Mensajes en el blog

Supercomputing 2016 HPC Server Demos

Super Computing 2016 brought into sharp focus the powerful impact HPC is having on everything from life sciences and research to machine learning.

Autor Mike P. (Intel) Última actualización 30/09/2019 - 16:50

Boosting Deep Learning Training & Inference Performance on Intel® Xeon® and Intel® Xeon Phi™ Processors

In 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.
Autor Vikram S. (Intel) Última actualización 15/10/2019 - 15:30

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

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.
Autor Última actualización 15/10/2019 - 15:30

Migrating Applications from Knights Corner to Knights Landing Self-Boot Platforms

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.
Autor Michael Greenfield (Intel) Última actualización 15/10/2019 - 16:40

IDF'15 Webcast: Data Analytics and Machine Learning

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
Autor Mike P. (Intel) Última actualización 15/10/2019 - 16:50
Mensajes en el blog

How Intel® Xeon Phi™ Processors Benefit Machine Learning/Deep Learning Apps and Frameworks

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.
Autor Pradeep Dubey (Intel) Última actualización 15/10/2019 - 18:24