Of special interest is the design of algorithms, which reuse as much as possible of the corresponding sequential methods, thereby keeping the effort to update an existing implementation at a minimum. This could be achieved by making use of the properties of shared memory systems as they are widely available in the form of workstations or compute servers. These systems provide a simple and commonly supported programming interface in the form of POSIX-threads.
programming with posix threads butenhof pdf 46
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In this authoritative work, Linux programming expert Michael Kerrisk provides detailed descriptions of the system calls and library functions that you need in order to master the craft of system programming, and accompanies his explanations with clear, complete example programs.
Michael Kerrisk has been using and programming UNIX systems for more than 20 years, and has taught many week-long courses on UNIX system programming. Since 2004, he has maintained the man-pages project ( -pages/), which produces the manual pages describing the Linux kernel and glibc programming APIs. He has written or co-written more than 250 of the manual pages and is actively involved in the testing and design review of new Linux kernel-userspace interfaces. Michael lives with his family in Munich, Germany.
The POSIX thread libraries are a standards based thread API for C/C++.It allows one to spawn a new concurrent process flow. It is most effectiveon multi-processor or multi-core systems where the process flow can be scheduled to run on another processor thus gaining speed through parallel or distributed processing.Threads require less overhead than "forking" or spawning a new process because the system does not initialize a new system virtual memory space and environment forthe process. While most effective on a multiprocessor system, gains arealso found on uniprocessor systems which exploit latency in I/O and othersystem functions which may halt process execution. (One thread may executewhile another is waiting for I/O or some other system latency.)Parallel programming technologies such as MPI and PVM are used in a distributedcomputing environment while threads are limited to a single computer system.All threads within a process share the same address space.A thread is spawned by defining a function and its arguments which willbe processed in the thread.The purpose of using the POSIX thread library in your software is to execute software faster.
A condition variable is a variable of type pthread_cond_t and isused with the appropriate functions for waiting and later, process continuation.The condition variable mechanism allows threads to suspend execution and relinquish the processor until some condition is true.A condition variable must always be associated with a mutexto avoid a race condition created by one thread preparing to wait and another threadwhich may signal the condition before the first thread actually waits on itresulting in a deadlock.The thread will be perpetually waiting for a signal that is never sent.Any mutex can be used, there is no explicit link between the mutex and the condition variable.
I'm currently taking the course Operating Systems in my university. We mainly study theory and have simple exercises in c++ to exercise some theoretical principles. I want to study more about the practical programming in concurrency and threads in c\c++ and i was wondering if any of you have a good book to recommend on.
To effectively and efficiently utilize various hardware resources at different granularity levels, software needs to decompose data and programs, map them onto multiple processing units, and support communication in order to coordinate different subtasks. The shared address space programming paradigm (i.e., multithreading) is a widely used approach on single CPU machines or shared memory multiprocessors [23]. With this programming paradigm, programmers develop multiple threads that process different data sets. Communications among different threads are implicit and achieved through global variables. POSIX (Portable Operating System Interface) Threads [24] and OpenMP (Open Multi-Processing) [25] are popular software packages that support multithread programming. The major advantages of this paradigm are programmability and flexibility. However, it cannot be directly applied on clusters. On distributed memory machines or clusters, the message passing programming paradigm is more effective and commonly used. With this approach, multiple programs are developed and executed concurrently on different computers. Each program runs independently unless it needs to communicate with another program in order to share and synchronize data. If the overhead of such communications can be under control, this approach will utilize the otherwise idle processors in multiprocessors or idle computers in clusters. MPI (Message Passing Interface) [26] is a representative library for this paradigm.
While SIMD instructions exploit instruction-level parallelism within one CPU, in order to take advantage of multiple CPUs or CPUs with multiple cores in a computer system, users must develop multithreading programs. As a widespread programming model, multithreading allows a process to generate multiple software threads that share the same virtual address space. These threads inherit many resources from the hosting process and, at the same time, have their own stacks and registers. Programmers need to partition source code or data into different threads that will be mapped onto different hardware units. Then, these threads execute concurrently and finish their own jobs in parallel.
Even though the achieved speedups cannot be directly compared due to the variations in experimental setups and database sizes, several observations can be made from this table. First, the overall speedup achieved by instruction level parallelism is limited since it only exploits internal parallelism within one single processor. But this approach does not require any new hardware and can be easily combined with other approaches like MPI. Second, the performance achieved by shared memory parallelism is generally moderate since the number of processors sharing the same memory is often limited. However, good performance can be obtained under the assistance of EARTH since it provides an efficient architecture for running threads on large distributed memory systems like clusters. Finally, compared with instruction level parallelism and shared memory parallelism, distributed memory parallelism has the greatest potential to achieve the best performance after carefully taking care of I/O issues and other communication bottlenecks. The disadvantage of this approach is its programming complexity. Programmers need to explicitly divide databases, map computing tasks onto different nodes, and handle various synchronizations among different tasks.
In brief, we have presented a comprehensive analysis of the bottleneck of HMMER algorithm and efficiency of various implementations on modern computing platforms. Most parallel bioinformatics applications use just one of these abovementioned forms to express parallelism, but with the deeper hierarchical structure within heterogeneous computing systems, there has been a movement towards hybrid computing models. The goal of hardware acceleration or hybrid computing is to boost performance and extend the range of applications, particularly to bioinformatics computing. Hybrid computing involves combining multiple techniques together, which apply a different programming paradigm to different levels of the hierarchical system, for example, mixing MPI for internode communication with OpenMP for the intra-node parallelization. Additional parallelism can be achieved to take advantage of the distinct properties of specialized underlying hardware to gain higher-level efficiencies.
XBD ERN 26: Reject: The group struggled to understand the problem statement. The standard is written in C terms, andexpressing problems in C++ does not help the group to understand the issue.4.10 states (in relevant part) "Such access is restricted usingfunctions that synchronize thread execution and also synchronize memorywith respect to other threads." The aardvark is confusing the two aspects of"synchronization" to try to stress a misinterpretation.
The application is responsible for ensuring that two threads cannotaccess the same "memory location" at the same time. However, there aremany ways to accomplish this without explicit "thread execution"synchronization (e.g., mutexes, semaphores, etc.) It is in factsufficient to ensure that only one thread can call a function thatmodifies some memory location. This satisfies the applicationrequirement quoted. 2ff7e9595c
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