Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. The large number of computing nodes, resources heterogeneity and diversity of application requirements are factors that turn the scheduling into an NP-Hard problem. Evolutionary algorithms have proved to be effective for scheduling applications. In this paper, we present a novel approach combining particle swarm optimization and a genetic algorithm to solving the resource matching and scheduling of parallel applications in Federated cluster environments. The proposed hybrid meta-heuristic, referred to as MPSO-FGA, not only minimizes the overall energy consumption but also the makespan for a whole workload. The experimental results show the superiority of evolutionary algorithms over basic heuristics. The hybrid meta-heuristic is able to obtain similar results to a genetic algorithm in terms of energy consumption and makespan but reducing the time for scheduling decisions by two orders of magnitude.

Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. Most of the techniques have focused on determining the computing requirements based on load predictions and thus turning unnecessary nodes on and off. Nevertheless, once the available resources have been configured, new opportunities arise for reducing energy consumption by providing optimal matching of parallel applications to the available computing nodes. Current research in scheduling has concentrated on not only optimizing the energy consumed by the processors but also optimizing the makespan, i.e., job completion time. The large number of heterogeneous computing nodes and variability of application-tasks are factors that make the scheduling an NP-Hard problem. Our aim in this paper is a multi-objective genetic algorithm based on a weighted blacklist able to generate scheduling decisions that globally optimizes the energy consumption and the makespan.

Multiple sequence alignment (MSA) is crucial for high-throughput next generation sequencing applications. Large-scale alignments with thousands of sequences are necessary for these applications. However, the quality of the alignment of current MSA tools decreases sharply when the number of sequences grows to several thousand. This accuracy degradation can be mitigated using global consistency information as in the T-Coffee MSA-Tool, which implements a consistency library. However, consistency-based methods do not scale well because of the computational resources required to calculate and store the consistency information, which grows quadratically. In this paper, we propose an alternative method for building the consistency-library. To allow unlimited scalability, consistency information must be discarded to avoid exceeding the environment memory. Our first approach deals with the memory limitation by identifying the most important entries, which provide better consistency. This method is able to achieve scalability, although there is a negative impact on accuracy. The second proposal, aims to reduce this degradation of accuracy, with three different methods presented to attain a better alignment.

Multiple sequence alignment (MSA) is one of the most useful tools in bioinformatics. However, the growth of sequencing data imposes further difficulties for aligning it with traditional tools. For large-scale alignments with thousands of sequences it will be necessary to use and take profit of the high performance computing (HPC). This paper, focused on the consistency-based T-Coffee MSA tool, presents several innovative solutions with the objective of improving its efficiency, scalability and accuracy. The results obtained show that our approach doubles the speed-up of the progressive alignment, thus allowing T-Coffee to align twice as many sequences while also improving the alignment accuracy.

Multiple Sequence Alignment (MSA) is an extremely powerful tool for important biological applications, such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In this paper we propose a new approach to reduce the computational requirements of TCoffee, a memory demanding MSA tool that uses a consistency based scheme to produce more accurate alignments. Our goal is to minimize the memory constraints in order to increase the performance and scalability of the application. The experimental results show that our approach is able to reduce the memory consumption and increase both the time performance and the number and the length of sequences that the method can align. In summary, it is able to reduce the memory requirements by between 72% and 58% depending on the optimization level, improving the alignment scalability as a whole. Also, the library reduction yields a further improvement: the alignment execution time can be reduced by up to 92%. These results are obtained without a significant impact on the final alignment quality, that declines by less than 3%.

SUMMARY: We present the first parallel implementation of the T-Coffee consistency-based multiple aligner. We benchmark it on the Amazon Elastic Cloud (EC2) and show that the parallelization procedure is reasonably effective. We also conclude that for a web server with moderate usage (10K hits/month) the cloud provides a cost-effective alternative to in-house deployment. AVAILABILITY: T-Coffee is a freeware open source package available from http://www.tcoffee.org/homepage.html

The exploitation of parallelism in a message-passing platform implies a previous modelling phase of the parallel application as a task graph, which properly reflects its temporal behaviour. In this paper, we analyse the classical task graph models of the literature and their drawbacks when modelling message-passing programs with arbitrary task structure. We define a new task graph model called TTIG (Temporal Task Interaction Graph) that integrates the classical models used in the literature. The TTIG allows to explicitly capture the ability of concurrency of adjacent tasks for applications where adjacent tasks can communicate at any point inside them. A mapping strategy is developed from this model, that minimizes the expected execution time by properly exploiting task parallelism. The effectiveness of this approach has been proved in different experimentation scopes for a wide range of message-passing applications.

Parallelism in applications that act on a stream of input data can be exploited with two different approaches, spatial and temporal. In this paper we propose a new task mapping algorithm, called EXPERT, to exploit temporal parallelism efficiently when the streaming application is running in a pipeline fashion. We compare the performance of spatial and temporal approaches, in terms of latency and throughput for a video compression application. The results show that the pipeline execution with the task assignment provided by EXPERT algorithm, significantly overcomes spatial parallelism. Additionally, this temporal parallelism presents better scalability results when the dimension of the problem is augmented