A Pluggable Framework for Lightweight Task Offloading in Parallel and Distributed Computing

Multicore processors have quickly become ubiquitous in supercomputing, cluster computing, datacenter computing, and even personal computing. Software advances, however, continue to lag behind. In the past, software designers could simply rely on clock-speed increases to improve the performance of their software. With clock speeds now stagnant, software designers need to tap into the increased horsepower of multiple cores in a processor by creating software artifacts that support parallelism. Rather than forcing designers to write such software artifacts from scratch, we propose a pluggable framework that designers can reuse for lightweight task offloading in a parallel computing environment of multiple cores, whether those cores be colocated on a processor within a compute node, between compute nodes in a tightly-coupled system like a supercomputer, or between compute nodes in a loosely-coupled one like a cloud computer. To demonstrate the efficacy of our framework, we use the framework to implement lightweight task offloading (or software acceleration) for a popular parallel sequence-search application called mpiBLAST. Our experimental results on a 9-node, 36-core AMD Opteron cluster show that using mpiBLAST with our pluggable framework results in a 205% speed-up

Architectural Refactoring for Fast and Modular Bioinformatics Sequence Search

Bioinformaticists use the Basic Local Alignment Search Tool (BLAST) to characterize an unknown sequence by comparing it against a database of known sequences, thus detecting evolutionary relationships and biological properties. mpiBLAST is a widely-used, high-performance, open-source parallelization of BLAST that runs on a computer cluster delivering super-linear speedups. However, the Achilles heel of mpiBLAST is its lack of modularity, adversely affecting maintainability and extensibility; an effective architectural refactoring will benefit both users and developers. This paper describes our experiences in the architectural refactoring of mpiBLAST into a modular, high-performance software package. Our evaluation of five component-oriented designs culminated in a design that enables modularity while retaining high-performance. Furthermore, we achieved this refactoring effectively and efficiently using eXtreme Programming techniques. These experiences will be of value to software engineers faced with the challenge of creating maintainable and extensible, high-performance, bioinformatics software

Parallelisation for data-intensive applications over peer-to-peer networks

In Data Intensive Computing, properties of the data that are the input for an application decide running performance in most cases. Those properties include the size of the data, the relationships inside data, and so forth. There is a class of data intensive applications (BLAST, SETI@home, Folding@Home and so on so forth) whose performances solely depend on the amount of input data. Another important characteristic of those applications is that the input data can be split into units and these units are not related to each other during the runs of the applications. This characteristic helps this class of data intensive applications to be parallelised in the way where the input data is split into units and application runs on different computer nodes for certain portion of the units. SETI@home and Folding@Home have been successfully parallelised over peer-to-peer networks. However, they suffer from the problems of single point of failure and poor scalability. In order to solve these problems, we choose BLAST as our example data intensive applications and parallelise BLAST over a fully distributed peer-to-peer network. BLAST is a popular bioinformatics toolset which can be used to compare two DNA sequences. The major usage of BLAST is searching a query of sequences inside a database for their similarities so as to identify whether they are new. When comparing single pair of sequences, BLAST is efficient. However, due to growing size of the databases, executing BLAST jobs locally produces prohibitively poor performance. Thus, methods for parallelising BLAST are sought. Traditional BLAST parallelisation approaches are all based on clusters. Clusters employ a number of computing nodes and high bandwidth interlinks between nodes. Cluster-based BLAST exhibits higher performance; nevertheless, clusters suffer from limited resources and scalability problems. Clusters are expensive, prohibitively so when the growth of the sequence database are taken into account. It involves high cost and complication when increasing the number of nodes to adapt to the growth of BLAST databases. Hence a Peer-to-Peer-based BLAST service is required. This thesis demonstrates our parallelisation of BLAST over Peer-to-Peer networks (termed ppBLAST), which utilises the free storage and computing resources in the Peer-to-Peer networks to complete BLAST jobs in parallel. In order to achieve the goal, we build three layers in ppBLAST each of which is responsible for particular functions. The bottom layer is a DHT infrastructure with the support of range queries. It provides efficient range-based lookup service and storage for BLAST tasks. The middle layer is the BitTorrent-based database distribution. The upper layer is the core of ppBLAST which schedules and dispatches task to peers. For each layer, we conduct comprehensive research and the achievements are presented in this thesis. For the DHT layer, we design and implement our DAST-DHT. We analyse balancing, maximum number of children and the accuracy of the range query. We also compare the DAST with other range query methodology and state that if the number of children is adjusted to more two, the performance of DAST overcomes others. For the BitTorrent-like database distribution layer, we investigate the relationship between the seeding strategies and the selfish leechers (freeriders and exploiters). We conclude that OSS works better than TSS in a normal situation

