Showing papers in "Cluster Computing in 2012"

DataSpaces: an interaction and coordination framework for coupled simulation workflows

Abstract: Emerging high-performance distributed computing environments are enabling new end-to-end formulations in science and engineering that involve multiple interacting processes and data-intensive application workflows. For example, current fusion simulation efforts are exploring coupled models and codes that simultaneously simulate separate application processes, such as the core and the edge turbulence. These components run on different high performance computing resources, need to interact at runtime with each other and with services for data monitoring, data analysis and visualization, and data archiving. As a result, they require efficient and scalable support for dynamic and flexible couplings and interactions, which remains a challenge. This paper presents DataSpaces a flexible interaction and coordination substrate that addresses this challenge. DataSpaces essentially implements a semantically specialized virtual shared space abstraction that can be associatively accessed by all components and services in the application workflow. It enables live data to be extracted from running simulation components, indexes this data online, and then allows it to be monitored, queried and accessed by other components and services via the space using semantically meaningful operators. The underlying data transport is asynchronous, low-overhead and largely memory-to-memory. The design, implementation, and experimental evaluation of DataSpaces using a coupled fusion simulation workflow is presented.

A new approach to the job scheduling problem in computational grids

Abstract: Job scheduling is one of the most challenging issues in Grid resource management that strongly affects the performance of the whole Grid environment. The major drawback of the existing Grid scheduling algorithms is that they are unable to adapt with the dynamicity of the resources and the network conditions. Furthermore, the network model that is used for resource information aggregation in most scheduling methods is centralized or semi-centralized. Therefore, these methods do not scale well as Grid size grows and do not perform well as the environmental conditions change with time. This paper proposes a learning automata-based job scheduling algorithm for Grids. In this method, the workload that is placed on each Grid node is proportional to its computational capacity and varies with time according to the Grid constraints. The performance of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with those of several existing methods. Numerical results confirm the superiority of the proposed algorithm over the others in terms of makespan, flowtime, and load balancing.

Data Partitioning on Heterogeneous Multicore and Multi-GPU Systems Using Functional Performance Mode

Abstract: Transition to hybrid CPU/GPU platforms in high performance computing is challenging in the aspect of efficient utilisation of the heterogeneous hardware and existing optimised software. During recent years, scientific software has been ported to multicore and GPU architectures and now should be reused on hybrid platforms. In this paper, we model the performance of such scientific applications in order to execute them efficiently on hybrid platforms. We consider a hybrid platform as a heterogeneous distributed-memory system and apply the approach of functional performance models, which was originally designed for uniprocessor machines. The functional performance model (FPM) represents the processor speed by a function of problem size and integrates many important features characterising the performance of the architecture and the application. We demonstrate that FPMs facilitate performance evaluation of scientific applications on hybrid platforms. FPM-based data partitioning algorithms have been proved to be accurate for load balancing on heterogeneous networks of uniprocessor computers. We apply FPM-based data partitioning to balance the load between cores and GPUs in the hybrid architecture. In our experiments with parallel matrix multiplication, we couple the existing software optimised for multicores and GPUs and achieve high performance of the whole hybrid system.

STACRP: a secure trusted auction oriented clustering based routing protocol for MANET

Abstract: In mobile ad hoc network (MANET) nodes have a tendency to drop others' packet to conserve its own energy. If most of the nodes in a network start to behave in this way, either a portion of the network would be isolated or total network functionality would be hampered. This behavior is known as selfishness. Therefore, selfishness mitigation and enforcing cooperation between nodes is very important to increase the availability of nodes and overall throughput and to achieve the robustness of the network. Both credit and reputation based mechanisms are used to attract nodes to forward others' packets. In light of this, we propose a game theoretic routing model, Secure Trusted Auction oriented Clustering based Routing Protocol (STACRP), to provide trusted framework for MANET. Two auction mechanisms procurement and Dutch are used to determine the forwarding cost-per-hop for intermediate nodes. Our model is lightweight in terms of computational and communication requirements, yet powerful in terms of flexibility in managing trust between nodes of heterogeneous deployments. It manages trust locally with minimal overhead in terms of extra messages. STACRP organizes the network into 1-hop disjoint clusters and elects the most qualified and trustworthy nodes as Clusterhead. The trust is quantified with carefully chosen parameters having deep impact on network functionality. The trust model is analyzed using Markov chain and is proven as continuous time Markov chain. The security analysis of the model is analyzed to guarantee that the proposed approach achieves a secure reliable routing solution for MANETs. The proposed model have been evaluated with a set of simulations that show STACRP detects selfish nodes and enforces cooperation between nodes and achieves better throughput and packet delivery ratio with lees routing overhead compare to AODV.

Optimizing dataflow applications on heterogeneous environments

Abstract: The increases in multi-core processor parallelism and in the flexibility of many-core accelerator processors, such as GPUs, have turned traditional SMP systems into hierarchical, heterogeneous computing environments. Fully exploiting these improvements in parallel system design remains an open problem. Moreover, most of the current tools for the development of parallel applications for hierarchical systems concentrate on the use of only a single processor type (e.g., accelerators) and do not coordinate several heterogeneous processors. Here, we show that making use of all of the heterogeneous computing resources can significantly improve application performance. Our approach, which consists of optimizing applications at run-time by efficiently coordinating application task execution on all available processing units is evaluated in the context of replicated dataflow applications. The proposed techniques were developed and implemented in an integrated run-time system targeting both intra- and inter-node parallelism. The experimental results with a real-world complex biomedical application show that our approach nearly doubles the performance of the GPU-only implementation on a distributed heterogeneous accelerator cluster.

Performance analysis of virtual clusters in personal communication networks

Abstract: The objective of the paper is to analyze the performance of virtual cluster architectures in wireless networks. The key issues in wireless domain are flooding, connectivity, and power management. These issues arise during the path finding and maintenance between source and destination nodes. To overcome these issues three approaches are introduced in this paper namely; fusion virtual structure, link quality connected dominating set and cluster backbone approach. These approaches follow the distributed localized computations for virtual cluster constructions and focus on fundamental connectivity problems and are partially involved in power saving process of individual nodes. The proposed methods are analyzed in terms of backbone size, packet delivery ratio and normalized routing overhead and the results are witnessed by simulation.

A heterogeneous computing environment to solve the 768-bit RSA challenge

Abstract: In December 2009 the 768-bit, 232-digit number RSA-768 was factored using the number field sieve. Overall, the computational challenge would take more than 1700 years on a single, standard core. In the article we present the heterogeneous computing approach, involving different compute clusters and Grid computing environments, used to solve this problem.

Traffic-aware routing protocol for wireless sensor networks

Abstract: In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.

Reliable MapReduce computing on opportunistic resources

Abstract: MapReduce offers an ease-of-use programming paradigm for processing large data sets, making it an attractive model for opportunistic compute resources. However, unlike dedicated resources, where MapReduce has mostly been deployed, opportunistic resources have significantly higher rates of node volatility. As a consequence, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate on such volatile resources. In this paper, we propose MOON, short for MapReduce On Opportunistic eNvironments, which is designed to offer reliable MapReduce service for opportunistic computing. MOON adopts a hybrid resource architecture by supplementing opportunistic compute resources with a small set of dedicated resources, and it extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms to take advantage of the hybrid resource architecture. Our results on an emulated opportunistic computing system running atop a 60-node cluster demonstrate that MOON can deliver significant performance improvements to Hadoop on volatile compute resources and even finish jobs that are not able to complete in Hadoop.

Design and analysis of a distributed grid resource discovery protocol

Abstract: Computational grids have been emerging as a new paradigm for solving large complex problems over the recent years. The problem space and data set are divided into smaller pieces that are processed in parallel over the grid network and reassembled upon completion. Typically, resources are logged into a resource broker that is somewhat aware of all of the participants available on the grid. The resource broker scheme can be a bottleneck because of the amount of computational power and network bandwidth needed to maintain a fresh view of the grid. In this paper, we propose to place the load of managing the network resource discovery on to the network itself: inside of the routers. In the proposed protocol, the routers contain tables for resources similar to routing tables. These resource tables map IP addresses to the available computing resource values, which are provided through a scoring mechanism. Each resource provider is scored based on the attributes they provide such as the number of processors, processor frequency, amount of memory, hard drive space, and the network bandwidth. The resources are discovered on the grid by the protocol's discovery packets, which are encapsulated within the TCP/IP packets. The discovery packet visits the routers and look up in the resource tables until a satisfactory resource is found. The protocol is validated by simulations with five different deployment environments.

Monotonicity and performance evaluation: applications to high speed and mobile networks

Abstract: We illustrate through examples how monotonicity may help for performance evaluation of networks. We consider two different applications of stochastic monotonicity in performance evaluation. In the first one, we assume that a Markov chain of the model depends on a parameter that can be estimated only up to a certain level and we have only an interval that contains the exact value of the parameter. Instead of taking an approximated value for the unknown parameter, we show how we can use the monotonicity properties of the Markov chain to take into account the error bound from the measurements. In the second application, we consider a well known approximation method: the decomposition into Markovian submodels. In such an approach, models of complex networks or other systems are decomposed into Markovian submodels whose results are then used as parameters for the next submodel in an iterative computation. One obtains a fixed point system which is solved numerically. In general, we have neither an existence proof of the solution of the fixed point system nor a convergence proof of the iterative algorithm. Here we show how stochastic monotonicity can be used to answer these questions and provide, to some extent, the theoretical foundations for this approach. Furthermore, monotonicity properties can also help to derive more efficient algorithms to solve fixed point systems.

The impact of heterogeneous multi-core clusters on graph partitioning: an empirical study

Abstract: The advent of multi-core architectures provides an opportunity for accelerating parallelism in mesh-based applications. This multi-core environment, however, imposes challenges not addressed by conventional graph-partitioning techniques that are originally designed for distributed-memory uniprocessors. As the first step to exploit the multi-core platform, this paper presents experimental evaluation to understand partitioning performance on small-scaled heterogeneous multi-core clusters. With results and analyses gathered, we propose a hierarchical framework for resource-aware graph partitioning on heterogeneous multi-core clusters. Preliminary evaluation demonstrates the potential of the framework and motivates directions for incorporating application requirements into graph partitioning.

Performance, reliability, and performability of a hybrid RAID array and a comparison with traditional RAID1 arrays

Abstract: We describe a hybrid mirrored disk organization patented by LSI Logic Corp. and compare its performance, reliability, and performability with traditional mirrored RAID1 disk organizations and RAID(4+?), ??1. LSI RAID has the same level of redundancy as mirrored disks, but also utilizes parity coding. Unlike RAID1, which cannot tolerate all two disk failures, LSI RAID similarly to RAID6 is 2 Disk Failure Tolerant (2DFT), but in addition it can tolerate almost all three disk failures, while RAID1 organizations are generally 1DFT. We list analytic expressions for the reliability of various RAID1 organizations and use enumeration when the reliability expression cannot be obtained analytically. An asymptotic expansion method based on disk unreliabilities is used for an easy comparison of RAID reliabilities. LSI RAID performance is evaluated with the Read-Modify-Write (RMW) and ReConstruct Write (RCW) methods to update parities. The combination of the two methods is used to balance data and parity disk loads, which results in maximizing the I/O throughput. The analysis shows that LSI RAID has an inferior performance with respect to basic mirroring in processing an OLTP workload, but it outperforms RAID6. LSI RAID in spite of its higher Mean Time to Data Loss (MTTDL) is outperformed by other RAID1 organizations as far as its performability is concerned, i.e., the number of I/Os carried out by the disk array operating at maximum I/Os Per Second (IOPS) until data loss occurs. A survey of RAID1 organizations and distributed replicated systems is also included.

SEParAT: scheduling support environment for parallel application task graphs

Abstract: Programs using parallel tasks can be represented by task graphs so that scheduling algorithms can be used to find an efficient execution order of the parallel tasks. This article proposes a flexible, component-based and extensible scheduling framework called SEParAT that supports the scheduling of a parallel program in multiple ways. The article describes the functionality and the software architecture of SEParAT. The flexible interfaces enable the cooperation with other programming tools, e.g., tools exploiting a specification of the parallel task structure of an application. The core component of SEParAT is an extensible scheduling algorithm library that provides an infrastructure to determine efficient schedules for task graphs. Homogeneous as well as heterogeneous platforms can be handled. The article also includes detailed experimental results comprising the evaluation of SEParAT as well as the evaluation of a variety of scheduling algorithms.

A distributed clustering algorithm for large-scale dynamic networks

Abstract: We propose an algorithm that builds and maintains clusters over a network subject to mobility. This algorithm is fully decentralized and makes all the different clusters grow concurrently. The algorithm uses circulating tokens that collect data and move according to a random walk traversal scheme. Their task consists in (i) creating a cluster with the nodes it discovers and (ii) managing the cluster expansion; all decisions affecting the cluster are taken only by a node that owns the token. The size of each cluster is maintained higher than m nodes (m is a parameter of the algorithm). The obtained clustering is locally optimal in the sense that, with only a local view of each clusters, it computes the largest possible number of clusters (i.e. the sizes of the clusters are as close to m as possible). This algorithm is designed as a decentralized control algorithm for large scale networks and is mobility-adaptive: after a series of topological changes, the algorithm converges to a clustering. This recomputation only affects nodes in clusters where topological changes happened, and in adjacent clusters.

A new degree of freedom for memory allocation in clusters

Abstract: Improvements in parallel computing hardware usually involve increments in the number of available resources for a given application such as the number of computing cores and the amount of memory. In the case of shared-memory computers, the increase in computing resources and available memory is usually constrained by the coherency protocol, whose overhead rises with system size, limiting the scalability of the final system. In this paper we propose an efficient and cost-effective way to increase the memory available for a given application by leveraging free memory in other computers in the cluster. Our proposal is based on the observation that many applications benefit from having more memory resources but do not require more computing cores, thus reducing the requirements for cache coherency and allowing a simpler implementation and better scalability. Simulation results show that, when additional mechanisms intended to hide remote memory latency are used, execution time of applications that use our proposal is similar to the time required to execute them in a computer populated with enough local memory, thus validating the feasibility of our proposal. We are currently building a prototype that implements our ideas. The first results from real executions in this prototype demonstrate not only that our proposal works but also that it can efficiently execute applications that make use of remote memory resources.

Stochastic bounds for composite Web services response times

Abstract: In this paper, we propose bounding models, which provide upper and lower bounds on response time in composite Web service model, for alleviating the state explosion problem. The considered models have heterogeneous servers and the number of elementary Web services can be very large. More precisely, we study two types of composite Web services. First, we investigate the performance of a single composite Web service execution instance. Second, this assumption is relaxed (i.e. multiple composite Web services execution instances are considered). These models allows to find trade-off between the accuracy of the bounds and the computation complexity.

Flexible authorization in home network environments

Abstract: In home network environments, OSGi platform plays a major role as the service gateway to access into home appliances. It is important to provide appropriate services as well as security mechanisms to protect confidential or sensitive information and devices. Authorization is especially important when controlling the access of different users. OSGi platform supports the role based access control but it does not support various facilities in the RBAC model. To address such shortcomings, several works have proposed the enhanced access control mechanisms for the OSGi service platform. However, these are still limited to applying the traditional RBAC conventions to OSGi platform. This paper extends the existing authorization mechanism of OSGi platform to address its limitations for dynamic deployments. By adding the relative role concept and activating the access control using the delegation model, proposed mechanism enables a diverse and outstanding access control. We implement the proposed mechanism using aspectJ and illustrate how to develop a bundle including access control logic.

Traffic management based on token bucket mechanism for WiMAX networks

Abstract: In large scale networks like the IEEE 802.16 (WiMAX), it is very important not only to monitor, but also to control the amount of traffic injected into the network. This process helps in decreasing congestion and by consequence, in guaranteeing the Quality of Service (QoS) requirements for each class of traffic. In this study we propose a traffic policer based on token bucket concept for WiMAX networks. Token bucket parameters (token rate and bucket size) are adjusted according to the traffic characteristics for each traffic class individually. Simulation results show that the proposed traffic policing technique greatly enhances the network performance. It decreases the average delay for real time traffic like rtPS, and therefore, reduces data drop probability due to missed deadlines. It also decreases data loss probability for non real time service class like nrtPS.

Modular implementation of dynamic algorithm switching in parallel simulations

Abstract: We present a modular approach to implementing dynamic algorithm switching for parallel scientific software. By using a compositional framework based on function call interception techniques, our proposed method transparently integrates algorithm switching code with a given program without directly modifying the original code structure. Through fine-grained control of algorithmic behavior of an application at the level of functions, our approach supports design and implementation of application-specific switching scenarios in a modular way. Our approach encourages algorithm switching to dynamically perform at the loop end of a parallel simulation, where cooperating processes in concurrent execution typically synchronize and intermediate computation results are consistent. In this way, newly added switching operations do not cause race conditions that may produce unreliable computation results in parallel simulations. By applying our method to a real-world scientific application and adapting its algorithmic behavior to the properties of input problems, we demonstrate the applicability and effectiveness of our approach to constructing efficient parallel simulations.

An architecture for P2P bag-of-tasks execution with multiple task allocation policies in desktop grids

Abstract: In this paper, we propose and evaluate a flexible architecture for desktop grids that supports multiple task allocation policies on top of a structured P2P overlay. In our proposal, a Bag-of-Tasks application is submitted to random nodes and placed in their local queue, that is processed in a FIFO way. When a node becomes idle, a task allocation policy is executed that fetches tasks from remote nodes. The proposed architecture is flexible since it is decoupled from both the P2P middleware and the P2P overlay. A prototype of the proposed architecture was implemented on top of the JXTA middleware, using the Chord P2P search overlay. The results obtained in a 16-machine heterogeneous desktop grid show that very good performance gains are obtained with multiple task allocation policies. Also, a speedup of 9.85 was achieved for an application composed of 270 network flow balancing tasks, reducing its wallclock execution time from 32.51 min to 3.3 min.

Explicit coordination to prevent congestion in data center networks

Abstract: Large cluster-based cloud computing platforms increasingly use commodity Ethernet technologies, such as Gigabit Ethernet, 10GigE, and Fibre Channel over Ethernet (FCoE), for intra-cluster communication. Traffic congestion can become a performance concern in the Ethernet due to consolidation of data, storage, and control traffic over a common layer-2 fabric, as well as consolidation of multiple virtual machines (VMs) over less physical hardware. Even as networking vendors race to develop switch-level hardware support for congestion management, we make the case that virtualization has opened up a complementary set of opportunities to reduce or even eliminate network congestion in cloud computing clusters. We present the design, implementation, and evaluation of a system called XCo, that performs explicit coordination of network transmissions over a shared Ethernet fabric to proactively prevent network congestion. XCo is a software-only distributed solution executing only in the end-nodes. A central controller uses explicit permissions to temporally separate (at millisecond granularity) the transmissions from competing senders through congested links. XCo is fully transparent to applications, presently deployable, and independent of any switch-level hardware support. We present a detailed evaluation of our XCo prototype across a number of network congestion scenarios, and demonstrate that XCo significantly improves network performance during periods of congestion. We also evaluate the behavior of XCo for large topologies using NS3 simulations.

Introduction to special issue: ICISA 2010

Abstract: Providing high-performance computing and security is a challenging task. High-performance computing systems and distributed environments currently suffer from security support and high-performance. This special issue addresses relationships between security and high performance systems in many aspects. First, it considers how to add security properties (authentication, authorization, accounting, confidentiality, integrity, non-repudiation, access control) to high-

A smart spaces architecture based on heterogeneous contexts, particularly social contexts

Abstract: Advances in smart technologies, wireless networking, and the increased interest in services have led to the emergence of ubiquitous and pervasive computing as one of the most promising areas of computing in recent years. Researchers have become specifically interested in smart spaces and the significant improvements it can introduce to our lives. Most smart spaces rely on physical components such as sensors to sense and acquire information about the real world environment and surroundings. Although sensor networks can provide useful contextual information, they are known for their high degree of unreliability and limited resources. We believe that it is necessary to augment physical sensors with other kinds of data to create more reliable and truly context-aware smart spaces. In this paper we therefore utilize mobile devices and social networks to acquire more detailed and useful contextual information that can help create smarter spaces. We then propose a smart spaces architecture that utilizes these new contexts and in particular the social context.

