Introducing Multithreaded Programming: Posix Threads And Nvidia's Cuda

POSIX Threads (Pthreads)

Encyclopedia of Parallel Computing ◽

10.1007/978-0-387-09766-4_447 ◽

2011 ◽

pp. 1592-1593

Author(s):

Bruce Leasure ◽

David J. Kuck ◽

Sergei Gorlatch ◽

Murray Cole ◽

Gregory R. Watson ◽

...

Keyword(s):

Posix Threads

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Programowanie wielowątkowe w językach strukturalnych i obiektowych

Journal of Computer Sciences Institute ◽

10.35784/jcsi.194 ◽

2019 ◽

Vol 10 ◽

pp. 49-53

Author(s):

Mateusz Wiśniewski

Keyword(s):

Posix Threads

W artykule przedstawiono sposoby programowania wielowątkowego w wybranych językach programowania takich jak: C podejście strukturalne oraz obiektowe C++ przy użyciu bibliotek Posix Threads dla języka C oraz bibliotekę Boost dla języka C++. Opisano również charakterystykę wybranych bibliotek. Przedstawiono przykładowe rozwiązania typowych problemów programistycznych wykorzystujących wątki. Starano odpowiedzieć na pytanie czym kierować się przy wyborze języka do nauki programowania wielowątkowego oraz programowania w ogólnym tego słowa znaczeniu.

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Multithreaded Programming of Reconfigurable Embedded Systems

Reconfigurable Embedded Control Systems ◽

10.4018/978-1-60960-086-0.ch002 ◽

2011 ◽

pp. 31-54

Author(s):

Jason Agron ◽

David Andrews ◽

Markus Happe ◽

Enno Lübbers ◽

Marco Platzner

Keyword(s):

Development Time ◽

Programming Model ◽

General Purpose ◽

System Level ◽

Performance Ratio ◽

Multithreaded Programming ◽

Price Performance ◽

Flexible Hardware ◽

Custom Hardware ◽

Application Requirements

Embedded and Real-Time (ERTS) systems have continued to expand at a vigorous rate. Designers of ERTS systems are continually challenged to provide new capabilities that can meet the expanding requirements and increased computational needs of each new proposed application, but at a decreasing price/performance ratio. Conventional solutions using general purpose processors or custom ASICs are less and less able to satisfy the contradictory requirements in performance, flexibility, power, development time, and cost. This chapter introduces the concept of generating semi-custom platforms driven from a traditional multithreaded programming model. This approach offers the advantage of achieving productivity levels close to those associated with software by using an established programming model but with a performance level close to custom hardware through the use of a flexible hardware platform capable of adapting to specialized application requirements. We discuss the underlying concepts, requirements and advantages of multithreading in the context of reconfigurable hardware, and present two approaches which provide multithreading support to hardware and software components at the operating system level.

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Parallel real-world LU decomposition: Gauss vs. Crout algorithm

Open Computer Science ◽

10.1515/comp-2018-0020 ◽

2018 ◽

Vol 8 (1) ◽

pp. 210-217

Author(s):

Marek Stabrowski

Keyword(s):

Real World ◽

Numerical Experiments ◽

Matrix Decomposition ◽

Decomposition Algorithms ◽

Density Matrices ◽

Hardware Platform ◽

Posix Threads ◽

Real World Applications ◽

Matrix Decomposition Algorithms ◽

Algorithm Implementation

Abstract This paper presents numerical experiments with assorted versions of parallel LU matrix decomposition algorithms (Gauss and Crout algorithm). The tests have been carried out on the hardware platform with fourcore Skylake processor featuring hyperthreading technology doubling virtually core number. Parallelization algorithms have been implemented with the aid of classic POSIX threads library. Experiments have shown that basic 4-thread acceleration of all parallel implementations is almost equal to the number of threads/processors. Both algorithms are worth considering in real-world applications (Florida University collection). Gauss algorithm is a better performer, with respect to timing, in the case of matrices with lower density of nonzeros, as opposed to higher density matrices. The latter are processed more efficiently with the aid of Crout algorithm implementation.

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