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Gennady_F_Intel

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09-11-2018
09:38 PM

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Intel® MKL version 2019 is now available

Intel® Math Kernel Library (Intel® MKL) is a highly optimized, extensively threaded, and thread-safe library of mathematical functions for engineering, scientific, and financial applications that require maximum performance.

**Intel MKL 2019 Release is now ready for download.**

Please check the Intel® MKL 2019 Release Notes to learn more information.

**What's New in Intel® MKL v.2019:**

- BLAS Features:
- Introduced automatic S/DGEMM JIT capability for small matrix sizes (m,n,k <=16) to improve S/DGEMM performance for Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extensions 512 (Intel® AVX-512) when compiling with MKL_DIRECT_CALL_JIT (threaded usage) or MKL_DIRECT_CALL_SEQ_JIT (sequential usage).
- Introduced new functions to JIT (create) optimized S/DGEMM-like matrix multiply kernels for small matrix sizes (m,n,k <=16) for Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extensions 512 (Intel® AVX-512), execute the optimized kernel created using matrices with matching dimensions, and to remove (destroy) the JIT kernel.

- Sparse BLAS:
- Introduced SYPR and Sp2M functionality for triple matrix multiply ABA^t and matrix multiply AB (and their transposes).
- Improved performance of Inspector-Executor Sparse BLAS routines for Intel® TBB and sequential threading layers.
- Improved performance of SpMV , MKL_SPARSE_[S,D,C,Z]_SYMGS and MKL_SPARSE_[S,D,C,Z]_TRSV routines for Intel® Advanced Vector Extensions 512 (Intel® AVX-512).
- DNN:
- Deep Neural Network (DNN) component is deprecated and will be removed in the next Intel MKL release. We will continue to provide optimized functions for deep neural networks in Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN).

- LAPACK:
- Aligned MKL LAPACK functionality with Netlib LAPACK 3.7.1 and 3.8.0:
- Added routines for symmetric indefinite matrix factorization using a 2-stage Aasen’s algorithm.

- Improved performance of ?GETRF for Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and other micro architectures with OpenMP* threading.
- Improved performance of ?GETRF and ?POTRF with TBB* threading.

- Aligned MKL LAPACK functionality with Netlib LAPACK 3.7.1 and 3.8.0:
- ScaLAPACK:
- Improved performance and significantly reduced memory footprint of ScaLAPACK Eigensolvers P?[SY|HE]EV[D|X|R] routine.

- FFT:
- Improved performance of 1D real-to-complex FFT.
- Improved performance of C2C 1D and 2D FFT for Intel® Advanced Vector Extensions 512 (Intel® AVX-512).

- Sparse Solvers:
- Introduced SparseQR functionality.
- Introduced Extreme{EVD/SVD} functionality to calculate set of most positive or most negative eigen/singular values of a symmetric(Hermitian) matrix.
- Introduced support of partial inversion of sparse matrices (compute diagonal of inverse) in Intel® MKL PARDISO.

- Random Generators
- Introduced Multinominal Random Number Generators.

Intel MKL is available as part of the Intel® Parallel Studio XE and Intel® System Studio. Please visit the Intel® Math Kernel Library Product Page.

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Paul_C_2

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10-23-2018
12:42 PM

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Is there a release date planned for 2019 update 1 that can be shared?

Gennady_F_Intel

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10-24-2018
07:24 AM

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meyer__Karin

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10-24-2018
04:15 PM

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- Introduced support of partial inversion of sparse matrices (compute diagonal of inverse) in Intel® MKL PARDISO.

I am very interested in this new feature - but cannot find it in the developer reference for MKL on-line. Could you provide a link to the relevant documentation, please?

Gennady_F_Intel

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10-24-2018
08:43 PM

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Paul_C_2

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10-24-2018
09:16 PM

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Gennady,

There are some fixes in 2019 (relative to 2018.3) that we need. It's a fair amount of effort to upgrade our code base to new MKL releases, so I was just wondering when your next update was to maybe time our update around it.

Paul

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