Built for Speed and Scalability

Experience great performance improvements out of the box for computation packages like NumPy*, SciPy*, and scikit-learn*.

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Benchmark source code Native Code Python

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Benchmark Source Code

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Benchmark Source Code

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Benchmark Source Code

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Benchmark Source Code

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Benchmark Source Code

Product and Performance Information

1

Performance results are based on testing as November 27, 2019 18 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely scure.[Configuration Disclosure core i7]

Testing by Intel as of November 27, 2019. Configuration: Stock Python: Python 3.7.5 h0371630_0 installed from conda*, NumPy 1.17.4, Numba* 0.46.0, llvmlite 0.30.0, SciPy 1.3.2, scikit-learn* 0.21.3 installed from PIP*; Intel® Distribution for Python* 2020 Gold: Python 3.7.4 hf484d3e_3, NumPY 1.17.3 py37ha68da19_4, Intel® Math Kernel Library (Intel® MKL) 2020 intel_133, mkl_fft 1.0.15 py37ha68da19_3, mkl_random 1.1.0 py37ha68da19_0, Numba 0.45.1 np117py37_1, llvmlite 0.29.0 py37hf484d3e_9, SciPY 1.3.1 py37ha68da19_2, scikit-learn 0.21.3 py37ha68da19_14, Intel® Data Analytics Acceleration Library (Intel® DAAL) 2020 intel_133, daal4py 2020 py37ha68da19_4; CentOS Linux* 7.4.1708, kernel 3.10.0-693.el7.x86_64; Hardware: Intel® Core™ i7-7567U processor at 3.50GHz (1 socket, 2 cores per socket, HT:2), 32 GB of DDR4 RAM, 2 DIMMs of 16 GB at 2133 MHz.

2

Performance results are based on testing as of November 27, 2019 may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. [Configuration Disclosure core i7 ]

Testing by Intel as of November 27, 2019. Configuration: Stock Python: Python 3.7.5 h0371630_0 installed from conda, NumPy 1.17.4, Numba 0.46.0, llvmlite 0.30.0, scipy 1.3.2, scikit-learn 0.21.3 installed from PIP; Intel® Distribution for Python* 2020 Gold: Python 3.7.4 hf484d3e_3, NumPY 1.17.3 py37ha68da19_4, Intel MKL 2020 intel_133, mkl_fft 1.0.15 py37ha68da19_3, mkl_random 1.1.0 py37ha68da19_0, Numba 0.45.1 np117py37_1, llvmlite 0.29.0 py37hf484d3e_9, SciPY 1.3.1 py37ha68da19_2, scikit-learn 0.21.3 py37ha68da19_14, Intel DAAL 2020 intel_133, daal4py 2020 py37ha68da19_4; CentOS Linux 7.3.1611, kernel 3.10.0-514.el7.x86_64; Hardware: Intel® Xeon® Platinum 8280 processor at 2.70 GHz (2 sockets, 28 cores per socket, HT: off), 256 GB of DDR4 RAM, 16 DIMMs of 16 GB at 2666 MHz.

3

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, see Performance Benchmark Test Disclosure.

 

4

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804