Intel Data Analytics Acceleration Library

The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*, R, and Matlab*. for highly efficient data access. Intel DAAL is available for Linux*, OS X* and Windows*.

Intel® Atom™ processors support in Intel® Math Kernel Library

Intel® Math Kernel Library (Intel® MKL) 11.2 includes support for the latest 32-bit and 64-bit Intel® Atom™ processors.

Intel® Atom™ processors supporting Supplemental Streaming SIMD Extensions 3 (Intel® SSSE3), Intel® Streaming SIMD Extensions 4.1 (Intel® SSE4.1) or Intel® Streaming SIMD Extensions 4.2 (Intel® SSE4.2) instruction sets will run out-of-the-box with Intel® MKL. Intel® MKL’s internal dispatching mechanism identifies which instruction sets the Intel® Atom™ processor supports and automatically selects the optimal code path to execute during run time.

  • C/C++
  • MKL
  • How Intel® AVX Improves Performance on Server Application

    The latest Intel® Xeon® processor E7 v2 family includes a feature called Intel® Advanced Vector Extensions (Intel® AVX), which can potentially improve application performance. Here we will explain the context, and provide an example of how using Intel® AVX improved performance for a commonly known benchmark.

    For existing vectorized code that uses floating point operations, you can gain a potential performance boost when running on newer platforms such as the Intel® Xeon® processor E7 v2 family, by doing one of the following:

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