Abstract DNA methylation analysis has become an important topic in the study of human health. In previous work, we developed a suite of tools to perform this analysis. It includes HPG-Dhunter, a web-based tool for automatic detection of differentially methylated regions (DMRs) between different samples. The back-end of that tool receives an undefined number of simultaneous requests to detect DMRs on different datasets. Currently, simultaneous requests are queued and processed one at a time. This paper proposes a parallel architecture where multiple daemons serve requests simultaneously. Daemons can also share the same physical GPUs. A scheduler manages requests and forwards them to daemons. The number of daemons per GPU is configurable, thus adapting the architecture to the available hardware. Results show that the proposed parallel architecture hugely reduces the execution time. Furthermore, the speedup increases proportionally to the number of available GPUs (up to 7.47x in our experimental setup).

The fight against sexism is nowadays one of the flagship social movements in western countries. Adolescence is a crucial period, and some empirical studies have focused on the socialization of teenagers, proving that the socialization with the surrounding environment prevent sexist practices. In a previous work, we developed and tested the effectiveness of a mobile app, called Liad@s , with the goals of helping teenagers to prevent sexism and build healthy couple relationships. In this article, we carry out a study where (using a real situation) we compare the effectiveness of the Liad@s app in front of traditional interventions like a workshop about sexism for teenagers. Also, we evaluate the usability of the app and the user satisfaction with this application. In this study, our primary hypothesis is that the effectiveness of using our mobile application, in terms of knowledge acquired about sexism, would be at least as good as attending the workshop. Our secondary hypothesis is that the user satisfaction with the mobile application would be higher than the one with the workshop, causing a preference for the app. The results of this study show significant differences in learning appeared between gender and between the two different procedures when separately evaluating the data collected from both hostile sexism (HS) and benevolent sexism (BS) questionnaires. These results validate our primary hypothesis. Also, most of the population under study preferred the mobile app in front of the traditional workshop, validating also our secondary hypothesis.

Abstract The Large Hadron Collider (LHC) is about to enter its third run at unprecedented energies. The experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousands of physics users. The ATLAS EventIndex project, currently running in production, builds a complete catalogue of particle collisions, or events, for the ATLAS experiment at the LHC. The distributed nature of the experiment data model is exploited by running jobs at over one hundred Grid data centers worldwide. Millions of files with petabytes of data are indexed, extracting a small quantity of metadata per event, that is conveyed with a data collection system in real time to a central Hadoop instance at CERN. After a successful first implementation based on a messaging system, some issues suggested performance bottlenecks for the challenging higher rates in next runs of the experiment. In this work we characterize the weaknesses of the previous messaging system, regarding complexity, scalability, performance and resource consumption. A new approach based on an object-based storage method was designed and implemented, taking into account the lessons learned and leveraging the ATLAS experience with this kind of systems. We present the experiment that we run during three months in the real production scenario worldwide, in order to evaluate the messaging and object store approaches. The results of the experiment show that the new object-based storage method can efficiently support large-scale data collection for big data environments like the next runs of the ATLAS experiment at the LHC.

BACKGROUND: Software tools for analyzing DNA methylation do not provide graphical results which can be easily identified, but huge text files containing the alignment of the samples and their methylation status at a resolution of base pairs. There have been proposed different tools and methods for finding Differentially Methylated Regions (DMRs) among different samples, but the execution time required by these tools is large, and the visualization of their results is far from being interactive. Additionally, these methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance when used with real datasets, probably due to the different approaches they use for DMR identification. Thus, a tool which automatically detects DMRs among different samples and interactively visualizes DMRs at different scales (from a bunch to ten of millions of DNA locations) can be the key for shortening the DNA methylation analysis process in many studies. RESULTS: In this paper, we propose a software tool based on the wavelet transform. This mathematical tool allows the fast automatic DMR detection by simple comparison of different signals at different resolution levels. Also, it allows an interactive visualization of the DMRs found at different resolution levels. The tool is publicly available at https://grev-uv.github.io/ , and it is part of a complete suite of tools which allow to carry out the complete process of DNA alignment and methylation analysis, creation of methylation maps of the whole genome, and the detection and visualization of DMRs between different samples. CONCLUSIONS: The validation of the developed software tool shows similar concordance with other well-known and extended tools when used with real and synthetic data. The batch mode of the tool is capable of automatically detecting the existing DMRs for half (twelve) of the human chromosomes between two sets of six samples (whose.csv files after the alignment and mapping procedures have an aggregated size of 108 Gigabytes) in around three hours and a half. When compared to other well-known tools, HPG-DHunter only requires around 15% of the execution time required by other tools for detecting the DMRs.

