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TensorFlow Deep Learning Optimized for Modern Intel Architectures

Researchers at Google and Intel recently collaborated to extract the maximum performance from Intel® Xeon and Intel® Xeon Phi processors running TensorFlow*, a leading deep learning and machine learning framework. This effort resulted in significant performance gains and leads the way for ensuring similar gains from the next generation of products from Intel. Optimizing Deep Neural Network (DNN) models such as TensorFlow presents challenges not unlike those encountered with more traditional High Performance Computing applications for science and industry.

Speeding Up Big Data Analysis With Intel MKL and Intel DAAL

“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.”

Deep Learning Frameworks Get a Performance Benefit from Intel MKL Matrix-Matrix Multiplication

Intel® Math Kernel Library 2017 (Intel® MKL 2017) includes new GEMM kernels that are optimized for various skewed matrix sizes. The new kernels take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and achieves high GEMM performance on multicore and many-core Intel® architectures, particularly for situations arising from deep neural networks..

Machine Learning and the Intel Xeon Phi Processor

“With up to 72 processing cores, the Intel Xeon Phi processor x200 can accelerate applications tremendously. Each core contains two Advanced Vector Extensions, which speeds up the floating point performance. This is important for machine learning applications which in many cases use the Fused Multiply-Add (FMA) instruction.”