Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
open source performance library for deep learning applications. The library
accelerates deep learning applications and framework on Intel(R) architecture.
Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can
use to implement deep neural networks (DNN) with C and C++ interfaces.
DNN functionality optimized for Intel architecture is also included in Intel(R)
Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible
with Intel MKL-DNN and does not include certain new and experimental features.
===> The following configuration options are available for onednn252-2.5.2:
====> Use CPU-specific optimizations: you have to select exactly one of them
SIMD_DEFAULT=on: Default, no non-default SIMD instructions are used
SIMD_NATIVE=off: Optimize for this CPU
SIMD_SSE41=off: Use SSE4.1 instructions
====> Threading runtime for CPU engines: you have to select exactly one of them
OPENMP=on: Parallel processing support via OpenMP
TBB=off: Threading Building Blocks
SEQ=off: Sequential (no parallelism)
THREADPOOL_STANDALONE=off: Threadpool based on the standalone implementation
THREADPOOL_EIGEN=off: Threadpool based on the Eigen implementation
THREADPOOL_TBB=off: Threadpool based on the TBB implementation
===> Use 'make config' to modify these settings
all: drop support for FreeBSD 13, mips and risc64sf
Reviewed by: many (*)
Differential Revision: https://reviews.freebsd.org/D55624
Differential Revision: https://reviews.freebsd.org/D55642 (merged in from bofh)
many (*)
acm arrowd brooks cmt dch decke dinoex eduardo fluffy fuz gahr garga
jbeich joerg jrm kai kenrap martymac matthew mfechner michaelo mizhka
nobutaka pkubaj se tagattie thj
Anton Saietskii
GenericRikka
Gert Doering
Jan Bramkamp
Oleh Hushchenkov
Oleksandr Kryvulia
Ralf van der Enden
Yamagi
desktop kde python tcltk office