BACKGROUND: DNA methylation is an important mechanism of epigenetic regulation in development and disease. New generation sequencers allow genome-wide measurements of the methylation status by reading short stretches of the DNA sequence (Methyl-seq). Several software tools for methylation analysis have been proposed over recent years. However, the current trend is that the new sequencers and the ones expected for an upcoming future yield sequences of increasing length, making these software tools inefficient and obsolete. RESULTS: In this paper, we propose a new software based on a strategy for methylation analysis of Methyl-seq sequencing data that requires much shorter execution times while yielding a better level of sensitivity, particularly for datasets composed of long reads. This strategy can be exported to other methylation, DNA and RNA analysis tools. CONCLUSIONS: The developed software tool achieves execution times one order of magnitude shorter than the existing tools, while yielding equal sensitivity for short reads and even better sensitivity for long reads.

Different communication-aware mapping techniques were proposed in recent years for improving the performance of distributed systems based on both, off-chip and on-chip networks. Some of these proposals were based on heuristic search for finding pseudo-optimal assignments of tasks and processing elements. However, the technology integration improvements have allowed a significant increase in the number of network nodes, requiring the acceleration of the heuristic search. In this paper, we propose a comparative study of the local search method used in a communication-aware mapping technique, when implemented on different parallel architectures. We compare the performance provided by a version of the local search method when executed on a single Graphics Processing Unit (GPU) with the one provided by the MPI version executed on a supercomputer with the same theoretical performance of the GPU platform, in order to study a fair scenario. We have considered a GPU based on the Fermi architecture, evaluating the improvements achieved by some new architectural features of this platform. The results show that a mixed parallel implementation on a single GPU outperforms the MPI implementation of the local search method. These results validate the GPU implementation as a very cost-effective accelerator for the local search method.

MOTIVATION: DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. As it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed. RESULTS: We present a new software tool, called HPG-Methyl, which efficiently maps bisulphite sequencing reads on DNA, analyzing DNA methylation. The strategy used by this software consists of leveraging the speed of the Burrows-Wheeler Transform to map a large number of DNA fragments (reads) rapidly, as well as the accuracy of the Smith-Waterman algorithm, which is exclusively employed to deal with the most ambiguous and shortest reads. Experimental results on platforms with Intel multicore processors show that HPG-Methyl significantly outperforms in both execution time and sensitivity state-of-the-art software such as Bismark, BS-Seeker or BSMAP, particularly for long bisulphite reads. AVAILABILITY AND IMPLEMENTATION: Software in the form of C libraries and functions, together with instructions to compile and execute this software. Available by sftp to [email protected] (password 'anonymous'). CONTACT: [email protected] or [email protected].

Last years have witnessed the widespread use of online terrain visualization applications. However, the significant improvements achieved in sensing technologies have allowed an increasing size of the terrain databases. These increasing sizes represent a serious drawback when terrain data must be transmitted and rendered at interactive rates. In this paper, we propose a novel wavelet-tiled pyramid for compressing terrain data that replaces the traditional multiresolution pyramid usually used in wavelet compression schemes. The new wavelet-tiled pyramid modifies the wavelet analysis and synthesis processes, allowing an efficient transmission and reconstruction of terrain data in those applications based on multiresolution tiled pyramids. A comparative performance evaluation with the currently existing techniques shows that the proposed scheme obtains a better compression ratio of the terrain data, reducing the storage space and transmission bandwidth required, and achieving a better visual quality of the virtual terrain reconstructed after data decompression.

Collaborative Augmented Reality (CAR) systems allow multiple users to share a real world environment including computer generated images in real time. Currently, the hardware features of most mobile phones not only provide excellent multimedia services, but it also includes wireless network capabilities that provides a natural platform for CAR systems. However, the wide variety of these hardware features can have important effects on the performance of the mobile CAR applications. This paper presents the experimental characterization of CAR applications for mobile phones in regard to well-known performance metrics in distributed systems. Characterization results show that the most time consuming stage in a CAR application is the marker detection stage. Moreover, the rendering stage is decoupled on some devices. This decoupling process allows avoiding low refresh rate, facilitating the collaborative work. These results can be used as the basis for an efficient design of CAR systems and applications.

The design flow of network-on-chip (NoCs) include several key issues. Among other parameters, the decision of where cores have to be topologically mapped and also the routing algorithm represent two highly correlated design problems that must be carefully solved for any given application in order to optimize several different performance metrics. The strong correlation between the different parameters often makes that the optimization of a given performance metric has a negative effect on a different performance metric. In this paper we propose a new strategy that simultaneously refines the mapping and the routing function to determine the Pareto optimal configurations which optimize average delay and routing robustness. The proposed strategy has been applied on both synthetic and real traffic scenarios. The obtained results show how the solutions found by the proposed approach outperforms those provided by other approaches proposed in literature, in terms of both performance and fault tolerance.

A necessary condition for providing quality of service to distributed virtual environments (DVEs) is to provide a system response below a maximum threshold to the client computers. In this sense, latency-aware partitioning methods try to provide response times below the threshold to the maximum number of client computers as possible. These partitioning methods should find an assignment of clients to servers that optimizes system throughput, system latency, and partitioning efficiency. In this paper, we present a new algorithm based on greedy randomized adaptive search procedure with memory for finding the best solutions as possible to this problem. We take into account several different alternatives in order to design both the constructive phase and the local search phase of this multistart metaheuristic for combinatorial problems. Additionally, we enhance this basic approach with some intensification strategies that improve the efficiency of the basic search method. Performance evaluation results show that the new algorithm increases the performance provided by other metaheuristics when applied to solve the latency-aware partitioning problem in DVE systems.

Networks on Chip (NoCs) have been shown as an efficient solution to the complex on-chip communication problems derived from the increasing number of processor cores. One of the key issues in the design of NoCs is the reduction of both area and power dissipation. As a result, two-dimensional meshes have become the preferred topology, since it offers low and constant link delay. Unfortunately, manufacturing defects or even real-time failures often make the resulting topology to become irregular, preventing the use of traditional routing algorithms. This scenario shows the need for topology-agnostic routing algorithms that provide a valid routing solution when applied over any topology. Moreover, in order to deal with run-time failures, the routing algorithm should be able to fit runtime constraints. This paper proposes a new communication-aware routing technique, referred to as CART, that optimizes the network performance for application-specific NoCs. CART combines a flexible, topology-agnostic routing algorithm with a communication-aware mapping technique that matches the traffic generated by the application with the available network bandwidth. Since the mapping technique can be pruned as needed in order to fit either quality function values or time constraints, CART can be adapted to fit with different computational costs. The evaluation results show that CART significatively improves network performance in terms of both latency and power consumption.

Simulating the realistic behavior of large crowds of autonomous agents is still a challenge for the computer graphics community. In order to handle large crowds, some scalable architectures have been proposed. Nevertheless, the effective use of distributed systems requires the use of partitioning methods that can properly assign different sets of agents to the existing distributed resources. In this paper, we propose the improvement of the partitioning method for distributed crowd simulations by using irregular shape regions. Concretely, we propose the partition of the virtual world using convex hulls. The performance evaluation results show that the convex Hull method outperforms the rest of the considered methods in terms of both fitness function values and execution times, regardless of the movement pattern followed by the agents. These results show that the shape of the regions in the partition can improve the performance of the partitioning method, rather than the heuristic method used.

Crowd simulation can be considered as a special case of virtual environments where avatars are intelligent agents instead of user-driven entities. These applications require both rendering visually plausible images of the virtual world and managing the behavior of autonomous agents. Although several proposals have focused on the software architectures for these systems, the scalability of crowd simulation is still an open issue. In this paper, we propose a scalable architecture that can manage large crowds of autonomous agents at interactive rates. This proposal consists of enhancing a previously proposed architecture through the efficient parallelization of the action server and the distribution of the semantic database. In this way, the system bottleneck is removed, and new action servers (hosted each one on a new computer) can be added as necessary. The evaluation results show that the proposed architecture is able to fully exploit the underlying hardware platform, regardless of both the number and the kind of computers that form the system. Therefore, this system architecture provides the scalability required for large-scale crowd simulation.

