ZHW: A Numerical CODEC for Big Data Scientific Computation

Big data is one of the important development direction of modern information technology, realizing the sharing and analysis of large data will bring immeasurable economic value, but also has a tremendous role in promoting the social. In the age of big data, unified the data representation, large data processing, query, analysis and visualization are the key problem to be solved urgently. In order to provide a standardized framework construction of the large data service platform, this paper designed a large data service oriented architecture user experience. Secondly, in the aspect of data model, in order to achieve high data service for non structured data, the design of the non structured data model based on subject behavior. In large data service model, algebraic model large data services and their composition was established by using process algebra. In large data service applications, detailed retrieval, process analysis and visualization services, and by improving the retrieval accuracy and efficiency of the service in two aspects of measures to achieve the high data service optimization.

With the highly demanded requirements for manipulating large scientific datasets, scientists are in need of flexible cluster-level software to execute fast scientific data analysis. In this paper, we discuss whether the Apache Spark framework is suitable for scientific data management. We present our system SparkArray, which extends Spark with a multidimensional array data model and a set of common used array operations (e.g., filter, subarray, smooth and join). We present analysis and performance evaluation results on different implementation methods for executing array operations on clusters. We also compared the performance of SparkArray to SciDB, a recent scientific database management system, using the workloads of the Standard Science DBMS Benchmark (SS-DB). The results show that SparkArray is feasible alternative solution for large-scale scientific data management, especially when scientists require fast data loading or one-time data analysis on large scientific datasets.

Parallel TCP connections are used for large scientific dataset transfers to increase throughput. Therefore, to accurately characterize big-data movement, it is important to reconstruct parallel flowsets from traffic measurements. In this work, we start with NetFlow records collected in an operational research-and-education network across which large scientific datasets are moved routinely, reconstruct individual elephant flows from the NetFlow records, and assemble parallel flowsets from elephant flows. Our findings are as follows. The top 1% of flowset sizes were in the hundreds of GBs to low TBs range, 95% of flowsets had rates less than 2.5 Gbps, and 99% of flowsets had durations shorter than 4 hours. Median flowset rate increases and rate variance decreases with increasing number of per-flowset component flows. Such findings are useful for network planning, traffic engineering, and for improving user performance, since large dataset transfers are among the most demanding of network applications.

Numerical simulations running on very large High Performance Computer clusters still suffer from the I/O bottleneck. The cost of communication can overwhelm the cost of computation, and scales inversely with the number of processors used in the cluster. In previous work we have developed a multiresolution data model to help improve performance for visualizations of very large multi dimensional scientific data sets. In our approach, the data is represented as a multi level hierarchy. Reconstructive error analysis is used to identify regions in the data where the data loss is greatest. We have incorporated this data model into the OpenGGCM solar wind simulation environment. In this paper, we demonstrate that this approach can reduce the I/O and improve the overall performance of a large numerical simulation environment.

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.

In data-parallel computing that uses a graphic processing unit (GPU), processing of large data requires that multiple GPUs be used in the computer to increase its execution performance. Increasing processing performance by using multiple computing resources has been enabled by the development of computing frameworks based on a directed acyclic graph (DAG). However, their performance degrades in out-of-core processing, which often occurs in processing of large data on GPUs with limited memory capacity. The GPU data input/output (I/O) for data swapping between host memory and GPU memory during the execution of a user DAG is usually a performance bottleneck. A computing framework called is proposed to overcome this drawback. It uses a novel scheduler that involves two methods to minimize the total amount of GPU data I/O of data swapping. First, it performs locality-aware scheduling. When it schedules a task, it selects one that requires the minimum amount of data swapping and reuses as much of the data residing in GPU memory as possible. Second, it extends the locality-aware scheduling so that GPUs can execute data prefetching. Prefetching data that are swapped out from a GPU enables efficient use of bottleneck GPU I/O resources. To prefetch the input data of future tasks, it is required to determine the schedule of future tasks. Victream's scheduler (hereafter, the Victream scheduler) extends the locality-aware scheduling so that it can schedule future tasks to enable data prefetching that is executed in the way that minimizes the amount of data I/O of data swapping. Evaluation of a Victream prototype showed that the performance of Victream is better than that of conventional frameworks by up to 117%.

User-Defined Functions (UDF) allow application programmers to specify analysis operations on data, while leaving the data management tasks to the system. This general approach enables numerous custom analysis functions and is at the heart of the modern Big Data systems. Even though the UDF mechanism can theoretically support arbitrary operations, a wide variety of common operations -- such as computing the moving average of a time series, the vorticity of a fluid flow, etc., -- are hard to express and slow to execute. Since these operations are traditionally performed on multi-dimensional arrays, we propose to extend the expressiveness of structural locality for supporting UDF operations on arrays. We further propose an in situ UDF mechanism, called ArrayUDF, to implement the structural locality. ArrayUDF allows users to define computations on adjacent array cells without the use of join operations and executes the UDF directly on arrays stored in data files without requiring to load their content into a data management system. Additionally, we present a thorough theoretical analysis of the data access cost to exploit the structural locality, which enables ArrayUDF to automatically select the best array partitioning strategy for a given UDF operation. In a series of performance evaluations on large scientific datasets, we have observed that -- using the generic UDF interface -- ArrayUDF consistently outperforms Spark, SciDB, and RasDaMan.

Database systems instructors face an ongoing challenge to develop meaningful assignments for their courses. We have found that instructors can successfully use large scientific datasets in teaching a database systems course to better prepare students for real-world database systems work.

We have setup a new course on the large scale data processing using clusters. It introduces the concepts and design of distributed systems. Many newly developed ideas such as Google file system and MapReduce programming framework for processing large scale data sets are introduced. Students will gain practical experience with distributed programming technologies via several small labs and one large multi-week final project. Labs and projects will be completed using Hadoop, an open-source implementation of Google's distributed file system and MapReduce programming model. We have taught this class named Mass Data Processing Technology on Large Scale Clusters for two years. This paper will describe the design, perform of the course as well as the experiences and lessons learned.

Mining large data sets is no longer the prerogative of computer scientists - specialists in a wide variety of domains are performing analytics as a day-to-day activity. Often such analyses are specific to the domain and analysts are required to devise new algorithms or techniques. For such scenarios, providing a high-level programming environment that delivers high performance on clusters is a challenge. We propose a framework that supports high-level programming using domain abstractions in data mining while delivering scalable performance on commodity clusters i.e. clusters of multi-core workstations. This framework includes a domain specific programming language, DWARF, to enable data mining specialists to rapidly prototype algorithms. DWARF is supported by a compiler that automatically parallelizes code by identifying domain specific patterns and translating them to parallel code that exploits data parallelism and task parallelism. The compiler generates code for a hybrid virtual machine supporting distributed memory model at the top level and shared memory model nested within. The code generated by the compiler can be scheduled on commodity clusters. We compare the proposed framework with other frameworks commonly used for data mining on distributed platforms.

Is power everything? What can we learn from large data sets

The massive adoption of smart phones and other mobile devices has generated humongous amount of spatial and spatio-temporal data. The importance of spatial analytics and aggregation is ever-increasing. An important challenge is to support interactive exploration over such data. However, spatial analytics and aggregation using all data points that satisfy a query condition is expensive, especially over large data sets, and could not meet the needs of interactive exploration. To that end, we present novel indexing structures that support spatial online sampling and aggregation on large spatial and spatio-temporal data sets. In spatial online sampling, random samples from the set of spatial (or spatio-temporal) points that satisfy a query condition are generated incrementally in an online fashion. With more and more samples, various spatial analytics and aggregations can be performed in an online, interactive fashion, with estimators that have better accuracy over time. Our design works well for both memory-based and disk-resident data sets, and scales well towards different query and sample sizes. More importantly, our structures are dynamic, hence, they are able to deal with insertions and deletions efficiently. Extensive experiments on large real data sets demonstrate the improvements achieved by our indexing structures compared to other baseline methods.

The value of protein models obtained with automated protein structure prediction depends primarily on their accuracy. Protein model quality assessment is thus critical to select the model that can best answer biologically relevant questions from an ensemble of predictions. However, despite many advances in the field, different methods capture different types of errors, begging the question of which method to use. We introduce TopScore, a meta Model Quality Assessment Program (meta-MQAP) that uses deep neural networks to combine scores from 15 different primary predictors to predict accurate residue-wise and whole-protein error estimates. The predictions on six large independent data sets are highly correlated to superposition-independent errors in the model, achieving a Pearson's Rall2 of 0.93 and 0.78 for whole-protein and residue-wise error predictions, respectively. This is a significant improvement over any of the investigated primary MQAPs, demonstrating that much can be gained by optimally combining different methods and using different and very large data sets.

Cloud resources are more often used for large scale computing and data processing. However, the usage of the cloud is different than traditional High-Performance Computing (HPC) systems and both algorithms and codes have to be adjusted. This work is often time-consuming and performance is not guaranteed. To address this problem we have developed the PCJ library (Parallel Computing in Java), a novel tool for scalable high-performance computing and big data processing in Java. In this paper, we present a performance evaluation of parallel applications implemented in Java using the PCJ library. The performance evaluation is based on the examples of highly scalable applications that run on the traditional HPC system and Amazon AWS Cloud. For the cloud, we have used Intel x86 and ARM processors running Java codes without changing any line of the program code and without the need for time-consuming recompilation. Presented applications have been parallelized using the PGAS programming model and its realization in the PCJ library. Our results prove that the PCJ library, due to its performance and ability to create simple portable code, has great promise to be successful for the parallelization of various applications and run them on the cloud with a similar performance as for HPC systems.

Biclustering refers to simultaneous clustering of objects and their features. Use of biclustering is gaining momentum in areas such as text mining, gene expression analysis and collaborative filtering. Due to requirements for high performance in large scale data processing applications such as Collaborative filtering in E-commerce systems and large scale genome-wide gene expression analysis in microarray experiments, a high performance prallel/distributed solution for biclustering problem is highly desirable. Recently, Ahmad et al [1] showed that Bipartite Spectral Partitioning, which is a popular technique for biclustering, can be reformulated as a graph drawing problem where objective is to minimize Hall's energy of the bipartite graph representation of the input data. They showed that optimal solution to this problem is achieved when nodes are placed at the barycenter of their neighbors. In this paper, we provide a parallel algorithm for biclustering based on this formulation. We show that parallel energy minimization using barycenter heuristic is embarrassingly parallel. The challenge is to design a bi-cluster identification algorithm which is scalable as well as accurate. We show that our parallel implementation is not just extremely scalable, it is comparable in accuracy as well with serial implementation. We have evaluated proposed parallel biclustering algorithm with large synthetic data sets on upto 256 processors. Experimental evaluation shows large superlinear speedups, scalability and high level of accuracy.