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.htmlContact: cedric.notredame@crg.es

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.

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.

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