Parallel programming

Start Date: 10/13/2019

Course Type: Common Course

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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

With every smartphone and computer now boasting multiple processors, the use of functional ideas to

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.
Parallel programming model A parallel programming language may be based on one or a combination of programming models. For example, High Performance Fortran is based on shared-memory interactions and data-parallel problem decomposition, and Go provides mechanism for shared-memory and message-passing interaction.
Symposium on Principles and Practice of Parallel Programming PPoPP, the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, is an academic conference in the field of parallel programming. PPoPP is sponsored by the Association for Computing Machinery special interest group SIGPLAN.
Parallel programming model In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its "generality": how well a range of different problems can be expressed for a variety of different architectures, and its "performance": how efficiently the compiled programs can execute. The implementation of a parallel programming model can take the form of a library invoked from a sequential language, as an extension to an existing language, or as an entirely new language.
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.
CODE (programming language) CODE (computationally oriented display environment) is a visual programming language and system for parallel programming, which lets users compose sequential programs into parallel programs.
Parallel programming model Shared memory is an efficient means of passing data between processes. In a shared-memory model, parallel processes share a global address space that they read and write to asynchronously. Asynchronous concurrent access can lead to race conditions and mechanisms such as locks, semaphores and monitors can be used to avoid these. Conventional multi-core processors directly support shared memory, which many parallel programming languages and libraries, such as Cilk, OpenMP and Threading Building Blocks, are designed to exploit.
Scala (programming language) Scala also comes with built-in support for data-parallel programming in the form of Parallel Collections integrated into its Standard Library since version 2.9.0.
ParaSail (programming language) Parallel Specification and Implementation Language (ParaSail) is an object-oriented parallel programming language. Its design and ongoing implementation is described in a blog and on its official website.
Parallel Processing Letters Parallel Processing Letters is a journal published by World Scientific since 1991. It covers the field of parallel processing, including topics such as design and analysis of parallel and distributed algorithms, parallel programming languages and parallel architectures and VLSI circuits.
Parallel computing Parallel programming languages and parallel computers must have a consistency model (also known as a memory model). The consistency model defines rules for how operations on computer memory occur and how results are produced.
Sieve C++ Parallel Programming System The Sieve C++ Parallel Programming System is a C++ compiler and parallel runtime designed and released by Codeplay that aims to simplify the parallelization of code so that it may run efficiently on multi-processor or multi-core systems. It is an alternative to other well-known parallelisation methods such as OpenMP, the RapidMind Development Platform and Threading Building Blocks (TBB).
Explicit parallelism The advantage of explicit parallel programming is the absolute programmer
Collective operation Collective operations are present in the following parallel programming frameworks:
Threading Building Blocks TBB is a collection of components for parallel programming: