Super Computing 2016 brought into sharp focus the powerful impact HPC is having on everything from life sciences and research to machine learning.
Boosting Deep Learning Training & Inference Performance on Intel® Xeon® and Intel® Xeon Phi™ ProcessorsIn this work we present how, without a single line of code change in the framework, we can further boost the performance for deep learning training by up to 2X and inference by up to 2.7X on top of the current software optimizations available from open source TensorFlow* and Caffe* on Intel® Xeon® processors.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
While there are many different programming models for the Intel® Xeon Phi™ coprocessor (code-named Knights Corner (KNC)), this paper lists the more prevalent KNC programming models and further discusses some of the necessary changes to port and optimize KNC models for the Intel® Xeon Phi™ processor x200 self-boot platform.
This Technology Insight will demonstrate how to optimize data analytics and machine learning workloads for Intel® Architecture based data center platforms. Speaker: Pradeep Dubey Intel Fellow, Intel Labs Director, Parallel Computing Lab, Intel Corporation
Machine learning can take very large amounts of data to predict possible outcomes with a high degree of accuracy. The second-generation Intel® Xeon Phi processor has the processor performance and memory bandwidth to address complex machine learning applications.