Speeding Up a Big Data Platform

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1 Case Study Software and Services Big Data Analytics Speeding Up a Big Data Platform Software MeritData Inc. improves performance and the potential for big data algorithms and visualization with high-performance Intel libraries Through close collaboration with Intel engineers, we adopted the Intel Data Analytics Acceleration Library and Intel Math Kernel Library for algorithm optimization in our big data analysis platform (Tempo*). The performance and customers experience is improved significantly. We really appreciate the collaboration with Intel, and are looking forward to more collaboration. Jin Qiang Data Mining Algorithm Architect MeritData Inc. MeritData, Inc. is a top big data analysis technology and service provider in China. Its Tempo* big data platform has been widely used by well-known power, manufacturing, financial, and global enterprises and by cloud service providers. With the fusion of high-performance computing technology, leading data analysis algorithms, high-dimensional visualization, and creative data visualization language, MeritData helps its customers explore and exploit data value and ultimately creates value through data processing, data mining, and data visualization solutions. To analyze data quickly and precisely, MeritData must optimize its algorithms from the perspectives of algorithm principles, computing, and programming. Intel worked with MeritData s algorithm engineers to optimize the company s multiple data mining algorithms. The extreme learning machines (ELM), in-house L1/2 sparse iterative algorithm, and linear regression (LR) were all optimized by Intel Data Analytics Acceleration Library (Intel DAAL) and Intel Math Kernel Library (Intel MKL). The result was average performance improvements ranging from 3x all the way to 14x. The Need for Speed in Big Data Analysis Platforms The development of hardware and information technology have led to a new era of big data. Global data is growing exponentially and with it, the market for big data analysis and vigorous big data services. Almost every large company has invested resources in big data analysis and expects to integrate years of historic and newly generated data and to mine the value of data quickly and accurately. As one of the industry s top large data analysis technology and service providers, MeritData provides the Tempo big data analysis platform, which can store and process all kinds of data for customers. To analyze the data effectively, and to process more data more quickly, the company needed to improve the algorithm s performance to the extreme. Algorithm modeling can help with a high computation load that needs to perform repeated computation iterations on input data. When the data volume is small, operating time is usually acceptable. But as the data volume surges, the operating time of some algorithms increases exponentially until they can no longer meet customer requirements. To meet the continually growing demand for data mining, MeritData worked closely with Intel. Using Intel MKL and Intel DAAL, MeritData was able to accelerate Tempo s core algorithm library, giving its customers a powerful data analysis solution. Compared with the original hardware-independent implementation, the new scheme can quickly and accurately analyze huge amounts of data processing and modeling, enabling customers to quickly explore and realize the full value of their data with an average performance improvement of 3x and a speedup of up to 14x.

2 Solution: Tempo Big Data Analysis Platform Based on Intel MKL and Intel DAAL, Intel worked with MeritData to build a Tempo big data analysis platform to accelerate the core algorithm libraries on Intel architecture. Through the cloud computing architecture, the team implemented a big data analysis solution for fast modeling and analysis. At the same time, it provided integrated services to satisfy its customers with different fields and levels of data analysis to achieve the value of their data, data visualization exploration, and in-depth analysis. The system architecture of the Tempo platform includes the data access layer, analysis and modeling, result display, and access layer, providing unified cloud service access, cloud resource scheduling, and cloud platform management. Data Access Layer The data access layer needed to be able to accept different sources of data, including intelligently adapting to both SQL* and non-sql databases, docking with Kafka*, flume streaming data sources, and unstructured text data sources. Analysis and Modeling Layer Based on Intel MKL and Intel DAAL, it accelerates the core analysis algorithms of the Tempo data analysis platform. The intelligent platform can: Manage node scheduling Analyze assigned compute nodes Write the related job log Classify anomaly information records Write the related job log Result Display and Access Layer After analysis modeling is completed, the modeling results are displayed as statistics and can generate a result report to improve the utilization rate of resources and help make decision-making more agile. It also allows the customer to build a new model based on pretrained results, directly accepting the new data sources and making accurate predictions. This layer provides an access interface in the form of an API call, making it convenient for the customer s future development. The Technical Advantage Intel provides not only a strong CPU, but also high-performance software for MeritData s needs. The Intel Xeon processor E v4 2.2 GHz provides up to 44 cores, 88 threads, and 3.0 teraflops of single-precision peak performance. They re the good choice for virtualization and business-critical like big datamining applications. Intel MKL provides a wide range of matrix, vector, and math processing routines that are highly optimized for multilevel parallelism on Intel architecture. These routines make efficient use of available resources in multicore, many-core, and clustered architectures and automatically balance workloads across Intel Xeon processors. The highly optimized functions (Figure 2) are widely used in computing performancedemanding applications in the fields of science, engineering, and financial services, which can make full use of Intel s multicore processors that can maximize the performance of the application and reduce development time. Figure 1. Tempo* big data analysis platform for data-mining system architecture 2

3 Figure 2. Intel Math Kernel Library (Intel MKL) components Intel MKL includes defacto BLAS/LAPACK/FFTW functions, which enable it to easily integrate into third-party software such as MATLAB* and NumPy*. It can also easily replace the corresponding functions in third-party software such as Eigen*. (Learn more here.) Like Intel MKL, Intel DAAL is a high-performance library; however, it has more functionality than Intel MKL for big data, helping speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (preprocessing, transformation, analysis, modeling, validation, and decision-making), for offline, streaming, and distributed analytics usages. It is designed for use with popular data platforms including Hadoop*, Spark*, R*, and MATLAB for highly efficient data access. Intel DAAL (Figure 3) provides a rich set of algorithms, ranging from the most basic descriptive statistics for data sets to more advanced data mining and machine learning algorithms. It can help big data developers develop highly optimized code for many big data algorithms with relatively little effort. Boosting Tempo s Performance MeritData s customers have an insatiable need for higher computing performance to let them model and analyze bigger data with faster speed and greater fidelity. Based on Intel MKL and Intel DAAL, Intel worked with MeritData to accelerate core algorithms of the Tempo large data analysis platform over the original method. Performance was boosted dramatically, effectively helping customers maximize their data value, meet their business needs, and quickly analyze huge amounts of new data. Specifically, the team was able to optimize Tempo s ELM and in-house L1/2 sparse iterative algorithms. ELM Algorithm ELM is one of MeritData s data-mining algorithms. The team profiled the code of the ELM algorithm and found the performance bottleneck was matrix computation, including inversion of functions like matrix and matrix multiply. Originally, MeritData used the Eigen library, which provided all kinds of matrix operations but less performance optimization than Intel MKL. MeritData decided to replace the related functions with Intel MKL functionality. The code replacement was straightforward (Table 1). In-House L1/2 Sparse Iterative Algorithm The sparse iterative algorithm, based on L1/2, is MeritData s inhouse algorithm. (Originally, the team used the Flanagan Java scientific library to implement the code.) After test and Figure 3. Intel Data Analytics Acceleration Library components 3

4 Table 1. Comparison showing before and after the machine learning algorithm was used for code optimization Figure 4. Time comparison before and after the optimization with Intel MKL 4

5 Table 2. L1/2 sparse code before and after optimization with the iteration algorithm Figure 5. L1/2 sparse iterative algorithm running-time comparison before and after the transformation 5

6 analysis, the team found the performance bottlenecks in this algorithm were the kernel function and basic matrix operations. For basic matrix operations, they used the Intel MKL BLAS function. For kernel function, they used Intel DAAL kernel functionality. Table 2 shows the exact optimization before and after switching to Intel DAAL. Summary With a better and more in-depth understanding of data, MeritData s customers want to integrate both historic and newly generated data and mine the value in it using limited resources. MeritData s Tempo platform needs to provide a faster and highly efficient solution for their customers data mining. Through close collaboration with Intel engineers, MeritData adopted both Intel DAAL and Intel MKL for algorithm optimization in the Tempo big data analysis platform. Now the company can analyze big data fast, model accurately, and satisfy customers from different fields with different levels of data analysis. The solution greatly improved MeritData s data analysis processing capacity and significantly improved the performance and user experience. MeritData and Intel will continue working together to optimize MeritData Tempo to keep up with the increasing computation capability provided by Intel architecture. This will help MeritData developers deliver higher performance with less effort, so they can continue to provide the best possible user experience for their customers. Learn More Intel Data Analytics Acceleration Library > Intel Math Kernel Library > Intel technologies features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer, or learn more at Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information go to Intel does not control or audit the design or implementation of third party benchmark data or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase. This document and the information given are for the convenience of Intel s customer base and are provided AS IS WITH NO WARRANTIES WHATSOEVER, EXPRESS OR IMPLIED, INCLUD- ING ANY IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT OF INTELLECTUAL PROPERTY RIGHTS. Receipt or possession of this document does not grant any license to any of the intellectual property described, displayed, or contained herein. Intel products are not intended for use in medical, lifesaving, life-sustaining, critical control, or safety systems, or in nuclear facility applications. Copyright 2016 Intel Corporation. All rights reserved. Intel, Xeon, and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries. * Other names and brands may be claimed as the property of others. Printed in USA 1016/SS Please Recycle 6

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