Synchronization Algorithms for Multi-cores and Multiprocessors

A distributed system is a group of processors that do not allocate memory. As an alternative, each processor has its own local memory, and the processors communicate with one another through communication lines such as local-area or wide-area networks. The processors in a distributed system vary in size and function. Such systems may include small handheld or real-time devices, personal computers, workstations, and large mainframe computer systems. Distributed systems, will have their own set of unique challenges, including synchronizing data and creating sense of conflicts. Effective synchronization algorithms performance depends on runtime factors that are rigid to predict. The designers have protocols to employ the synchronization operation and waiting mechanisms to wait for synchronization delays. In this paper an effort is made to investigate synchronization algorithm that vigorously select waiting mechanisms and protocols in response to runtime factors so as to attain enhanced performance. DOI: 10.17762/ijritcc2321-8169.150615

The FastMap Algorithm for Shortest Path Computations

We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing approaches that produce a Euclidean embedding using Semidefinite Programming. FastMap also produces admissible and consistent heuristics and therefore guarantees the generation of shortest paths. Moreover, FastMap applies to general undirected graphs for which many traditional heuristics, such as the Manhattan Distance heuristic, are not well defined. Empirically, we demonstrate that A* search using the FastMap heuristic is competitive with A* search using other state-of-the-art heuristics, such as the Differential heuristic