Implementing Breadth-First Search on a Compact Supercomputer Suiren

This paper presents a general framework for determining average program execution times and their variance, based on the program's interval structure and control dependence graph. Average execution times and variance values are computed using frequency information from an optimized counter-based execution profile of the program.

Evaluation of Level of Confidence and Optimization of Roll-back Recovery with Checkpointing for Real-Time Systems

Supercomputing systems have made great strides in recent years as the extensive computing needs of cutting-edge engineering work and scientific discovery have driven the development of more powerful systems. The first teraflop computer, ASCI Red, came on the scene in late 1996, and now a machine must achieve 5.9 teraflops to gain entry to the very bottom of the Top500 supercomputing list.

2016 4th International Workshop on Energy Efficient Supercomputing (E2SC)

Resource provisioning in scalable cloud using bio-inspired artificial neural network model

IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

INTRODUCTION Simulation training has become a widely accepted educational tool for the development of technical skills in endoscopic surgery. The objective of this work was to evaluate bachelors who hold a Degree in Medicine in the execution of exercises carried out in both physical and virtual simulations. MATERIAL AND METHODS The descriptive study was carried out among 400 students (266 women and 134 men) in their third year of medicine studies between 2016 and 2019. The practises consisted of 11 hours of teaching, made of theory introduction, and exercise work in groups of 15-20 students on 4 workstations: LapSim, Pelvitrainer, Transanal Endoscopic Surgery (singleport) and Flexible Endoscopy. The execution time per individual exercise and in pairs is collected. RESULTS Individual exercises: the average execution time was: 277.0±117.8 sg, LapSim: 158.8± 66.44 sg and Pelvitrainer: 123.6±82.7sg. Couple exercises: the average execution time in Transanal Endoscopic Surgery was: 79.4±51.9 sg and Flexible Endoscopy: 53.56±34.4 sg. The analysis by gender showed that men performed better in LapSim with an average of 141.4 sg ± 52.2 in respect to women, 167.8 sg ± 71.1 (p < 0.05). In the rest of the exercises, no significant differences were shown. CONCLUSIONS Conducting simulated practises or studies for further development in the Degree is feasible, with objective evaluations of the skills acquired. The possibility of interacting in a controlled environment and with immediate feedback has made it possible to identify baseline differences by gender only in virtual simulation.

The ability to accurately estimate the execution time of computationally expensive e-science algorithms enables better scheduling of workflows that incorporate those algorithms as their building blocks, and may give users an insight into the expected cost of workflow execution on cloud resources. When a large history of past runs can be observed, crude estimates such as the average execution time can easily be provided. We make the hypothesis that, for some algorithms, better estimates can be obtained by using the histories to learn regression models that predict execution time based on selected features of their inputs. We refer to this property as input predictability of algorithms. We are motivated by e-science workflows that involve repetitive training of multiple learning models. Thus, we verify our hypothesis on the specific case of the C4.5 decision tree builder, a well-known learning method whose training execution time is indeed sensitive to the specific input dataset, but in non- obvious ways. We use the case study to demonstrate a method for assessing input predictability. While this yields promising results, we also find that its more general applicability involves a trade off between the black-box nature of the algorithms under analysis, and the need for expert insight into relevant features of their inputs.

In order to have a spacecraft orbital motion model, the developer usually tends to use traditional spacecraft orbital motion models. In order to have a good model, the developer resorts to modelling techniques that are more complex. Increasing model complexity increases algorithm execution time and prevents its usage on-board a spacecraft. This is due to real time constraints and limited computational resources usually imposed by the spacecraft on-board computer. This article develops a new method based on neural networks. The developed method allows the developer of spacecraft orbital motion model to alleviate the problem of increasing execution time with increasing model complexity. The positioning error percent of the proposed algorithm should not be more than 5%. The new method effectiveness evaluation is confirmed by the calculations using three test cases. The first test case has been the exact solution of the two-body problem. The second test case has been the Gamma Ray Observatory (GRO) spacecraft verified simulator. The third test case is a spacecraft simulated on the General Mission Analysis Tool (GMAT), which is developed by NASA and private industries. The maximum obtained error percent of the new method in all the test cases is 0.6%, which is considered a very good performance compared to the predefined 5% bound. The average execution time of traditional spacecraft motion models increases approximately 700% as the disturbance model complexity increases. On the contrary, the developed neural networks algorithm has approximately a constant average execution time regardless of the complexity of the disturbance models utilized. This enables using of the newly developed neural networks algorithm with very complex disturbance models without any increase of the computational load.

This paper presents a new approach to scheduling jobs on a service Grid using a genetic algorithm (GA). A fitness function is defined to minimize the average execution time of scheduling N jobs to M(≤ N) machines on the Grid. Two models are proposed to predict the execution time of a single job or multiple jobs on each machine with varied system load. The single service type model is used to schedule jobs of one single service to a machine while the multiple service types model schedules jobs of multiple services to a machine. The predicted execution times from these models are used as input to the genetic algorithm to schedule N jobs to M machines on the Grid. Experiments on a small Grid of four machines have shown a significant reduction of the average execution time by the new job scheduling approach.

Cloud computing is a platform serving millions of users simultaneously. This platform employs various task scheduling algorithms that play a significant role in determining the cloud computing performance (waiting time, response time, execution time, and total finish time for all tasks). Tasks are usually different in their nature, importance, length, and requirements. Therefore, we will be using Analytic Hierarchy Process (AHP) scheduling algorithm for making decisions and prioritizing tasks executions. We are proposing the use of Task Classification (TC) and VMs Categorization (VC) model with AHP to reduce the number of comparisons in the pairwise comparison matrix and improve consistency ratio (CR) and scheduling tasks based on their importance. The proposed algorithm is compared to different scheduling algorithms presented in the literature in terms of average waiting time and average execution time. Simulation results show that the proposed algorithm gives the best compromise between the considered metrics.

Banana detection based on color and texture features in the natural environment

Electric power consumed by servers has to be reduced in order to realize green societies. We consider computation (CP) and storage (ST) types of application processes performed on servers in this paper, where CPU and storage drives are mainly used, respectively. In the storage- and computation-based power consumption model proposed by the authors, the power consumption rate of a server depends on what types of processes are performed but is independent of how may processes are performed on the server. In the storage- and computation-based processing model, the execution time of an ST process depends on the number of concurrent CP and ST processes but the execution time of a CP process depends on only CP processes and is independent of ST processes. In our previous studies, the energy-aware algorithm is discussed to select a server in a cluster of servers for each request so that the total power consumption of the servers can be reduced. However, a server consumes electric power even if the server is idle, i.e. no process is performed. In this paper, we discuss a dynamic energy-aware (DEA) cluster which includes only active servers where at least one process is performed. A server for each request is selected in a dynamic cluster so that the total power consumption of servers in the cluster can be reduced. We evaluate the DEA algorithm in terms of the total power consumption and average execution time and show the total power consumption can be reduced.

Scalable parallel computers are often nonuniform communication architectures (NUCAs), where the access time to other processor’s caches vary with their physical location. Still, few attempts of exploring cache-to-cache communication locality have been made. This paper introduces a new kind of synchronization primitives (lock-unlock) that favor neighboring processors when a lock is released. This improves the lock handover time as well as access time to the shared data of the critical region. A critical section guarded by our new RH lock takes less than half the time to execute compared with the same critical section guarded by any other lock on our NUCA hardware. The execution time for Raytrace with 28 processors was improved 2.23 - 4.68 times, while global traffic was dramatically decreased compared with all the other locks. The average execution time was improved 7 - 24% while the global traffic was decreased 8 - 28% for an average over the seven applications studied.

Summary form only given. We present a simulation of an acyclic n/spl times/n DR-Mesh on an n/spl times/n LR-Mesh. The simulation is efficient in regards to size since both models use the same number of processors. The worst execution time for this simulation is O(n/sup 2/) time, but we demonstrate that its average execution time is O(log n). The existing fastest simulation takes O(log n) time, but it uses an extremely large number of processors. On the other hand, the most efficient simulation in terms of size takes O(log/sup 2/ n) time with O(n/sup 4//log/sup 2/ n) processors. Both of the existing simulations are for the unrestricted DR-Mesh. This paper provides an important step to efficiently simulate the unrestricted DR-Mesh on weaker models such as the R-Mesh and the LR-Mesh.