Evaluating the Robustness of Resource Allocations Obtained through Performance Modeling with Stochastic Process Algebra

Recent developments in the field of parallel and distributed computing has led to a proliferation of solving large and computationally intensive mathematical, science, or engineering problems, that consist of several parallelizable parts and several non-parallelizable (sequential) parts. In a parallel and distributed computing environment, the performance goal is to optimize the execution of parallelizable parts of an application on concurrent processors. This requires efficient application scheduling and resource allocation for mapping applications to a set of suitable parallel processors such that the overall performance goal is achieved. However, such computational environments are often prone to unpredictable variations in application (problem and algorithm) and system characteristics. Therefore, a robustness study is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime. In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of the allocation techniques is performed for finding a robustmapping from a set of initial mapping schemes. The numerical analysis of the performance models have confirmed similarity with the simulation results of earlier research available in existing literature. When compared to direct experiments and simulations, numerical models and the corresponding analyses are easier to reproduce, do not incur any setup or installation costs, do not impose any prerequisites for learning a simulation framework, and are not limited by the complexity of the underlying infrastructure or simulation libraries

Parallelisation for data-intensive applications over peer-to-peer networks

In Data Intensive Computing, properties of the data that are the input for an application decide running performance in most cases. Those properties include the size of the data, the relationships inside data, and so forth. There is a class of data intensive applications (BLAST, SETI@home, Folding@Home and so on so forth) whose performances solely depend on the amount of input data. Another important characteristic of those applications is that the input data can be split into units and these units are not related to each other during the runs of the applications. This characteristic helps this class of data intensive applications to be parallelised in the way where the input data is split into units and application runs on different computer nodes for certain portion of the units. SETI@home and Folding@Home have been successfully parallelised over peer-to-peer networks. However, they suffer from the problems of single point of failure and poor scalability. In order to solve these problems, we choose BLAST as our example data intensive applications and parallelise BLAST over a fully distributed peer-to-peer network. BLAST is a popular bioinformatics toolset which can be used to compare two DNA sequences. The major usage of BLAST is searching a query of sequences inside a database for their similarities so as to identify whether they are new. When comparing single pair of sequences, BLAST is efficient. However, due to growing size of the databases, executing BLAST jobs locally produces prohibitively poor performance. Thus, methods for parallelising BLAST are sought. Traditional BLAST parallelisation approaches are all based on clusters. Clusters employ a number of computing nodes and high bandwidth interlinks between nodes. Cluster-based BLAST exhibits higher performance; nevertheless, clusters suffer from limited resources and scalability problems. Clusters are expensive, prohibitively so when the growth of the sequence database are taken into account. It involves high cost and complication when increasing the number of nodes to adapt to the growth of BLAST databases. Hence a Peer-to-Peer-based BLAST service is required. This thesis demonstrates our parallelisation of BLAST over Peer-to-Peer networks (termed ppBLAST), which utilises the free storage and computing resources in the Peer-to-Peer networks to complete BLAST jobs in parallel. In order to achieve the goal, we build three layers in ppBLAST each of which is responsible for particular functions. The bottom layer is a DHT infrastructure with the support of range queries. It provides efficient range-based lookup service and storage for BLAST tasks. The middle layer is the BitTorrent-based database distribution. The upper layer is the core of ppBLAST which schedules and dispatches task to peers. For each layer, we conduct comprehensive research and the achievements are presented in this thesis. For the DHT layer, we design and implement our DAST-DHT. We analyse balancing, maximum number of children and the accuracy of the range query. We also compare the DAST with other range query methodology and state that if the number of children is adjusted to more two, the performance of DAST overcomes others. For the BitTorrent-like database distribution layer, we investigate the relationship between the seeding strategies and the selfish leechers (freeriders and exploiters). We conclude that OSS works better than TSS in a normal situation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo