Remo Suppi

R/parallel – speeding up bioinformatics analysis with R

BackgroundR is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians.ResultsWe have designed and implemented an R add-on package, R/parallel, that extends R by adding user-friendly parallel computing capabilities. With R/parallel any bioinformatician can now easily automate the parallel execution of loops and benefit from the multicore processor power of todays desktop computers. Using a single and simple function, R/parallel can be integrated directly with other existing R packages. With no need to change the implemented algorithms, the processing time can be approximately reduced N-fold, N being the number of available processor cores.ConclusionR/parallel saves bioinformaticians time in their daily tasks of analyzing experimental data. It achieves this objective on two fronts: first, by reducing development time of parallel programs by avoiding reimplementation of existing methods and second, by reducing processing time by speeding up computations on current desktop computers. Future work is focused on extending the envelope of R/parallel by interconnecting and aggregating the power of several computers, both existing office computers and computing clusters.

Impact of parallel programming models and CPUs clock frequency on energy consumption of HPC systems

Energy consumption has become one of the greatest challenges in the field of high performance computing (HPC). The energy cost produced by supercomputers during the lifetime of the installation is similar to acquisition. Thus, besides its impact on the environment, energy is a limiting factor for the HPC. Our research aims to reduce the energy consumption of computer systems to run parallel HPC applications. In this article we analyse the possible influence on the energy consumption of parallel programming paradigms of shared memory (OpenMP) and message passing (MPI), and the behaviour of systems at different clock frequencies of CPUs. The results show that the programming model has a major impact on the energy consumption of computer systems. It was found that the impact of reduced clock frequencies on the execution time, energy efficiency, and maximum power consumption depends not only on the type of application but also on its implementation in a specific programming model. We believe that another criteria to consider when choosing a parallel programming model is the impact on energy consumption.

High performance distributed cluster-based individual-oriented fish school simulation

Abstract Individual-oriented simulation allows us to represent the global behavior of a system through local interaction in discrete time steps. As we face up close-to-reality models and large-scale workloads, we focus on turning from traditional approaches towards distributed simulation in order to obtain more accurate results in less time. One of the main problems in distributed simulation is how to distribute individuals efficiently through distributed architecture. Individual-oriented systems can be implemented in a distributed fashion by using either a grid-based or cluster-based approach. On one hand, grid-based approaches consist of assigning to each node a simulation space portion, together with the set of individuals currently residing in that area. On the other hand, cluster-based approaches consist of assigning to each node a fixed set of individuals. In this work we present a cluster-based method based on Voronoi diagrams and covering radius criterion in order to avoid unnecessary interaction between individuals. We can show experimentally that our proposal reduces the communication and computing times significantly increasing simulation efficiency.

Proximity load balancing for distributed cluster-based individual-oriented fish school simulations.

Abstract Partitioning and load balancing are highly important issues in distributed individual-oriented simulation. Choosing how to distribute individuals on the distributed environment can be a crucial factor at the moment of executing the simulation. Partitioning an individual-oriented system should be efficient in order to reduce communication involved in interaction between individuals belong to different logical processes. Furthermore, if the individual-oriented model exhibits mobility patterns, we should be able to maintain the load balancing in order to keep the global application performance. In this work, we present a proximity load balancing strategy for a distributed cluster-based individual-oriented fish school simulator. On one hand, we implement a robust cluster-based partitioning method by means of covering radius criterion and voronoi diagrams. We use a proximity criterion to distribute individuals on the distributed architecture. On the other hand, we propose a proximity load balancing strategy in order to maintain the application performance as the simulation progresses.

