xeon phi

Capabilities of Intel® AVX-512 in Intel® Xeon® Scalable Processors (Skylake)

September 19, 2017

This paper reviews the Intel® Advanced Vector Extensions 512 (Intel® AVX-512) instruction set and answers two critical questions: How do Intel® Xeon® Scalable processors based on the Skylake architecture (2017) compare to their predecessors based on Broadwell due to AVX-512? How are Intel Xeon processors based on Skylake different from their alternative, Intel® Xeon Phi™ processors with the Knights Landing architecture, which also feature AVX-512? We address these questions from the programmer’s perspective by demonstrating C language code of microkernels benefitting from AVX-512. For each example, we dig deeper and analyze the compilation practices, resultant assembly, and optimization reports. In addition to code studies, the paper contains performance measurements for a synthetic benchmark with guidelines on estimating peak performance. In conclusion, we outline the workloads and application domains that can benefit from the new features of AVX-512 instructions. Colfax-SKL-AVX512-Guide.pdf (524 KB) Table of Contents 1. Intel Advanced Vector Extensions 512 [...]

FALCON Library: Fast Image Convolution in Neural Networks on Intel Architecture

November 9, 2016

We describe FALCON, an original open-source implementation of image convolution with a 3×3 filter based on Winograd’s minimal filtering algorithm. Compared to direct convolution, Winograd’s algorithm reduces the number of arithmetic operations at the cost of complicating the memory access pattern. This study is carried out in the context of image analysis in convolutional neural networks. Our implementation combines C language code with BLAS function calls for general matrix-matrix multiplication. The code is optimized for Intel Xeon Phi processors x200 (formerly Knights Landing) with Intel Math Kernel Library (MKL) used for BLAS call to the SGEMM function. To test the performance of FALCON in the context of machine learning, we benchmarked it for a set of image and filter sizes corresponding to the VGG Net architecture. In this test, FALCON achieves 10% greater overall performance than convolution from DNN primitives in Intel MKL. However, for some layers, FALCON is faster than MKL by 1.5x, but for other layers slower by as much as 4x. This indicates a possibility of a [...]

Performance Optimization for Intel® Xeon Phi™ x200 Product Family: Video

September 29, 2016

Optimization for Intel Xeon Phi Processors x200 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. Slides: HOW-Knights-Landing.pdf (4 MB) Part 1. Meet Intel Xeon Phi processors Purpose of Intel Xeon Phi processors and their organization from the programmer’s point of view. Episode 01. ► Intel architecture: today and tomorrow (14 min) Episode 02. ► Cores in Intel Xeon Phi processors (7 min) Episode 03. ► Vector Instruction Support (14 min) Episode 04. ► High-bandwidth memory (8 min) Episode 05. ► Clustering modes (9 min) Part 2. Hands-on Demonstrations Exercises in performance optimization for Intel Xeon Phi processors. Episode 06. ► Memory bandwidth optimization (19 min) (bonus: ► with memkind) (9 min) Episode 07. ► Vectorization with AVX-512 (13 min) (bonus: ► threading) (9 min) Episode 08. ► Tuning with Intel Math Kernel Library (MKL) (20 min) Episode 09. [...]

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, [...]

Intel® Python* on 2nd Generation Intel® Xeon Phi™ Processors: Out-of-the-Box Performance

June 20, 2016

This paper reports on the value and performance for computational applications of the Intel® distribution for Python* 2017 Beta on 2nd generation Intel® Xeon Phi™ processors (formerly codenamed Knights Landing). Benchmarks of LU decomposition, Cholesky decomposition, singular value decomposition and double precision general matrix-matrix multiplication routines in the SciPy and NumPy libraries are presented, and tuning methodology for use with high-bandwidth memory (HBM) is laid out. Download as PDF: Colfax-Intel-Python.pdf (1 MB) or read online below. Code: coming soon, check back later. See also: colfaxresearch.com/get-ready-for-intel-knights-landing-3-papers/ 1. A Case for Python in Computing Python is a popular scripting language in computational applications. Empowered with the fundamental tools for scientific computing, NumPy and SciPy libraries, Python applications can express in brief and convenient form basic linear algebra subroutines (BLAS) and linear algebra package (LAPACK) functions for operations on matrices and systems of linear algebraic [...]