Abstract The increasing popularity of multi‐player online games is leading to the widespread use of large‐scale Distributed Virtual Environments (DVEs) nowadays. In these systems, peer‐to‐peer (P2P) architectures have been proposed as an efficient and scalable solution for supporting massively multi‐player applications. However, the main challenge for P2P architectures consists of providing each avatar with updated information about which other avatars are its neighbors. This problem is known as the awareness problem. In this paper, we propose a comparative study of the performance provided by those awareness methods that are supposed to fully solve the awareness problem. This study is performed using well‐known performance metrics in distributed systems. Moreover, while the evaluations shown in the literature are performed by executing P2P simulations on a single (sequential) computer, this paper evaluates the performance of the considered methods on actually distributed systems. The evaluation results show that only a single method actually provides full awareness to avatars. This method also provides the best performance results. Copyright © 2008 John Wiley & Sons, Ltd.

Crowd simulations require both rendering visually plausible images and managing the behavior of autonomous agents. Therefore, these applications need an efficient design that allow them to simultaneously tackle these two requirements. Although several proposals have focused on the software architectures for these systems, no proposals have focused on the computer systems supporting them. In this paper, we analyze the computer architectures used in the literature to support virtual environments. Also, we propose a distributed computer architecture efficient enough to support simulations of thousand of autonomous agents. This proposal consists of a cluster of computers in order to improve flexibility and robustness, as well as a hierarchical software architecture that efficiently provides consistency. Performance evaluation results show that the trade-off between flexibility and consistency allows to effi- ciently manage thousands of autonomous agents.

The current expansion of multi-player online games has promoted the growth of large scale distributed virtual environments (DVEs). In these systems, peer-to-peer architectures have been proved as the most scalable scheme for supporting massively multi-player applications. Nevertheless, the interactions among clients that can take place in this type of systems can lead to the temporal saturation of some of the clients. Since a client saturation has an effect on other clients, these situations limit the performance of peer-topeer DVEs. In this paper, we propose an adaptive technique for avoiding the saturation of the client computers in DVE systems based on peer-to-peer architectures. This technique is based on monitoring the client state and discarding some of the messages received from other clients when the client is close to saturation. The evaluation results show that the proposed method improves the system performance without having an effect on the awareness rate, regardless of the movement pattern that avatars can follow. As a result, both the performance and the scalability of peer-to-peer DVEs are significantly improved.

The current expansion of multi-player online games has promoted the growth of large scale distributed virtual environments (DVEs). In these systems, peer-to-peer architectures have been proved as an efficient scheme for supporting massively multi-player applications. In order to research on this type of architecture, stand-alone simulators do not take into account inconsistencies due to network latency, and it is necessary to develop a distributed tool that allows to simulate large-scale DVEs in an efficient way. In this paper, we propose a distributed platform for simulating the behavior of peer-to-peer DVEs. This simulator is implemented following a modular architecture. It is capable of providing the main performance metrics in distributed systems, and it contains all the elements involved in real DVE simulations like the awareness method and the graphic interface. As a result, this tool can be used in real simulations of peer-to-peer DVEs, becoming an invaluable tool for capturing the behavior of this kind of systems.

Last years have witnessed how crowd simulations have become an essential tool for many virtual environment applications. These applications require both rendering visually plausible images and managing the behavior of autonomous agents, and therefore they need a scalable design that allows them to simultaneously tackle these two requirements. One of the main problems in the design of a scalable crowd simulation consists of efficiently distributing among different computers the semantic database containing the virtual world. In this paper, we propose a genetic approach for distributing the semantic database of crowd simulations in such a way that the dependencies among the computers hosting the pieces of the database are minimized. The proposed approach avoids the saturation of these computers by ensuring that the size of the pieces assigned to each computer is properly balanced. The performance evaluation results show that the proposed approach significantly reduces the resulting overhead in regard to other local search methods, regardless of the movement pattern of the agents. Therefore, it allows an effective partition of the semantic database.

