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This paper analyzes the parallelization efficiency of Menge [1], an open source virtual crowd simulation system widely used for algorithm benchmarking, with focuses on three aspects: performance of the existing parallel processing scheme, bottleneck of parallel processing, and improvement opportunities for parallel efficiency of the system. First, we calculate the speedup ratio of each Menge module...
In this paper, we present, evaluate and analyse the performance of parallel synchronous Jacobi algorithms by different partitioned procedures including band-row splitting, band-row sparsity pattern splitting and substructuring splitting, when solving sparse large linear systems. Numerical experiments performed on a set of academic 3D Laplace equation and on a real gravity matrices arising from the...
Distributed parallel computing platform performs well in processing big data. However, due to the platform's complexity and distributed characteristics, it is hard to design and achieve. For example, during the platform's design phase, variations are unpredictable. To address these issues, a highly structured object-oriented framework for systematic modeling, which has high flexibility, reusability...
This paper presents a novel parallelization approach to speedup EMT simulation, using GPU-based computing. This paper extends earlier published works in the area, by exploiting additional parallelism to accelerate EMT simulation. A 2D-parallel matrix-vector multiplication is used that is faster than previous 1D-methods. Also this paper implements a simpler GPU-specific sparsity technique to further...
In recent years parallel computing has been widely employed for both science research and commercial applications. For parallel systems such as many-core or computer clusters, it is inevitable to have one or more computing node failures due to random errors or injected attacks. Usually a diagnosis mechanism is able to locate several defective nodes through a number of tests and the analysis of those...
Computing model based on the architecture of Google Map-Reduce model is widely applied. In this paper, a set of NETRMR prototype system used for distributed computing is designed with the .NET Remoting technology under the .NET platform. The system encapsulates fault-tolerant and load scheduling, to simplify the complexity of parallel programming under distributed computing environment. Take the parallel...
Instead of hybrid recommendation, recommendation system research mainly focused on improving the traditional algorithm. So, this paper proposes the hybrid recommendation model of commodity based on parallel computing. This model improves the results of the recommendation through combining various ways that have existed in the main e-commerce websites. In this model, there is one big problem that the...
In this paper, we investigate computing systems and network architectures, dedicated to high frequency trading applications and evaluate their performances. Both a high processing speed and low network latency are important for high-frequency traders. The financial market literature suggests, however, that extremely high speeds discourage other traders from participating in the market, therefore harming...
Java8 introduced the notion of streams that is a new data structure and supports multi-core processors. When the sum method is called for a stream of floating-point numbers, the summation is calculated at high-speed by applying MapReduce, which distributes computations to cores. However, since floating-point calculation causes an error, simple adaptation of this method can not determine the result...
In a cloud computing environment, data replication is frequently used for failure recovery. However, data replication can be used also to improve the performance of the execution of application. In this context, this article presents a policy for data replication for a computational cloud with bioinformatics data, such that the execution of application of bioinformatics is reduced.
Parallel computing namely the unification of multiple computers or servers into a single unit that can work simultaneously or processes simultaneously. Parallel computing is creating programs and processes run faster as more and more CPU used. Basically parallel computing using network media, but that is characteristic in particular is how to resolve the issue. Problems encountered here is how me-rendering...
In this paper, the performance of parallel computing will be thoroughly discussed in the domain of image matching. The concept of image matching is widely used in the areas of security, medical and computer vision which require comparing two images for similarities. However, depending on the size of images, it is highly possible that the application computation cannot be handled in a single processor...
Nowadays, it is widely accepted that exploiting all forms of parallelism is the only way to significantly improve performance. The three major forms of parallelism on a modern processor are ILP, DLP, and TLP, which are not mutually exclusive. To gain further performance improvements, MPI can be used on a cluster of computers. This paper exploits the capabilities of distributed multi-core Intel processors...
The ability to design effective solutions using parallel processing should be a required competency for every computing student. However, teaching parallel concepts is sometimes challenging and costly, specially at early stages of a computer science degree. For such reasons we present a set of modules to teach parallel computing paradigms using as examples problems that are computationally intensive,...
This live demonstration features a vision chip based on a neighborhood level parallel processing paradigm. Processors are physically embedded within groups of pixels, complete with memory and algorithmic capabilities controlled by a custom instruction set. This results in a scalable resolution, parallel processing vision chip with flexible programmability that can perform a wide variety of image and...
In today's scenario there is a need of fast computers to perform huge tasks in less time. In serial computation one task will be done after another but it takes more time. On the other hand, time taken by a computation problem can be reduced by performing several operations simultaneously. Parallel computing [4,8,9] is the concurrent use of multiple resources to solve a single problem. A computational...
The paper discusses the efficiency of parallel implementation an algorithm of frequency analysis of textual information. The algorithm is implemented as a multithreaded application. Two approaches for job distribution between threads are compared — the text distribution and alphabet distribution. Experiment results shown that both methods can be used for acceleration of the procedure of text frequency...
Dataflow computing is proved to be promising in high-performance computing. However, traditional dataflow architectures are general-purpose and not efficient enough when dealing with typical scientific applications due to low utilization of function units. In this paper, we propose an optimization of dataflow architectures for scientific applications. The optimization introduces a request for operands...
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