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Coarse grained tasks have less communication overhead but they often cause load imbalance. Hence optimal performance is achieved between the two extremes of fine-grained and coarse-grained parallelism. [6] Various studies [5] [7] [8] have proposed their solution to help determine the best granularity to aid parallel processing. Finding the best ...
Cycle i + 5: instruction k + 1 from thread B is issued. Conceptually, it is similar to cooperative multi-tasking used in real-time operating systems, in which tasks voluntarily give up execution time when they need to wait upon some type of event. This type of multithreading is known as block, cooperative or coarse-grained multithreading.
Fine-grained multithreading—such as in a barrel processor—issues instructions for different threads after every cycle, while coarse-grained multithreading only switches to issue instructions from another thread when the current executing thread causes some long latency events (like page fault etc.). Coarse-grain multithreading is more ...
A lock is a programming language construct that allows one thread to take control of a variable and prevent other threads from reading or writing it, until that variable is unlocked. The thread holding the lock is free to execute its critical section (the section of a program that requires exclusive access to some variable), and to unlock the ...
lock contention: this occurs whenever one process or thread attempts to acquire a lock held by another process or thread. The more fine-grained the available locks, the less likely one process/thread will request a lock held by the other. (For example, locking a row rather than the entire table, or locking a cell rather than the entire row);
The core is based on the Presburger Arithmetic and the transitive closure operation. Loop dependencies are represented with relations. TRACO uses the Omega Calculator, CLOOG and ISL libraries, and the Petit dependence analyser. The compiler extracts better locality with fine- and coarse-grained parallelism for C/C++ applications.
Due to the fallacies of distributed computing, finding an adequate granularity is hard. [2] There is no single simple answer but a number of criteria exist (see below). A primary goal of service modeling and granularity design is to achieve loose coupling and modularity, which are two of the essential SOA principles, [3] and to address other architecturally significant requirements.
This code shows the effect of false sharing. It creates an increasing number of threads from one thread to the number of physical threads in the system. Each thread sequentially increments one byte of a cache line, which as a whole is shared among all threads. The higher the level of contention between threads, the longer each increment takes.