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This research paper is a study on trade-off of using Java to create cluster capable of achieving high-performance computing. Here we have made a distributed computing architecture in Java using Low-level API implementations. Time is crucial in any field of work. We value fast and optic results. Creating cluster base through traditional method using C, C++ or FORTAN programming language can be tedious,...
Deep Neural Networks (DNNs) have demonstrated state-of-the-art performance on a broad range of tasks involving natural language, speech, image, and video processing, and are deployed in many real world applications. However, DNNs impose significant computational challenges owing to the complexity of the networks and the amount of data they process, both of which are projected to grow in the future...
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...
Software-based network packet processing on standard high volume servers promises better flexibility, manageability and scalability, thus gaining tremendous momentum in recent years. Numerous research efforts have focused on boosting packet processing performance by offloading to discrete Graphics Processing Units (GPUs). While integrated GPUs, residing on the same die with the CPU, offer many advanced...
In today’s highly intertwined network society, the demand for big data processing frameworks is continuously growing. The widely adopted model to process big data is parallel and distributed computing. This paper documents the significant progress achieved in the field of distributed computing frameworks, particularly Apache Hama, a top level project under the Apache Software Foundation, based on...
With supercomputer system scaling up, the performance gap between compute and storage system increases dramatically. The traditional speedup only measures the performance of compute system. In this paper, we firstly propose the speedup metric taking into account the I/O constraint. The new metric unifies the computing and I/O performance, and evaluates practical speedup of parallel application under...
Data analysis, mining, and machine learning on large-scale data sets have gained much attention in the academia and industry. Tremendous computational and storage capacities are required in order to handle such large data sets. In these days, the conventional wisdom is to build a large cluster which consists of a number of commodity x86 machines, each of which is equipped with two or four physical...
Since the dawn of the big data era the search giant Google has been in the lead for meeting the challenge of the new era. Results from Google's big data projects in the past decade have inspired the development of many other big data technologies such as Apache Hadoop and NoSQL databases. The study article examines ten major milestone papers on big data management published by Google, from Google...
Over the last few years, MapReduce systems has become popular for processing large-scale data sets and are increasingly being used in web indexing, data mining, and machine learning. Unlike simple application scenarios such as word count, many applications of MapReduce exhibit strong skewed access patterns in real production environment, the data access is non-uniform, often only a small portion of...
Due to the rapid improvement in resolution and codec, the number of video sensing device grows fast in the recent years. Tremendous data need to be stored and processed. To meet such a need, we developed a video hosting and processing platform for near-real time applications. An intelligent device can obtain connection-less upload function from our client-side SDK. Video data are sliced into pieces,...
The collective experience is the experience of unity, belonging, and purpose that occurs when large numbers of people come together and perceive themselves and others as part of a single social entity, and interact with each another accordingly. We are exploring how the collective experience can be supported in a fully computer-mediated environment through activities where a virtual crowd performs...
With the increase of science project in size and complexity, it is necessary to integrate and share global research resources for distributed experimentation and simulation. This paper presents an architecture of Cooperative Remote Laboratories (CRLab) with scalability, flexibility and heterogeneity. It consists of a central Web server, simulation servers, instrument servers and experiment devices,...
Cloud storage is one of the underlying services in cloud computing. However, there are some critical issues in file storage which should be resolved urgently, such as the blocking file storage. In order to solve the shortages of hot file storage and parallel computing support issues in fixed-blocking storage, we propose a smart-blocking file storage method in this paper. By setting up 6 grouping factors,...
Database systems have been essential for all forms of data processing for a long time. In recent years, the amount of processed data has been growing dramatically, even in small projects. At the other hand, database management systems tend to be static in terms of size and performance, which makes scaling a difficult and expensive task. Enterprises may have multiple database systems spread across...
The large gap between the speed in which data can be processed and the performance of I/O devices makes the shared storage infrastructure of a cluster a great bottle-neck. Parallel File Systems try to smooth such difference by distributing data onto several servers, increasing the system's available bandwidth. However, most implementations use a fixed number of I/O servers, defined during the initialization...
Parallel program performance analysis plays an important role in exploring parallelism and improving efficiency of parallel programs. A remote interactive parallel program performance analysis tool based on dynamic code instrumentation is designed on the basis of analysis and comparison of existing program performance analysis tools. A hierarchical structure is adopted by this tool which consists...
Futures trading evaluation system is used to analyze trading history of individuals, to find out the root cause of profit and loss, so that investors can learn from their past and make better decisions in the future. To analyze trading history of investors, the system processes a large volume of transaction data, to calculate key performance indicators, as well as time series behavior patterns, finally...
This paper addresses the implementation of a video player on an embedded asymmetric, dual-core architecture with the two cores having significantly different performances. The paper proposes a new parallelization approach that effectively handles the issues of load balancing and inter-core communication. Load balancing is based upon a coarse-grained strategy at the function level where the two cores...
Parallel programming has become mandatory to fully exploit the potential of modern CPUs. The data-flow model provides a natural way to exploit parallelism. However, traditional data-flow programming is not trivial: specifying dependencies and control using fine-grained tasks (such as instructions) can be complex and present unwanted overheads. To address this issue we have built a coarse-grained data-flow...
Amazon S3-style storage is an attractive option for clouds that provides data access over HTTP/HTTPS. At the same time, parallel file systems are an essential component in privately owned clusters that enable highly scalable dataintensive computing. In this work, we take advantage of both of those storage options, and propose pWalrus, a storage service layer that integrates parallel file systems effectively...
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