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Speeding Up Big Data Analysis With Intel MKL and Intel DAAL

With the ever increasing amount of data that is being produced, making sense of it that results in new insights can be a challenge. While many new business processes are being developed to take advantage of this data, the performance that is expected of a business analyst or data scientist cannot be ignored. Interactive performance and compelling visualizations that give new meaning to the data is critical and will lead to better decisions. Speeding up the big data analytics pipeline can lead to increased profits and insights.

While hardware performance has increased greatly over time as new types of computing systems have been developed, software algorithms for the analysis of large amounts of data has also progressed. New algorithms that can query massive amounts of data an draw conclusions have been developed, but these algorithms need to be optimized on the underlying hardware. This is where the expertise of vendors who develop the hardware can add tremendous value. Optimizing the underlying libraries that can execute with a high degree of parallelism will definitely lead to improved performance for the software and productivity gains for the organization.

The Intel Math Kernel Library (MKL) can be used to speed up all kinds of applications.Click To Tweet

The Intel Math Kernel Library (MKL) and the Intel Data Analytics Acceleration Library (DAAL) are examples of how a company known for it hardware excellence also works with developer and end users to create a very optimized environment for big data analytics. As developers create new software in a variety of domains, the underlying libraries that Intel supplies can have a tremendous impact on overall performance. Staring with the original hardware independent  software, the modified software that uses Intel performance libraries in order to analyze huge amounts of data can show performance gains of up to 14X. The result is that more data can be analyzed faster, leading to new revelations.

provides a wide range of matrix, vector, and math processing routines that are highly optimized for multilevel parallelism on Intel architecture. With the advent of multicore and manycore processors, a tuned library can use all of the available resources including clustered architectures that can automatically balance workloads across Intel Xeon processors. The highly optimized functions are widely used in computing performance- demanding applications in the fields of financial services, engineering, and science and research applications, which can make full use of the range of Intel’s processors. This leads to faster results with a more optimized application and associated stack.

The Intel DAAL  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.

As better understanding of data becomes a business critical process, organizations may want to look back in time to understand how behavior has changed or how buying patterns have changed, for example.  While more hardware can sometimes lead to more discoveries, it is important to be able to use the existing servers and systems to their fullest, leading to a reduced total cost of ownership (TCO). By optimizing software through the use of very tuned libraries such as Intel MKL and Intel DAAL, big data applications can lead to more insight in less time.  While the goals of different types of organizations may be quite varied, the faster that an answer or guidance can be returned will benefit all who are involved.

Download the Intel® Math Kernel Library (Intel® MKL) for free.

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