Improving web cache server performance through arbitral thread and delayed caching

Abstract: As the number of Internet users increase explosively, the delay in network response time is also increasing. An economic and efficient solution for this problem is web caching. But the use of a cache server can cause another bottleneck because of the concentration of requests at the cache server. Many studies on improving cache server performance have been suggested, but existing studies have focused on load balancing and/or caching capacity, not directly on improving the throughput of a single cache server. In this paper, we analyze the causes of cache server bottleneck, and propose an arbitral thread and the delayed caching mechanism as a solution. We use an arbitral thread in order to provide a quick service to users' service requests, and we use delayed caching in order to improve system reliability. The proposed cache server is implemented through a modification of the SQUID cache server, and we compare its performance with the original SQUID cache server.

Design of hierarchical coordination model for ubiquitous application

Abstract: Our research targets collaborative environments with focus on mobility and teams. This environment comprise a number of people working on multiple tasks and activities simultaneously. As mobile and wireless technology advances people are no longer bound to their offices. Team members are able to collaborate while on the move. So, mobile ubiquitous application can be a best solution to provide a mobility and collaboration for the environment. And the issue must be considered how to coordinate these ubiquitous application seamlessly and efficiently. We present a methodology for context modeling and employ a framework that reduces costs such as computing information and usage of network resources by transferring context at relevant levels of detail. At the same time, robustness of the system is improved by dealing with uncertain context information. Our framework is implemented on an OSGi container platform using Web services for communication means.

A decentralized clustering scheme for transparent mode devices

Abstract: Clustering multiple devices to form a single powerful device is a common method for improving performance. Most designs of the clustering schemes in the current literature are deploying a traffic splitter in front of devices in the cluster which acts as a centralized job dispatcher splitting workloads to backend devices. In this paper, we propose a decentralized clustering scheme, with no traffic splitter deployed, as an alternative solution on building a cluster system for those devices configured in transparent mode, such as bandwidth controllers, NIPSs, and traffic monitors. Devices in the cluster process the network traffic in parallel in a decentralized manner to scale the throughput. A device can also migrate its workload to others for the purpose of load balance or fault tolerance. Experiment results suggest that the proposed scheme can effectively improve performance of transparent mode devices in terms of throughput, load balance, and fault tolerance.

Special Issue: Performance evaluation of communications in distributed systems and Web based service architectures

Abstract: This special issue is devoted to performance evaluation of communications in distributed systems and Web based service architectures. Due to the rapid evolution of IP networks with different network access (Optical, Ethernet, 3G, LTE, WIMAX, WIFI,. . . ), computer Systems (PC, smart phones, Ipads) and Web based services architectures, performance evaluation becomes essential but still a complex issue in general. The complexity is due to the heterogeneity of those systems and the diversity of considered applications. The goal of the performance evaluation is analyzing and dimensioning those architectures in order to provide the Quality of Service (QoS) for the considered services and to maximize the bandwidth utilization. Indeed, several challenges remain to be resolved before these systems become a commodity. Ensure QoS for webbased systems as well as distributed and mobile systems and evaluating their communication performance is a real challenge to design them. Quantitative analysis can be very difficult and may be intractable because of the state space explosion. This is why recently new methodologies or/and tools emerged for these kinds of complex systems, such as Stochastic Automata Networks, Stochastic bounds, PEPA, just to mention a few. This special issue selected the best papers of the IEEE workshop PEDISWESEA that was held in Riccione, Italy in 2010 and co-located with IEEE ISCC. These papers cover recent studies on the performance evaluation of communications in distributed systems and Web based service archi-

Finding Scalable Configurations for AEDB Broadcasting Protocol using Multi-objective Evolutionary

Abstract: Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.

Two-level parallelization of Ehrenfest force calculations in ab initio molecular dynamics simulation

Abstract: A two-level parallel code for Ehrenfest force calculations in ab initio molecular dynamics simulations was developed for a shared memory multiprocessor cluster. Coarse-grain parallelism was implemented by atomic decomposition and a fine-grained parallelism was exploited to perform matrix multiplications. This two-level parallelism efficiently enhances the speed of computations.