recognition is a pattern recognition technique and one of the most important biometrics; it is used in a broad spectrum of applications. The accuracy is not a major problem that specifies the performance of automatic face recognition system alone, the time factor is also considered a major factor in real time environments. Recent architecture of the computer system can be employed to solve the time problem, this architecture represented by multi-core CPUs and many- core GPUs that provide the possibility to perform various tasks by parallel processing. However, harnessing the current advancements in computer architecture is not without difficulties. Motivated by such challenge, this paper proposes a Real Time Face Recognition System (RTFRS). In doing so, this paper provides the architectural design, detailed design, and four variant implementations of the RTFRS. Finally, this paper determines the speed up obtained for the three advanced implementations (i.e., Hybrid Parallel model, CPU Parallel model, and Hybrid Mono model) against the convention implementation (i.e., CPU Mono model). The practical results demonstrate that the Hybrid Parallel model gain highest speed up around 82X, CPU Parallel model also have a high speed up around 71X, and finally, the Hybrid Mono model gives a slight speed up about 1.04X.

/pdf/design-and-implementation-of-real-time-face-recognition-46m5fxqq7j.pdf

Design and Implementation of Real Time Face Recognition System (RTFRS)

Adding the number of computing nodes is a common approach to achieving higher performance in a parallel computing system. However, with constraint of fixed system architecture and fixed algorithm structure, it is difficult to improve the performance of parallel computing only by extending its scale absolutely. To realize such extension with fixed structure, we analyze key factors from architecture and parallel task, which affect the scalability, and then use the weighted graph to model architecture as well as parallel task. Especially, focusing on the case that architecture graph and parallel task graph are homogeneous, we propose the extension method of graph similarity; for the case that architecture graph and parallel task graph are heterogeneous, a critical-path-unchanged scaling method is proposed. Actually, the above two extending methods do not change the graph's structure. They only adjust the node weight and edge-weight in the relevant graph. Furthermore, through mathematical derivation, ...

Extending Parallel Computing with Constraint of Fixed Structure by Adjusting Graph

Scalability of parallel algorithms and parallel machines is one of the most important performance targets that the parallel algorithms designers and the high performance machine designers seek for. The previous scaling models, such as the iso efficiency scaling model, fixed time scaling model, and the iso speed scaling model and so on, only consider of one side of a problem, namely, one side performance or the whole utility of one kind of resources. They did not balance two important sides of the problem, just like the efficiency and the execution time of the combinations. The computer researchers focus on the higher efficiency and higher utility of resources, so the previous scaling models can satisfy their needs. But the application scientists have more interested in the shorter execution time and the previous scaling models are not enough for them. In this paper, we propose a near optimal scaling model. It considers both of the efficiency and execution time, and users can choose the appropriate constant factor according to their need and get the right scaling curve. At last we use this scalability analysis method to analyze the scalability of the combinations of some parallel algorithms and a typical massively parallel processing machine. Results show that this model can describe the scaling ability of the combinations of the parallel algorithms and the parallel machines. Results also show that it can be attained that the execution time is near to the shortest time and the efficiency is not too low when the system size and the problem size scales according to the right near optimal constant factor. It can guide the optimal matching of the parallel applications and the parallel machines, and is beneficial to the design and improvement of the parallel algorithms.

Near-Optimal Scalability: A Pratical Scalability Metric

Due to the sustained and rapid growth of big data and the demand on higher accuracy solutions for application problems, the completion time of fixed-time big data tasks executing on original parallel computing systems becomes longer and longer. To meet the requirement of fixed completion time, the original parallel computing systems need to be scaled accordingly. Therefore, this paper studies an iso-time scaling method to guide the scaling of parallel computing systems. Firstly, the models of big data parallel tasks and parallel computing systems are built, and an algorithm is designed to calculate the completion time of big data parallel tasks. Secondly, according to the actual situation of the current majority computing centers, we put forward some reasonable hypotheses, make full use of backup computational nodes, and optimize the cost of scaling parallel computing systems. Then, a vertical scaling algorithm is designed to upgrade computational nodes, and a horizontal scaling algorithm is designed to add computational nodes. Furthermore, this paper compares the two scaling algorithms in the aspects of time complexity, degree of parallelism and system utilization for scaled parallel computing system. Finally, some simulation experiments are conducted. The experimental results show that our method can keep the completion time within fixed time when the increasing data parallel tasks execute on the scaled parallel computing systems and it has better effect in scaling cost than traditional methods.

An iso-time scaling method for big data tasks executing on parallel computing systems

We present a mathematically rigorous iso-Quality-of-Service (QoS) metric which relates the achievable quality of service (QoS) for a real-time analytics service with workload specific and use case specific performance and output quality requirements to the energy cost of offering the service by different server architectures. Using a new iso-QoS evaluation methodology, we scale server resources to meet QoS targets and directly rank the servers in terms of their energy-efficiency and by extension cost of ownership. Our metric and method are platform-independent and enable fair comparison of datacenter compute servers with significant architectural diversity, including micro-servers. We deploy our metric and methodology to compare three servers running financial option pricing workloads on real-life market data. We find that server ranking is sensitive to data inputs and desired QoS level and that although scale-out micro-servers can be up to two times more energy-efficient than conventional heavyweight ser...

/pdf/iso-quality-of-service-fairly-ranking-servers-for-real-time-41sd68fidk.pdf

Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics

Scalability is an important performance metric of parallel computing, but the traditional scalability metrics only try to reflect the scalability for parallel computing from one side, which makes it difficult to fully measure its overall performance. This paper studies scalability metrics intensively and completely. From lots of performance parameters of parallel computing, a group of key ones is chosen and normalized. Further the area of Kiviat graph is used to characterize the overall performance of parallel computing. Thereby a novel scalability metric about iso-area of performance for parallel computing is proposed and the relationship between the new metric and the traditional ones is analyzed. Finally the novel metric is applied to address the scalability of the matrix multiplication Cannon's algorithm under LogP model. The proposed metric is significant to improve parallel computing architecture and to tune parallel algorithm design.

A novel scalability metric about iso-area of performance for parallel computing

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A novel scalability metric about iso-area of performance for parallel computing