Set Of Communication Primitives Research Articles

An improved, randomized algorithm for parallel selection with an experimental study

A common statistical problem is that of finding the median element in a set of data. This paper presents an efficient randomized high-level parallel algorithm for finding the median given a set of elements distributed across a parallel machine. In fact, our algorithm solves the general selection problem that requires the determination of the element of rank k, for an arbitrarily given integer k.Our general framework is an SPMD distributed-memory programming model that is enhanced by a set of communication primitives. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The algorithms have been coded in the message-passing standard MPI, and our experimental results from the IBM SP-2 illustrate the scalability and efficiency of our algorithm and improve upon all the related experimental results known to the author.The main contributions of this paper are(1) New techniques for speeding the performance of certain randomized algorithms, such as selection, which are efficient with likely probability.(2) A new, practical randomized selection algorithm (UltraFast) with significantly improved convergence.

On the Expressiveness of Linda-like Concurrent Languages

We compare the expressiveness of a class of concurrent languages that employ asynchronous communication primitives à la Linda. All the languages considered contain sequential, parallel and choice operators, and they differ from one another in the set of communication primitives used. These primitives include tell, get and ask operations for adding, deleting, and checking for the presence of data in a dataspace shared by a number of concurrent processes, as well as a nask (negative ask) operation for checking for the absence of data in the shared dataspace. We use the notion of modular embedding introduced by De Boer and Palamidessi in [3] to compare the relative expressive power of the languages. A first result is the formalisation of the intuitive separation result stating that the language with get and tell is strictly more expressive than the language with ask and tell operations. An interesting result is that the ability to check for the presence of information (ask) does not increase the power of a language containing get and tell operations, whereas the ability to check for the absence of information (nask) does increase the power of such a language. Another interesting result shown is that the language containing all the communication primitives considered is strictly more expressive than each of its sub-languages, except for the redundancy of ask.

Parallel Implementations of Iterated Runge-Kutta Methods

We investigate different parallel algorithms for the iter ated Runge-Kutta method on distributed memory mul tiprocessors for the solution of systems of ordinary differential equations (ODEs). The iterated Runge-Kutta method is an iteration scheme for the numerical solu tion of initial-value problems of nonstiff ODEs; embed ded approximation formulae are used to control the step size. The parallel algorithms realize potential par allelism across the method and the system in a group- SPMD (single-program, multiple-data) programming style, using an appropriate set of communication prim itives that can be implemented on all common topolo gies. A theoretical performance analysis with run-time formulae and a run-time simulation show the value of the algorithms. The implementation on the Intel iPSC/ 860 confirms the predicted run times. The speedup values depend strongly on the particular system of ODEs to be solved. The parallel iterated Runge-Kutta method is applied to a typical discretization problem, the discretized Brusselator equation. Application-spe cific modifications of the general parallel ODE solver are developed, which result in a considerable reduc tion in the parallel execution time.