корейский, Немецкий, Индонезийский, Испанский, Японский, Казахский

Часть программы вашего университета
Profiling with gprofВидео
Profiling For PythonВидео

Optimizing with Libraries and Compiler Options

Numerical LibrariesВидеоCompiler Options for PerformanceВидео

Module Assessments

Matrix Multiplication ProfilingПрограммированиеModule QuizЗадание
02Simple Optimization Techniques7 материалов

Dependency and Scalar Analysis

Dependency AnalysisВидеоScalar OptimizationВидео

Loop and Python Optimizations

Loop Optimizations - Part 1ВидеоLoop Optimizations - Part 2ВидеоPython Optimization with NumPyВидео

Module Assessments

Matrix Multiplication Optimizations: Loop Transformations and ParallelizationПрограммированиеModule QuizЗадание
03Computer Architecture and Vectorization6 материалов

Computer Architecture

Computer ArchitectureВидео

Vectorization and Optimization Techniques

Maximizing Performance with VectorizationВидеоPreparing your Application Data for Vectorization - Data AlignmentВидеоOpenMP - SIMDВидео

Quiz

Vectorization and Parallelization in Dot Product ComputationПрограммированиеModule QuizЗадание
04Computer Architecture6 материалов

Memory Architecture and Hierarchy

Memory ArchitectureВидеоProcessor Memory HierarchyВидеоCache and Memory Characteristics of a Compute NodeВидео

Data Access Scaling - BLAS

Data Access Scaling - BLASВидео

Module Assessments

Implementing Tiled Matrix AdditionПрограммированиеModule QuizЗадание
05Parallel and High Throughput Computing6 материалов

Foundations of Parallel and High Throughput Computing

Introduction to Parallel ComputingВидеоIntroduction to High Throughput ComputingВидео

Practical Applications and Tools

Slurm Job Arrays DemoВидеоHTC with GNU ParallelВидео

Module Assessments

Parallel Sum of SquaresПрограммированиеModule QuizЗадание