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%.

BACKGROUND: Parallel T-Coffee (PTC) was the first parallel implementation of the T-Coffee multiple sequence alignment tool. It is based on MPI and RMA mechanisms. Its purpose is to reduce the execution time of the large-scale sequence alignments. It can be run on distributed memory clusters allowing users to align data sets consisting of hundreds of proteins within a reasonable time. However, most of the potential users of this tool are not familiar with the use of grids or supercomputers. RESULTS: In this paper we show how PTC can be easily deployed and controlled on a super computer architecture using a web portal developed using Rapid. Rapid is a tool for efficiently generating standardized portlets for a wide range of applications and the approach described here is generic enough to be applied to other applications, or to deploy PTC on different HPC environments. CONCLUSIONS: The PTC portal allows users to upload a large number of sequences to be aligned by the parallel version of TC that cannot be aligned by a single machine due to memory and execution time constraints. The web portal provides a user-friendly solution.

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

Although a P2P scheme based unicast forward or application level multicast (ALM) is able to decentralize the video delivery process to reduce the VoD server load, the performance of such a scheme is limited by the high network resource requirement of the clients' communications. In this paper, we propose a new P2P architecture that uses multicast communications for peer collaborations. We introduce the concept of n-peers to m-peers cooperative collaboration to achieve high client delivery efficiency as well as unbounded scalable client collaboration capacity. Furthermore, the architecture selects peers by taking into account the underlying network infrastructure in order to avoid network saturation. We compared the new design with video delivery architecture based on Patching and Chaining schemes in a synthetic network model as well as a real Ethernet network topology. The experimental results showed that our design is better than Patching and Chaining in terms of admitted client requests and VoD server resource requirement. Furthermore, the results showed that our architecture is adaptable to any underlying physical networks.

For a high scalable VoD system, the distributed server architecture (DVoD) with more than one server-node is a cost-effective design solution. However, such a design is highly vulnerable to workload variations because the service capacity is limited. In this paper, we propose a new and efficient VoD architecture that combines DVoD with a P2P system. The DVoD's server-nodes is able to offer a minimum required quality of service (QoS) and the P2P system is able to provide the mechanism to increase the system service capacity according to client demands. Our P2P system is able to synchronize a group of clients in order to replace server-nodes in the delivery process. We compared the new VoD architecture with DVoD architecture based on classic multicast and P2P delivery policies (patching and chaining). The experimental results showed that our design is better than previous solutions, providing higher scalability

Distributed video-on-demand servers (DVS) are proposed as a solution to the limited streaming capacity and null scalability of large-scale centralized systems. Server interconnection topology plays an important role in video-on-demand systems' performance. This paper presents an analysis of different topologies and their influence over storage management and distribution, delivery policies performance, refusing requests occurrence, network consumption and scalability. To accomplish the proposal study, we have designed a complete simulation framework for DVS systems. Experimental results obtained under different workload conditions allow us to draw two important conclusions: First, a better connectivity implies a lower mean request service distance and lesser network requirements, improving multicast policies efficiency. Second, topology regularity is essential, as it allows a greater traffic balancing and provides more alternative routing paths. The analysis of global results shows that hypercube presents the best trade-off among all the evaluated metrics, providing a gradual and unlimited scalability for the DVS system.

In spite of the attractiveness of video-on-demand (VoD) services, their implementation up to the present has not been as widespread as could have been desired, due to centralized VoD systems having a limited streaming capacity and poor scaling. One-level proxy-based systems have been proposed to increase system capacity, but their scalability is still limited by the main net bandwidth. To achieve a scalable large-scale system (LVoD), we propose a hierarchical architecture, called double P-tree, based on a tree topology of independent local nets with proxies. To decentralize the architecture and facilitate the system's growth, the functionality of the proxy has been modified in such a way that it works at the same time as caching for the most-watched videos, and as a distributed mirror for the remaining videos. Moreover, the double P-tree topology interconnects local networks in order to improve connectivity and efficiency. The evaluation of this new architecture, through an analytical model, shows that the double P-tree architecture is a good approach to the design of flexible LVoD systems, without the need for using a complex server or high-bandwidth networks.

STW (Switch Time Warp) is a mechanism for limiting the optimism of the TW (Time Warp) method. The proposed method achieves significant time/performance improvements for rollback reduction in optimistic parallel discrete event simulation (PDES). The STW uses a cost function to decide if a process is running in an overoptimistic state. The STW mechanism will decide the LP's execution order within the processor and the amount of CPU time to assign to a logical process to reduce the number of rollbacks.