Parallel programming

Start Date: 07/05/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/parprog1

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About Course

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.

Course Syllabus

We motivate parallel programming and introduce the basic constructs for building parallel programs on JVM and Scala. Examples such as array norm and Monte Carlo computations illustrate these concepts. We show how to estimate work and depth of parallel programs as well as how to benchmark the implementations.

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Course Introduction

Parallel programming in Java This course teaches you how to use the language constructs that implement the common programming paradigms of parallel programs. It starts with an introduction to the core data types of the Java programming language, including lists, tuples, and structuring multiple files. It covers parallel data acquisition and processing, lists, tuples, and arrays, as well as the basics of threads and threads, locks, threads, and threads synchronization, and shared mutable data. It also covers parallel data structures, such as threads, threads, and locks, threads, and threads synchronization, and shared mutable data. You will need to install the JVM and use the system libraries to use all the features of this course. You will also need to have JDK 8.1. You will need to use the -XX:-UseConstantCPUVMs= option to force the use of dedicated hardware threads for parallel programs.Parallel Programming in Java List Analysis Tuples Arrays Particulate Matters and Health This is Part 2 of the Health in One Lesson course. In this course, we will learn about one of the most common constituents of the atmosphere - particulate matter (PM 2.5 , NO 2 ) pollution. We will examine the effects of particle pollution on health. We will look at the sources and effects of particle pollution and how particles can make their way into our body. We will

Course Tag

Data Structure Parallel Computing Data Parallelism Parallel Algorithm

Related Wiki Topic

Article Example
Parallel programming model Classifications of parallel programming models can be divided broadly into two areas: process interaction and problem decomposition.
F Sharp (programming language) Asynchronous parallel programming sample (parallel CPU and I/O tasks):
Parallel programming model Parallel programming models are closely related to models of computation. A model of parallel computation is an abstraction used to analyze the cost of computational processes, but it does not necessarily need to be practical, in that it can be implemented efficiently in hardware and/or software. A programming model, in contrast, does specifically imply the practical considerations of hardware and software implementation.
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Parallel language There are hundreds of different parallel programming languages. See also concurrent computing.
Parallel computing Mainstream parallel programming languages remain either explicitly parallel or (at best) partially implicit, in which a programmer gives the compiler directives for parallelization. A few fully implicit parallel programming languages exist—SISAL, Parallel Haskell, SequenceL, System C (for FPGAs), Mitrion-C, VHDL, and Verilog.
Parallel programming model Consensus around a particular programming model is important because it leads to different parallel computers being built with support for the model, thereby facilitating portability of software. In this sense, programming models are referred to as "bridging" between hardware and software.
F Sharp (programming language) Parallel programming is supported partly through the codice_32, codice_37 and other operations that run asynchronous blocks in parallel.
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Threading Building Blocks TBB is a collection of components for parallel programming: