Summary The cells of the sinoatrial node (SAN) are self‐excitable entities that show a coupled electrical pattern that consists of the synchronized activation of action potentials that determine the heart rate. To accurately describe the behavior of cell membrane proteins, theoretical biophysicists have devoted themselves to the study of the electrical activity of individual cells, which involves solving a large number of coupled differential equations. This computational limitation makes the modeling of a large number of cells unattainable, since the intracellular distribution of Ca2+ must be considered and this fact increases in grand extent the number of differential equations involved. In this work, we explore different parallel architectures (using OpenMP, MPI, and CUDA libraries) to show advances in the computational modeling and simulation of the SAN using a multicellular array in which the cells are endowed of heterogeneous conductances and are electrically coupled, considering a variable connectivity among them.

The sinoatrial node (SAN) has the function of initiating a depolarizing wave that runs throughout the heart. This wave produces the muscular contraction necessary to blood pumping in animals. In recent years different works have been proposed to simulate the electric potential behaviour of a single sinoatrial cell (SANC) and groups of cells, hence a set of differential equations needs to be solved for each microsecond of simulation. An important drawback comes up when the synchronization of millions of SANCs is required involving a huge processing time. Since the simulation of the behavior of a set of cells is an open research topic, it is important to propose efficient tools to reduce response times; unfortunately, because the complexity of the existent models of SANC, very scarce work has been done to this end. This paper proposes three parallel algorithms to simulate the synchronization of a set of SANCs based on the model of Severi (2012). The proposed approaches are built using OpenMP, MPI, and CUDA, in order to compare the benefits given by different computing platforms. We found that all parallel versions perform better when defining a cell per processing unit; however the CUDA version gives the best results in scalability and performance.

In this paper, we propose both reactive and proactive balancing traffic path recommendation mechanisms, which we refer to as Bal-Traf and Abs-Bal, respectively. Bal-Traf is initiated when a certain output road segment located at any road intersection is detected in an overloaded situation. In the event that the existing traffic density of any output road exceeds its optimal capacity, Bal-Traf recommends that those vehicles that plan to pass over this road segment as next hop choose another, less congested output road segment. On the other hand, Abs-Bal is a proactive balancing traffic mechanism. Its main purpose is to distribute input traffic completely even among all output road segments at intersections. Moreover, Abs-Bal considers the best travel time of vehicles in addition to the goal of balancing traffic. From the experimental results, we can see that Bal-Traf eliminates the number of overwhelmed road segments over the road network in scenarios with only partial network congestion. It also decreases the number of congested road segments in scenarios with complete network congestion. However, it increases the density drastically over the remaining congested road segments in these scenarios. Abs-Bal performs well in decreasing the percentage of congested road segments and balancing traffic among road segments located throughout the road network, in the event of complete network congestion.

Pore networks can be simulated in silico by using the dual site-bond Model. In this approach, a set of cavities (sites) are interconnected to each other by means of a set of throats (bonds), while considering that each site should be always larger than any of its delimiting bonds. The NoMISS greedy algorithm has been implemented recently in order to address this task; nevertheless, even if this procedure is relatively fast, there arises problems related to large memory consumption and long computing time, as pore networks become somewhat large. Here, three parallel methods are proposed to allow a proficient construction of large pore networks. The first method is a parallel Monte Carlo procedure, which applies a number of exchanges among pore sizes in order to obtain a valid pore network. The other two methods are parallel versions of the pioneering NoMISS greedy algorithm. The first version uses a static data partitioning to speed up the running time, whilst the second applies a dynamic data distribution policy to improve the pore network quality. The obtained results show the behavior of each proposed version with respect to their performance and quality, by employing the resources of a 125-core Linux cluster.

SUMMARY In recent years, three‐dimensional (3D) streaming over thin mobile devices has received very little attention from the research community. The 3D streaming‐based class of applications uses either a centralized or a distributed approach. whereas the former presents several drawbacks such as latency, server's bottleneck, and networks congestion; the latter, that is, peer‐to‐peer, has to deal with the supplying partner issue that consists in selecting the source that will be responsible for streaming the required 3D data. Given that frequent 3D streaming overuses the mobile device's resources and dissipates its energy, the energy factor should be taken into consideration during the selection of the supplier. In this paper, we propose two energy management control protocols for supplying partner selection in peer‐to‐peer 3D streaming that we refer to as one‐level energy‐based and two‐level energy‐based. We also propose a new source load estimator that takes into account two factors namely the source's residual energy and its number of served requests. In one‐level energy‐based, the requester uses the source's load information to prioritize the sources in an ascending order and distribute the requests starting with the least loaded sources. In two‐level energy‐based, both the requester and the supplier have important and complementary roles and participate in the energy management control. We then report on the performance evaluation of our energy management control protocols using an extensive set of simulation experiments with the NS2 tool. Copyright © 2011 John Wiley & Sons, Ltd.

Porous media simulation is an important contribution in the study of many physical phenomena. The No MISS greedy algorithm outstands from the existing sequential algorithms for constructing a pore sub network, in a relatively fast way. However, despite the No MISS time reduction, there are still problems related to the required processing time when very large networks need to be studied. In this work, a non scalable parallel version of the No MISS algorithm is presented, and a new approach is proposed to alleviate this issue, in both versions cluster cores work simultaneously on different porous sub network spaces. The first approach, named as Unbounded-No MISS, allows the cores to go forward with the initialization of the porous sub network space, applying a balancing policy when a core needs more data. At the end, the cores require a sequential synchronization to finish the porous network construction. The second approach, named as Bounded-No MISS, controls the porous sub network initialization by considering a site-size boundary, avoiding the final strong synchronization and improving considerably the scalability. The obtained results using a 125-core cluster are presented.

In order to detect and reduce the congestion level in downtown areas, many research works and projects have been proposed. The previous works have been based on the information gathered at a central database to recommend the best path for vehicles, which introduces a bottleneck problem as well as a single point-of-failure. In this work we propose a dynamic and real time protocol that intends to find the fastest path towards each destination in a distributed fashion, without the need for a centralized database. The proposed protocol recommends paths that will allow vehicles to avoid highly congested road segments towards a certain destination. Moreover, this protocol can alleviate the drastic increase in congestion scenarios by distributing the traffic load over the downtown grid road segments without adversely affecting the traveling time of vehicles. In this paper, we discuss our protocol in details, compare it to other path recommendation protocols and report on its performance evaluation an extensive set of scenarios and experiments implemented on the NS-2 simulator. From the results, we can conclude that our protocol provides better performance in terms of vehicular traveling time towards each destination, without the need for a central database.

Due to the important developments in the area of wireless networks and mobile computing, P2P networks-based 3D streaming over thin mobile devices is starting to gain the interest of many research works. Nevertheless, one of the main challenges that need to be resolved is the supplying partner selection issue that consists of selecting the source that holds the required 3D data. The selection process can be easy to apply, however several criteria need to be taken into consideration, in order to improve the system performance. In this paper, we propose a two-level energy-based supplying partner protocol for mobile P2P 3D streaming, which we refer to as 2L-EB. Our proposed protocol is applied at two levels: the requester's side and the supplier's side. We then report on the performance evaluation we have obtained to evaluate our 2L-EB protocol using an extensive set of simulation experiments with the NS2 tool.

Due to the rapid improvements of wireless technologies and mobile devices'capabilities, we are witnessing an important growth in applications using thin mobile devices. Augmented reality took advantage of the enhancement of thin mobile devices, which led to the emergence of Mobile Augmented Reality (MAR). Given that thin mobile devices can not have the entire virtual environment (VE) stored locally, multimedia streaming has been designed and is widely used nowadays in MAR. Most of the MAR systems apply the conventional client-server approach, that may suffer from delay and congestion, which affects the system performance. P2P-based multimedia streaming is considered, therefore, an alternative approach. Although mobile P2P based video streaming received a great deal of attention, mobile P2P based 3D streaming was not the choice of designed systems, mainly due to the limited mobile device's capabilities. Moreover, the main challenge that needs to be taken care of when designing mobile P2P based 3D streaming systems, is the selection of a supplying partner. A few studies have proposed supplying partner protocols for mobile P2P-based 3D streaming. However, they did not take into account the device's energy that is considered an important constraint when dealing with 3D streaming. In this paper, we propose an efficient energy-aware supplying partner protocol for mobile P2P networks based 3D streaming. A new load estimator is defined that takes into consideration two factors: the node's remaining energy and its number of served requests. We therefore propose the integration of an energy-aware criterion into the supplying partner selection in order to reduce the generation of overloaded nodes, and their energy dissipation. We then report on the performance evaluation we have obtained to evaluate our protocol using an extensive set of simulation experiments.

Load balancing algorithms are an essential component of parallel computing reducing the response time of applications. Frequently, balancing algorithms have a centralized behavior requiring a lot of messages to operate, thus causing scalability problems. A solution to improve scalability is to define a decentralized algorithm, avoiding the generation of bottlenecks. DLML (Data List Management Library) is a tool that, in a transparent way, allows the parallel processing of data that are organized through a List. One drawback of this tool is the global bidding algorithm used to distribute the data (work) generated during the execution. In this paper two load balancing algorithms for DLML handling partial information are proposed. The first algorithm considers a logical Torus topology and the second one follows a Binary Tree topology for communications. Results show how the scalability of DLML was improved, using two clusters of 40 and 1024 processing units, and executing dynamic and static applications.