Tim Mattson (Intel) has authored an extensive series of excellent videos as in introduction to OpenMP*.
I write this as an observer of something I had yet to witness.
Intel’s Excite project uses a combination of symbolic execution, fuzzing, and concrete testing to find vulnerabilities in sensitive code.
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.
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.
The core components of the Internet get updated constantly. Every time the source changes, the health and performance can change. A single source code change can fail to build, can break compatibility with existing code and can change the performance anywhere from a fraction of a percent up to 10% or more on major customer workloads. We're trying to read the pulse of our core components (Python,...