The Parallelism Motifs of Genomic Data Analysis

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing

Architectural patterns for Parallel Programming: models for performance estimation.

Parallel Programming relies on the coordination of computing resources, so that they simultaneously work towards a common objective. Achieving this requires extra effort from the software designer, because of the increased complexity involved. Furthermore, as Parallel Programming is considered a means to improve performance, the software designer has to consider sophisticated and cost-effective practices and techniques for performance measurement and analysis. In particular, it is of great interest to obtain performance information during design stages and before implementation, since this enables the software developer to select the organisation of computations and communications between components. The Architectural Performance Modelling Method is presented as a criteria for selecting the organisation of a parallel program based on estimating its probable per formance. By considering a parallel program as an instance of a software architecture, it can be described in terms of interacting software components. Such components can be classified depending on their particular objective and their rate of change, for example, as components associated with the hardware and software environment (or Platform), components representing the fundamental structural organisation for execution and communication (or Coordination), and so on. The performance of a parallel program can be estimated as the result of the contribution of each one of those kinds of components. An Architectural Performance Model is based on selecting from the Architectural Patterns for Parallel Programming (descriptions of coodinations commonly used in Parallel Programming), a component simulator (representing a simulation of a processing component's behaviour), and a performance analysis of parallel applications (in which the information on system performance is examined). Parallel programs simulated using the Architectural Performance Modelling Method range from a complete parallel pro gram to a partially implemented program design. The simulation of parallel systems, using the information about the problem to be solved, the available resources, and architectural patterns describing overall coordinations of the parallel programs, makes it possible to identify the best performing architectural solution for the system being built

A model for composible and extensible parallel architectural skeletons

Application of pattern-based approaches to parallel programming is an active area of research today. The main objective of pattern-based approaches to parallel programming is to facilitate the reuse of frequently occurring structures for parallelism whereby a user supplies mostly the application specific code-components and the programming environment generates most of the code for parallelization. Parallel Architectural Skeleton (PAS) is such a pattern-based parallel programming model and environment. The PAS model provides a generic way of describing the architectural/structural aspects of patterns in message-passing parallel computing. Application development using PAS is hierarchical, similar to conventional parallel programming using MPI, however with the added benefit of re-usability and high level patterns. Like most other pattern-based parallel programming models, the benefits of PAS were offset by some of its drawbacks such as difficulty in: (1) extending PAS and (2) skeleton composition. SuperPAS is an extension of PAS that addresses these issues. SuperPAS provides a skeleton description language to describe a skeleton in a generic way. Using SuperPAS, a skeleton developer can extend PAS by adding new skeletons to the skeleton repository (i.e., extensibility). SuperPAS also makes the PAS system more flexible by defining composition of skeletons. In this thesis, we describe the model and description language for SuperPAS and elaborate its use through examples

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services