Distributed P2P merging policy to decentralize the multicasting delivery

Advances in network technology make multicast one of the most feasible video streaming delivery techniques for the near future. However, the scalability of a multicast VoD system is limited by the server bandwidth. In this paper, we propose a new multicast delivery scheme that allows every active client to collaborate with the server in order to scale the VoD system performance beyond the servers physical limitations. The solution combined the multicast delivery scheme and peer-2-peer paradigm in order to decentralize the delivery process. The new video delivery scheme is able to merge two or more multicast channels using distributed collaborations between a group of clients. We compared the new policy with chaining and patching schemes and the experimental results showed that our policy is better than previous schemes in terms of reduction of resource requirements and local network load. Compared with multicast patching policy, the new scheme reduced the resource requirement up to 77.5% while the local network load was 66.9% lower than a peer-2-peer chaining policy.

Using PDES to Simulate Individual-Oriented Models in Ecology: A Case Study

The present work outlines the results of the Parallel Discrete Event Simulation (PDES) utilization for solving an individual based model: Fish Schools. This type of model cannot be solved through analytical methods, thus simulation techniques are necessary. The greater problem presented by this type of model is the high computing capacity necessary for solving middle-high size problems (hundreds to thousands of individuals). PDES is presented as a useful and low cost tool for individual-oriented models simulation, since it allows a scalable and efficient solution in accordance with the problem to be simulated.

A Fuzzy Logic Fish School Model

This paper summarizes our work on fuzzy modeling for an Individual-oriented Model (IoM). Our model is particularly geared toward simulating the movement of fish schools, derived from the model by Huth and Wissel. The background and motivation for the problem as well as a description of biological model are given here. A fuzzy logic implementation is discussed based on the mathematical model proposed. Finally, the experiments performed to demonstrate that our model represents the real behavior of fish schools. Keywords: Fuzzy Logic, Individual-oriented Models (IoM), Fish School.

Dynamic distributed collaborative merging policy to optimize the multicasting delivery scheme

The advance of Internet 2 and the proliferation of switches and routers with level three functionalities made the multicast one of the most feasible video streaming delivering techniques for the near future. Assuming this to be true, this study addressed the over-load situation that a streaming server could suffer due to client requests. As a solution, we proposed new multicast delivery scheme that allows every active client to collaborate with the server regardless of the video that they are watching, alleviating server loads, and therefore server resource requirements. The solution combined the multicast delivery scheme and client-side buffer collaboration in order to decentralize the delivery process. The new video delivering scheme was designed as two separate policies: the first policy used client collaboration to deliver first part of videos and the second policy could merge two or more multicast channels using distributed collaboration between a group of clients. Experimental results show that this scheme is better than previous schemes in terms of resource requirements and scalability.

Improving Communication Patterns for Distributed Cluster-based Individual-oriented Fish School Simulations

Parallel discrete event simulation (PDES) have shown to be an useful paradigm for simulating complex and large-scale models. An individual-oriented approach allows modelers capture complex emerging global behaviors generated by simple local interaction, like observed in self-organized systems. Usually, this type of simulations are highly expensive in terms of computing and communications. One one hand, we can reduce the computing involved in individual interactions by means of developing a robust partitioning method. On the other hand, we have to be able to efficiently handle a huge number of individuals interacting with other individuals stored in memory of remote processors. In this work we will analyze and compare three communication strategies: synchronous and asynchronous message passing (via MPI) and bulk-synchronous parallel (BSP) for our distributed cluster-based individual-oriented fish school simulator. In this type of simulations, the main contributions of our work are: a) we showed that distributed time-driven simulations do not always improve the performance when using synchronous communication strategies, b) we show asynchronous communications strategies are more efficient. In addition, we have verified that the bulk-synchronous parallel method is a scalable.

High performance individual-oriented simulation using complex models☆

Abstract Computational simulation has been used as a powerful tool to represent the dynamical behavior of systems based on complex analytic models. These types of models have two main drawbacks: (a) limitations due to the degree of abstraction needed to simulate them, (b) high computing power to simulate a heavily simplified models. The computing power available today can overcome these limitations to perform quicker simulations of complex models that are closer to reality. In this paper, the experiments and performance analysis of a distributed simulation for a complex individual oriented model (fish schools) are presented. The development of the fish school simulator includes the possibility of working with large models that include large numbers of fish (>106 of individuals), predators and obstacles in the simulated world.