Optimization

HOW Series “Deep Dive”: Webinars on Performance Optimization – 2017 Edition

June 30, 2017

Register Why Attend Roadmap Instructor Prerequisites Cluster Materials Book In a Nutshell HOW Series “Deep Dive” is a free Web-based training on parallel programming and performance optimization on Intel architecture. The workshop includes 20 hours of instruction and code for hands-on exercises. This training is free to everyone thanks to Intel’s sponsorship. You can access the video recordings of lectures, slides of presentations and code of practical exercises on this page using a free Colfax Research account. To run the hands-on exercises, you will need a multi-core Intel architecture processor and the Intel C++ Compiler. You can get this compiler for 30 days at no cost using an evaluation license for Intel Parallel Studio [...]

MC² Series: Modern Code Contributed Talks

February 10, 2017

In Modern Code Contributed Talks, or MC² Series, experts in computational disciplines share their experience. Register for these ongoing webinars to learn the performance optimization methods used in real-life applications. Would you like to contribute a talk? Contact us. Scholarship is available in the form of access to a diverse collection of powerful computing [...]

Optimizing Torch Performance for Intel Xeon Phi Processors

November 18, 2016

In this 1-hour webinar, Ryo Asai (Colfax) discusses how machine learning applications can benefit from code modernization. He begins by exploring the parallelism that gives modern computer architecture its performance, and how it can be leveraged. Then he applies code modernization techniques live on-screen to the Torch machine learning framework. Specifically, he optimizes image recognition through a deep convolutional neural network that uses the VGG-net architecture. For each code modernization technique, he explains why it works, and how to apply it in practice. What you will learn: What code modernization is, and its importance for machine learning Practical knowledge of modern computer architectures Code modernization techniques for leveraging parallelism Slides: Colfax-Torch-VGG-Webinar.pdf (2 [...]

Machine Learning on 2nd Generation Intel® Xeon Phi™ Processors: Image Captioning with NeuralTalk2, Torch

June 20, 2016

In this case study, we describe a proof-of-concept implementation of a highly optimized machine learning application for Intel Architecture. Our results demonstrate the capabilities of Intel Architecture, particularly the 2nd generation Intel Xeon Phi processors (formerly codenamed Knights Landing), in the machine learning domain. Download as PDF: Colfax-NeuralTalk2-Summary.pdf (814 KB) or read online below. Code: see our branch of NeuralTalk2 for instructions on reproducing our results (in Readme.md). It uses our optimized branch of Torch to run efficiently on Intel architecture. See also: colfaxresearch.com/get-ready-for-intel-knights-landing-3-papers/ 1. Case Study It is common in the machine learning (ML) domain to see applications implemented with the use of frameworks and libraries such as Torch, Caffe, TensorFlow, and similar. This approach allows the computer scientist to focus on the learning algorithm, leaving the details of performance optimization to the framework. Similarly, the ML frameworks usually rely on a third-party library such as Atlas, CuBLAS, [...]