In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
A number of usage models are possible given the flexible interfaces provided by the Cache Allocation Technology (CAT) feature, including prioritization of important applications and isolation of applications to reduce interference.
Cache Allocation Technology (CAT) provides benefits across a number of usages, as described in the previous article in this series. This article briefly describes one proof point from the data center (prioritizing a web server to improve its performance) and one from communications (protecting a key communications infrastructure virtual machine (VM)).
This article provides a snapshot of some of the software-enabling collateral available for the Cache Allocation Technology (CAT) feature.
This article provides a number of Memory Bandwidth Monitoring (MBM) example proof points and discussion fitting with the usage models described in previous articles.
This article describes software support available for the Memory Bandwidth Monitoring (MBM) feature. Prior blogs in this series have included an overview of the MBM feature and architecture and usage models, and detailed examples of proof points.