Colfax now offers a 2-hour Hands-On Workshop (HOW) video on the best practices for performance optimization for Intel Xeon Phi processor (formerly Knights Landing). Use links below the video to navigate the 10 episodes.

Data scientists and deep and machine learning researchers rely on frameworks and libraries such as Torch, Caffe, TensorFlow, and Theano. Studies by Colfax Research and Kyoto University have found that existing open source packages such as Torch and Theano deliver significantly faster performance through the use of Intel Scalable System Framework (Intel SSF) technologies like the Intel compiler and performance libraries for Intel Math Kernel Library (Intel MKL), Intel MPI (Message Passing Interface), and Intel Threading Building Blocks (Intel TBB), and Intel Distribution for Python (Intel Python). Andrey Vladimirov (Head of HPC Research, Colfax Research) noted that "new Intel SSF hardware and software in combination with code modernization delivered an observed 50x machine learning performance improvement in our case study". In the Colfax Research and Kyoto case studies as well as general Python scientific computing benchmarks, results run up to two orders of magnitude (100x) faster as a result of using Intel SSF technologies. Python is a powerful and popular scripting language that provides fast and fundamental tools for machine learning and scientific computing through popular packages such as scikit-learn, NumPy and SciPy.

Colfax International, a Silicon Valley company with 28 years of experience in high-end computing systems, is growing its research team by opening a full-time position of Research Engineer in Machine Learning based in Sunnyvale, California, USA. We are looking for professionals with passion for parallel computing with emphasis on machine learning tasks to help us expand our activity in research, education and consulting on modern computing technologies. Colfax International is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, veteran status, or any other category protected by law.

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) -- this file is available only to registered users. Register or Log In. or read online below. 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.