Knights Landing Webinar Slides Translated to Japanese

May 13, 2016

日XLsoft社の協力で、弊社の “Introduction to Next-Generation Intel® Xeon Phi™ Processor: Developer’s Guide to Knights Landing” で使われているスライド集が日本語に翻訳されました。 With the help of our partners at XLsoft, the slide deck for the webinar “Introduction to Next-Generation Intel® Xeon Phi™ Processor: Developer’s Guide to Knights Landing” has been translated to the Japanese language. XLsoft社のウェブサイト/XLsoft website Download here: JP-Colfax-Programmers-Guide-to-KNL.pdf (5 MB) For more information, and to register for the webinar, please visit: Webinar [...]

MCDRAM as High-Bandwidth Memory (HBM) in Knights Landing Processors: Developer’s Guide

May 11, 2016

This publication is part of a developer guide focusing on the new features in 2nd generation Intel® Xeon Phi™ processors code-named Knights Landing (KNL). In this document we discuss the on-package high-bandwidth memory (HBM) based on the multi-channel dynamic random access memory (MCDRAM) technology: Three configuration modes of HBM: Flat mode, Cache mode and Hybrid mode Utilization of the HBM as addressable memory using two methods: by setting affinity policy with the numactl tool and through the usage of special allocators in the memkind library Guidelines for determining the optimal usage model for applications running on bootable Knights Landing. Colfax_KNL_MCDRAM_Guide.pdf (255 KB) See also: colfaxresearch.com/get-ready-for-intel-knights-landing-3-papers/ Table of Contents 1. MCDRAM in KNL 2.1. Cache Mode 2.2. Flat Mode 2.3. Hybrid Mode 3. Using HBM as addressable memory 3.1. numactl 3.2. Memkind Library 3.3. Fortran 4. Choosing Memory and Programming Model 4.1. Programming with HBM… 4.2. …and Programming without HBM Appendix A: Application Memory [...]

Guide to Automatic Vectorization with Intel AVX-512 Instructions in Knights Landing Processors

May 11, 2016

This publication is part of a developer guide focusing on the new features in 2nd generation Intel® Xeon Phi™processors code-named Knights Landing (KNL). In this document, we focus on the new vector instruction set introduced in Knights Landing processors, Intel® Advanced Vector Extensions 512 (Intel® AVX-512). The discussion includes: Introduction to vector instructions in general, The structure and specifics of AVX-512, and Practical usage tips: checking if a processor has support for various features, compilation process and compiler arguments, and pros and cons of explicit and automatic vectorization using the Intel® C++ Compiler and the GNU Compiler Collection. Colfax_KNL_AVX512_Guide.pdf () See also: colfaxresearch.com/get-ready-for-intel-knights-landing-3-papers/ Table of Contents 1. Vector Instructions 2. Structure and Functionality of AVX-512 2.1. Subsets 2.2. AVX512-F 2.3. AVX512-CD 2.4. AVX512-ER 2.5. AVX512-PF 3. Feature Check 3.1. Command Line 3.2. Source Code 4. Compiling 4.1 Usage Models 4.2. Intel C++ Compiler 4.3. The GNU [...]

Auto-Vectorization with the Intel Compilers: is Your Code Ready for Sandy Bridge and Knights Corner?

March 12, 2012

One of the features of Intel’s Sandy Bridge-E processor released this month is the support for the Advanced Vector Extensions (AVX) instruction set. Codes suitable for efficient auto-vectorization by the compiler will be able to take advantage of AVX without any code modification, with only re-compilation. This paper explains the guidelines for code design suitable for auto-vectorization by the compiler (elimination of vector dependence, implementation of unit-stride data access and proper address alignment) and walks the reader through a practical example of code development with auto-vectorization. The resulting code is compiled and executed on two computer systems: a Westmere CPU-based system with SSE 4.2 support, and a Sandy Bridge-based system with AVX support. The benefit of vectorization is more significant in the AVX version, if the code is designed efficiently. An ‘elegant’, but inefficient solution is also provided and discussed. In addition, the paper provides a comparative benchmark of the Sandy Bridge and Westmere systems, based on the discussed [...]