Run-time scheduling and execution of loops on message passing machines

Sparse system solvers and general purpose codes for solving partial differential equations are examples of the many types of problems whose irregularity can result in poor performance on distributed memory machines. Often, the data structures used in these problems are very flexible. Crucial details concerning loop dependences are encoded in these structures rather than being explicitly represented in the program. Good methods for parallelizing and partitioning these types of problems require assignment of computations in rather arbitrary ways. Naive implementations of programs on distributed memory machines requiring general loop partitions can be extremely inefficient. Instead, the scheduling mechanism needs to capture the data reference patterns of the loops in order to partition the problem. First, the indices assigned to each processor must be locally numbered. Next, it is necessary to precompute what information is needed by each processor at various points in the computation. The precomputed information is then used to generate an execution template designed to carry out the computation, communication, and partitioning of data, in an optimized manner. The design is presented for a general preprocessor and schedule executer, the structures of which do not vary, even though the details of the computation and of the type of information are problem dependent

Applications for Multicore System

A multi-core processor is a single computing unit with two or more processors (“cores”). These cores are integrated into a single IC for enhanced performance, reduced power consumption and more efficient simultaneous processing of multiple tasks. Homogeneous multi-core systems include only identical cores, whereas heterogeneous multi-core systems have cores that are not identical. Most of the computers and workstations these days have multicore processors. However most software programs are not designed to make use of multi-core processors and hence even though we run these programs on the new machines equipped with multicore processors, we don’t see sizable improvements in application performance. The idea behind improved performance is in parallelizing the code and distributing the work amongst multiple cores, but writing programming logic to achieve this is complex. The conventional model of lock-based parallelism for writing such programs is difficult in use, error-prone and does not always lead to efficient use of the resources but with the help of OpenMP, programmers have enhanced support for parallel programming. In this work I have implemented quicksort algorithm using OpenMP library and analysed the performance in terms of execution time