Performance evaluation of Fast Ethernet, ATM and Myrinet under PVM

Congestion in network switches can limit the communication traffic between Parallel Virtual Machine (PVM) nodes in a parallel computation. The research introduces a new benchmark to evaluate the performance of PVM in various networking environments. The benchmark is used to achieve a better understanding of performance limitations in parallel computing that are imposed by the choice of the network. The networks considered here are Fast Ethernet, Asynchronous Transfer Mode (ATM) OC-3c (155Mb/s) and Myrinet. Together, they represent an interesting range of alternatives for parallel cluster computing. A characterization of network delays and throughput and a comparison of the expected costs of the three environments are developed to provide a basis for an informed decision on the networking methods and topology for a parallel database that is being considered for FBI\u27s National DNA Indexing System (NDIS)[17]. This network is used for communications among the nodes of the parallel machine; thus the security requirements defined for the FBI\u27s Criminal Justice Information Services Division Wide Area Network (CJIS-WAN) [12] are not a concern

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy

Process-Oriented Parallel Programming with an Application to Data-Intensive Computing

We introduce process-oriented programming as a natural extension of object-oriented programming for parallel computing. It is based on the observation that every class of an object-oriented language can be instantiated as a process, accessible via a remote pointer. The introduction of process pointers requires no syntax extension, identifies processes with programming objects, and enables processes to exchange information simply by executing remote methods. Process-oriented programming is a high-level language alternative to multithreading, MPI and many other languages, environments and tools currently used for parallel computations. It implements natural object-based parallelism using only minimal syntax extension of existing languages, such as C++ and Python, and has therefore the potential to lead to widespread adoption of parallel programming. We implemented a prototype system for running processes using C++ with MPI and used it to compute a large three-dimensional Fourier transform on a computer cluster built of commodity hardware components. Three-dimensional Fourier transform is a prototype of a data-intensive application with a complex data-access pattern. The process-oriented code is only a few hundred lines long, and attains very high data throughput by achieving massive parallelism and maximizing hardware utilization.Comment: 20 pages, 1 figur