Pthreads

Summary

In computing, POSIX Threads, commonly known as pthreads, is an execution model that exists independently from a programming language, as well as a parallel execution model. It allows a program to control multiple different flows of work that overlap in time. Each flow of work is referred to as a thread, and creation and control over these flows is achieved by making calls to the POSIX Threads API. POSIX Threads is an API defined by the Institute of Electrical and Electronics Engineers (IEEE) standard POSIX.1c, Threads extensions (IEEE Std 1003.1c-1995). Implementations of the API are available on many Unix-like POSIX-conformant operating systems such as FreeBSD, NetBSD, OpenBSD, Linux, macOS, Android, Solaris, Redox, and AUTOSAR Adaptive, typically bundled as a library libpthread. DR-DOS and Microsoft Windows implementations also exist: within the SFU/SUA subsystem which provides a native implementation of a number of POSIX APIs, and also within third-party packages such as pthrea

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https://en.wikipedia.org/wiki/Pthreads

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Deadlock immunity is a property by which programs, once afflicted by a given deadlock, develop resistance against future occurrences of that and similar deadlocks. We describe a technique that enables programs to automatically gain such immunity without assistance from programmers or users. We implemented the technique for both Java and POSIX threads and evaluated it with several real systems, including MySQL, JBoss, SQLite, Apache ActiveMQ, Limewire, and Java JDK. The results demonstrate effectiveness against real deadlock bugs, while incurring modest performance overhead and scaling to 1024 threads. We therefore conclude that deadlock immunity offers programmers and users an attractive tool for coping with elusive deadlocks.

2008

In this paper, we present the first system that implement OpenMP on network of shared-memory multiprocessors. This system enables the program to rely on a single, standard, shared-memory API for parallelization within a multiprocessor and between multiprocessors. It is implemented via a translator that convert OpenMP directives to appropriate calls to a modified version of the TreadMarks software distributed shared memory(SDSM) system. In contrast to previous SDSM systems for SMPs, the modified TreadMarks system uses POSIX threads for parallelism within an SMP mode. This approach greatly simplifies the changes required to the SDSM in order to exploit the intra-mode hardware shared memory. We present performance results for seven applications (Barnes-Hut, CLU and Water from SPLASH-2, 3D-FFT from NAS, Red-Black SOR, TSP and MGS) running on an SP2 with four four-processor SMP nodes. A comparison between the thread implementation and the original implementation of TreadMarks shows that using the hardware shared memory within an SMP node significantly achieves speedups that are up to 30% better than the original versions. We also compare SDSM against message passing. Overall, the speedups of multithreaded RreadMarks programs are within 7-30% of the MPI versions.

2000

In this paper, we present the first system that implements OpenMP on a network of shared-memory multiprocessors. This system enables the programmer to rely on a single, standard, shared-memory API for parallelization within a multiprocessor and between multiprocessors. It is implemented via a translator that converts OpenMP directives to appropriate calls to a modified version of the TreadMarks software distributed memory system (SDSM). In contrast to previous SDSM systems for SMPs, the modified TreadMarks uses POSIX threads for parallelism within an SMP node. This approach greatly simplifies the changes required to the SDSM in order to exploit the intra-node hardware shared memory. We present performance results for six applications (SPLASH-2 Barnes-Hut andWater, NAS 3D-FFT, SOR, TSP and MGS) running on an SP2 with four four-processor SMP nodes. A comparison between the threaded implementation and the original implementation of TreadMarks shows that using the hardware shared memory within an SMP node significantly reduces the amount of data and the number of messages transmitted between nodes, and consequently achieves speedups up to 30% better than the original versions. We also compare SDSM against message passing. Overall, the speedups of multithreaded TreadMarks programs are within 7–30% of the MPI versions.

1999