Redundant movements in autonomous mobility: experimental and theoretical analysis

<p>Distributed load balancers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of autonomous mobile programs (AMPs) exhibit the same behaviour, and identifies two types of redundant movement (greedy effect). AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs.</p> <p>The paper explores the extent of greedy effects by simulating collections of AMPs and proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation. We establish new properties of balanced networks of AMPs, and use these to provide a theoretical analysis of greedy effects.</p&gt

Dynamic Loop Scheduling Using MPI Passive-Target Remote Memory Access

Scientific applications often contain large computationally-intensive parallel loops. Loop scheduling techniques aim to achieve load balanced executions of such applications. For distributed-memory systems, existing dynamic loop scheduling (DLS) libraries are typically MPI-based, and employ a master-worker execution model to assign variably-sized chunks of loop iterations. The master-worker execution model may adversely impact performance due to the master-level contention. This work proposes a distributed chunk-calculation approach that does not require the master-worker execution scheme. Moreover, it considers the novel features in the latest MPI standards, such as passive-target remote memory access, shared-memory window creation, and atomic read-modify-write operations. To evaluate the proposed approach, five well-known DLS techniques, two applications, and two heterogeneous hardware setups have been considered. The DLS techniques implemented using the proposed approach outperformed their counterparts implemented using the traditional master-worker execution model

ClustalXeed: a GUI-based grid computation version for high performance and terabyte size multiple sequence alignment

Abstract Background There is an increasing demand to assemble and align large-scale biological sequence data sets. The commonly used multiple sequence alignment programs are still limited in their ability to handle very large amounts of sequences because the system lacks a scalable high-performance computing (HPC) environment with a greatly extended data storage capacity. Results We designed ClustalXeed, a software system for multiple sequence alignment with incremental improvements over previous versions of the ClustalX and ClustalW-MPI software. The primary advantage of ClustalXeed over other multiple sequence alignment software is its ability to align a large family of protein or nucleic acid sequences. To solve the conventional memory-dependency problem, ClustalXeed uses both physical random access memory (RAM) and a distributed file-allocation system for distance matrix construction and pair-align computation. The computation efficiency of disk-storage system was markedly improved by implementing an efficient load-balancing algorithm, called "idle node-seeking task algorithm" (INSTA). The new editing option and the graphical user interface (GUI) provide ready access to a parallel-computing environment for users who seek fast and easy alignment of large DNA and protein sequence sets. Conclusions ClustalXeed can now compute a large volume of biological sequence data sets, which were not tractable in any other parallel or single MSA program. The main developments include: 1) the ability to tackle larger sequence alignment problems than possible with previous systems through markedly improved storage-handling capabilities. 2) Implementing an efficient task load-balancing algorithm, INSTA, which improves overall processing times for multiple sequence alignment with input sequences of non-uniform length. 3) Support for both single PC and distributed cluster systems.</p

Parallelisation of EST clustering

Master of Science - ScienceThe field of bioinformatics has been developing steadily, with computational problems related to biology taking on an increased importance as further advances are sought. The large data sets involved in problems within computational biology have dictated a search for good, fast approximations to computationally complex problems. This research aims to improve a method used to discover and understand genes, which are small subsequences of DNA. A difficulty arises because genes contain parts we know to be functional and other parts we assume are non-functional as there functions have not been determined. Isolating the functional parts requires the use of natural biological processes which perform this separation. However, these processes cannot read long sequences, forcing biologists to break a long sequence into a large number of small sequences, then reading these. This creates the computational difficulty of categorizing the short fragments according to gene membership. Expressed Sequence Tag Clustering is a technique used to facilitate the identification of expressed genes by grouping together similar fragments with the assumption that they belong to the same gene. The aim of this research was to investigate the usefulness of distributed memory parallelisation for the Expressed Sequence Tag Clustering problem. This was investigated empirically, with a distributed system tested for speed against a sequential one. It was found that distributed memory parallelisation can be very effective in this domain. The results showed a super-linear speedup for up to 100 processors, with higher numbers not tested, and likely to produce further speedups. The system was able to cluster 500000 ESTs in 641 minutes using 101 processors

CPL: A Core Language for Cloud Computing -- Technical Report

Running distributed applications in the cloud involves deployment. That is, distribution and configuration of application services and middleware infrastructure. The considerable complexity of these tasks resulted in the emergence of declarative JSON-based domain-specific deployment languages to develop deployment programs. However, existing deployment programs unsafely compose artifacts written in different languages, leading to bugs that are hard to detect before run time. Furthermore, deployment languages do not provide extension points for custom implementations of existing cloud services such as application-specific load balancing policies. To address these shortcomings, we propose CPL (Cloud Platform Language), a statically-typed core language for programming both distributed applications as well as their deployment on a cloud platform. In CPL, application services and deployment programs interact through statically typed, extensible interfaces, and an application can trigger further deployment at run time. We provide a formal semantics of CPL and demonstrate that it enables type-safe, composable and extensible libraries of service combinators, such as load balancing and fault tolerance.Comment: Technical report accompanying the MODULARITY '16 submissio