Vehicular Adhoc Network provides ability to wirelessly communicate between vehicles. Network fragmentations and frequent topology changes (Mobility of the nodes) and limited coverage of Wi-Fi, are issues in VANET, that arise due to absence of central manager entity. Because of these reasons, routing the packets within the network is difficult task. Hence, provisioning an adept routing strategy is vital for the deployment of VANETs. The optimized link state routing is a well-known mobile adhoc network routing protocol. In this paper, we are proposing an optimization strategy to fine-tune few parameters by configuring the OLSR protocol using metaheuristic method. We considered some of the quality parameters such as packet delivery ratio, latency, throughput and fitness value for fine tuning OSLR protocol. Then we made Comparison of genetic algorithm, particle swarm optimization algorithm by using QoS parameters. We implemented our work on Red Hat Enterprise Linux 6 platform. And results are shown by simulations using VanetMobiSim and NS2 simulators; the fine-tuned OSLR protocol behaves better than the original routing protocol with intelligence and optimization configuration.Keywords-Optimized link state routing (OSLR); particle swarm optimization (PSO); Quality of service (QoS); Vehicular Adhoc Network (VANET); Mobile Adhoc Networks (MANETs)I.

In scientific fields, solving large and complex computational problems using central processing units (CPU) alone is not enough to meet the computation requirement. In this work we have considered a homogenous cluster in which each nodes consists of same capability of CPU and graphical processing unit (GPU). Normally CPU are used for control GPU and to transfer data from CPU to GPUs. Here we are considering CPU computation power with GPU to compute high performance computing (HPC) applications. The framework adopts pinned memory technique to overcome the overhead of data transfer between CPU and GPU. To enable the homogeneous platform we have considered hybrid [message passing interface (MPI), OpenMP (open multi-processing), Compute Unified Device Architecture (CUDA)] programming model strategy. The key challenge on the homogeneous platform is allocation of workload among CPU and GPU cores. To address this challenge we have proposed a novel analytical workload division strategy to predict an effective workload division between the CPU and GPU. We have observed that using our hybrid programming model and workload division strategy, an average performance improvement of 76.06% and 84.11% in Giga floating point operations per seconds(GFLOPs) on NVIDIA TESLA M2075 cluster and NVIDIA QUADRO K 2000 nodes of a cluster respectively for N-dynamic vector addition when compared with Simplice Donfack et.al [5] performance models. Also using pinned memory technique with hybrid programming model an average performance improvement of 33.83% and 39.00% on NVIDIA TESLA M2075 and NVIDIA QUADRO K 2000 respectively is observed for saxpy applications when compared with pagable memory technique.

High-Performance Computing is the cornerstone for many scientific and industrial innovations. The demand for high-performance computing power is one of the driving factors for the innovations of computer hardware. In the hybrid system, CPUs and GPUs are combined to produce better performance while executing HPC applications. The critical challenge to achieving better performance in a heterogeneous cluster is the efficient distribution of the workload among the CPUs/GPUs in the nodes. In this work, to address the distribution workload issue, an optimized analytical workload division model for the heterogeneous cluster is developed to efficiently distribute the workload among the nodes of a heterogeneous cluster. The analytical model considers workload, processing capabilities, and the number of CPUs and GPUs on the cluster to effectively distribute the workload. HPL and merge sort benchmark applications are used to test the proposed strategy. Workload division strategy is tested by conducting extensive experiments. To address the inter-node and intra-node communication challenge, pinned memory technique is used along with a single MPI process per node technique and CUDA IPC. The proposed workload division strategy is validated with the HPL application and Merge sort. Experiments show that the proposed workload division strategy performs much better than the existing works.