Implementing a tool for designing portable parallel programs

The Implementation aspects of a novel parallel programming model called Cluster-M is presented in this thesis. This model provides an environment for efficiently designing highly parallel portable software. The two main components of this model are Cluster-M Specifications and Cluster-M Representations. A Cluster-M Specification consists of a number of clustering levels emphasizing computation and communication requirements of a parallel solution to a given problem. A Cluster-M Representation on the other hand, represents a multi-layered partitioning of a system graph corresponding to the topology of the target architecture. A set of basic constructs essential for writing Cluster-M Specifications using PCN are presented. Also, a. C program for generating the Cluster-M Representations is shown. Cluster-M Specifications are to be mapped onto the Representations using a proposed mapping methodology. Using Cluster-M a single software can be ported among various parallel computing systems. This thesis concentrates on the implementation of the Specifications and the Representations

Theory and design of portable parallel programs for heterogeneous computing systems and networks

A recurring problem with high-performance computing is that advanced architectures generally achieve only a small fraction of their peak performance on many portions of real applications sets. The Amdahl\u27s law corollary of this is that such architectures often spend most of their time on tasks (codes/algorithms and the data sets upon which they operate) for which they are unsuited. Heterogeneous Computing (HC) is needed in the mid 90\u27s and beyond due to ever increasing super-speed requirements and the number of projects with these requirements. HC is defined as a special form of parallel and distributed computing that performs computations using a single autonomous computer operating in both SIMD and MIMD modes, or using a number of connected autonomous computers. Physical implementation of a heterogeneous network or system is currently possible due to the existing technological advances in networking and supercomputing. Unfortunately, software solutions for heterogeneous computing are still in their infancy. Theoretical models, software tools, and intelligent resource-management schemes need to be developed to support heterogeneous computing efficiently. In this thesis, we present a heterogeneous model of computation which encapsulates all the essential parameters for designing efficient software and hardware for HC. We also study a portable parallel programming tool, called Cluster-M, which implements this model. Furthermore, we study and analyze the hardware and software requirements of HC and show that, Cluster-M satisfies the requirements of HC environments

Actors vs Shared Memory: two models at work on Big Data application frameworks

This work aims at analyzing how two different concurrency models, namely the shared memory model and the actor model, can influence the development of applications that manage huge masses of data, distinctive of Big Data applications. The paper compares the two models by analyzing a couple of concrete projects based on the MapReduce and Bulk Synchronous Parallel algorithmic schemes. Both projects are doubly implemented on two concrete platforms: Akka Cluster and Managed X10. The result is both a conceptual comparison of models in the Big Data Analytics scenario, and an experimental analysis based on concrete executions on a cluster platform