Large scale distributed virtual environments (DVEs) have become a major trend in distributed applications, mainly due to the enormous popularity of multi-player online games in the entertainment industry. Since architectures based on networked servers seem to be not scalable enough to support massively multi-player applications, peer-to-peer (P2P) architectures have been proposed as an efficient and truly scalable solution for this kind of systems. However, in order to design efficient DVEs based on peer-to-peer architectures these systems must be characterized, measuring the impact of different client behaviors on system performance. This paper presents the experimental characterization of peer-to-peer distributed virtual environments in regard to well-known performance metrics in distributed systems. Characterization results show that system saturation is inherently avoided due to the peer-to-peer scheme, as it could be expected. Also, these results show that the saturation of a given client exclusively has an effect on the surrounding clients in the virtual world, having no noticeable effect at all on the rest of avatars. Finally, the characterization results show that the response time offered to client computers greatly depends on the number of new connections that these clients have to make when new neighbors appear in the virtual world. These results can be used as the basis for an efficient design of peer-to-peer DVE systems.

Distributed virtual environment (DVE) systems allow multiple users working on different client computer's interconnected through different networks to interact in a shared virtual world. In these systems, latency is crucial for providing an acceptable quality of service (QoS), since it determines how fast client computers are reported about changes in the shared virtual scene produced by other client computers. This paper presents in a unified manner a partitioning approach for providing a latency below a threshold to the maximum number of users as possible in DVE systems. This partitioning approach searches the assignment of avatars, which represents the best trade-off among system latency, system throughput, and partitioning efficiency when solving the partitioning problem. Evaluation results show that the proposed approach not only maximizes system throughput, but also allows the system to satisfy, if possible, any specific latency requirement needed for providing QoS. This improvement is achieved without decreasing either image resolution or quality of animation, and it can be used together with other techniques already proposed. Therefore, it can contribute to provide QoS in DVEs.

The last years have witnessed a dramatic growth in the number as well as in the variety of distributed virtual environment systems. These systems allow multiple users, working on different client computers that are interconnected through different networks, to interact in a shared virtual world. One of the key issues in the design of scalable and cost-effective DVE systems is the partitioning problem. This problem consists of efficiently assigning the existing clients to the servers in the system and some techniques have been already proposed for solving it. This paper experimentally analyzes the correlation of the quality function proposed in the literature for solving the partitioning problem with the performance of DVE systems. Since the results show an absence of correlation, we also propose the experimental characterization of DVE systems. The results show that the reason for that absence of correlation is the nonlinear behavior of DVE systems with regard to the number of clients in the system. DVE systems reach saturation when any of the servers reaches 100 percent of CPU utilization. The system performance greatly decreases if this limit is exceeded in any server. Also, as a direct application of these results, we present a partitioning method that is targeted to keep all the servers in the system below a certain threshold value of CPU utilization, regardless of the amount of network traffic. Evaluation results show that the proposed partitioning method can improve DVE system performance, regardless of both the movement pattern of clients and the initial distribution of clients in the virtual world.

Many research activities have focused on the problem of task scheduling in heterogeneous systems from the computational point of view. However, an ideal scheduling strategy would also take into account the communication requirements of the applications and the communication bandwidth available in the network. One of the major problems to be solved in the development of this scheduling strategy is precisely the measurement of the communication requirements for each application. We propose a clustering-based method to characterize the communications between processes generated by message-passing applications. This technique provides a model consisting of several partitions of the processes generated by the application. Also, we propose a criterion to measure the quality of the obtained partitions. This approach can be used when a given application is repeatedly executed with different input data. Results show that the proposed method can provide a partition with the highest ratio between the intracluster and the intercluster required communication bandwidth. This partition can be used to map groups of processes to processors in the heterogeneous system.

Many research activities have focused on the problem of task scheduling in heterogeneous systems from the computational point of view. However an ideal scheduling strategy would also take into account the communication requirements of the applications and the communication bandwidth that the network can offer. In this paper, we first propose a criterion to measure the suitability of each allocation of network resources to each parallel application, according to the communication requirements. Second, we propose a scheduling technique based exclusively on this criterion that provides a near-optimal mapping of processes to processors according to the communication requirements. Evaluation results show that the use of this scheduling technique fully exploits the available network bandwidth, greatly improving network performance. Therefore, the proposed scheduling technique may be used in the design of communication-aware scheduling strategies for those situations where the communication requirements are the system performance bottleneck.