Use of SIMD Vector Operations to Accelerate Application Code Performance on Low-Powered ARM and Intel Platforms
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An Emerging Architecture in Smart Phones
International Journal of Electronic Engineering and Computer Science Vol. 3, No. 2, 2018, pp. 29-38 http://www.aiscience.org/journal/ijeecs ARM Processor Architecture: An Emerging Architecture in Smart Phones Naseer Ahmad, Muhammad Waqas Boota * Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan Abstract ARM is a 32-bit RISC processor architecture. It is develop and licenses by British company ARM holdings. ARM holding does not manufacture and sell the CPU devices. ARM holding only licenses the processor architecture to interested parties. There are two main types of licences implementation licenses and architecture licenses. ARM processors have a unique combination of feature such as ARM core is very simple as compare to general purpose processors. ARM chip has several peripheral controller, a digital signal processor and ARM core. ARM processor consumes less power but provide the high performance. Now a day, ARM Cortex series is very popular in Smartphone devices. We will also see the important characteristics of cortex series. We discuss the ARM processor and system on a chip (SOC) which includes the Qualcomm, Snapdragon, nVidia Tegra, and Apple system on chips. In this paper, we discuss the features of ARM processor and Intel atom processor and see which processor is best. Finally, we will discuss the future of ARM processor in Smartphone devices. Keywords RISC, ISA, ARM Core, System on a Chip (SoC) Received: May 6, 2018 / Accepted: June 15, 2018 / Published online: July 26, 2018 @ 2018 The Authors. Published by American Institute of Science. This Open Access article is under the CC BY license. -
SECOND AMENDED COMPLAINT 3:14-Cv-582-JD
Case 3:14-cv-00582-JD Document 51 Filed 11/10/14 Page 1 of 19 1 EDUARDO G. ROY (Bar No. 146316) DANIEL C. QUINTERO (Bar No. 196492) 2 JOHN R. HURLEY (Bar No. 203641) PROMETHEUS PARTNERS L.L.P. 3 220 Montgomery Street Suite 1094 San Francisco, CA 94104 4 Telephone: 415.527.0255 5 Attorneys for Plaintiff 6 DANIEL NORCIA 7 UNITED STATES DISTIRCT COURT 8 NORTHERN DISTRICT OF CALIFORNIA 9 DANIEL NORCIA, on his own behalf and on Case No.: 3:14-cv-582-JD 10 behalf of all others similarly situated, SECOND AMENDED CLASS ACTION 11 Plaintiffs, COMPLAINT FOR: 12 v. 1. VIOLATION OF CALIFORNIA CONSUMERS LEGAL REMEDIES 13 SAMSUNG TELECOMMUNICATIONS ACT, CIVIL CODE §1750, et seq. AMERICA, LLC, a New York Corporation, and 2. UNLAWFUL AND UNFAIR 14 SAMSUNG ELECTRONICS AMERICA, INC., BUSINESS PRACTICES, a New Jersey Corporation, CALIFORNIA BUS. & PROF. CODE 15 §17200, et seq. Defendants. 3. FALSE ADVERTISING, 16 CALIFORNIA BUS. & PROF. CODE §17500, et seq. 17 4. FRAUD 18 JURY TRIAL DEMANDED 19 20 21 22 23 24 25 26 27 28 1 SECOND AMENDED COMPLAINT 3:14-cv-582-JD Case 3:14-cv-00582-JD Document 51 Filed 11/10/14 Page 2 of 19 1 Plaintiff DANIEL NORCIA, having not previously amended as a matter of course pursuant to 2 Fed.R.Civ.P. 15(a)(1)(B), hereby exercises that right by amending within 21 days of service of 3 Defendants’ Motion to Dismiss filed October 20, 2014 (ECF 45). 4 Individually and on behalf of all others similarly situated, Daniel Norcia complains and alleges, 5 by and through his attorneys, upon personal knowledge and information and belief, as follows: 6 NATURE OF THE ACTION 7 1. -
CS 110 Discussion 15 Programming with SIMD Intrinsics
CS 110 Discussion 15 Programming with SIMD Intrinsics Yanjie Song School of Information Science and Technology May 7, 2020 Yanjie Song (S.I.S.T.) CS 110 Discussion 15 2020.05.07 1 / 21 Table of Contents 1 Introduction on Intrinsics 2 Compiler and SIMD Intrinsics 3 Intel(R) SDE 4 Application: Horizontal sum in vector Yanjie Song (S.I.S.T.) CS 110 Discussion 15 2020.05.07 2 / 21 Table of Contents 1 Introduction on Intrinsics 2 Compiler and SIMD Intrinsics 3 Intel(R) SDE 4 Application: Horizontal sum in vector Yanjie Song (S.I.S.T.) CS 110 Discussion 15 2020.05.07 3 / 21 Introduction on Intrinsics Definition In computer software, in compiler theory, an intrinsic function (or builtin function) is a function (subroutine) available for use in a given programming language whose implementation is handled specially by the compiler. Yanjie Song (S.I.S.T.) CS 110 Discussion 15 2020.05.07 4 / 21 Intrinsics in C/C++ Compilers for C and C++, of Microsoft, Intel, and the GNU Compiler Collection (GCC) implement intrinsics that map directly to the x86 single instruction, multiple data (SIMD) instructions (MMX, Streaming SIMD Extensions (SSE), SSE2, SSE3, SSSE3, SSE4). Yanjie Song (S.I.S.T.) CS 110 Discussion 15 2020.05.07 5 / 21 x86 SIMD instruction set extensions MMX (1996, 64 bits) 3DNow! (1998) Streaming SIMD Extensions (SSE, 1999, 128 bits) SSE2 (2001) SSE3 (2004) SSSE3 (2006) SSE4 (2006) Advanced Vector eXtensions (AVX, 2008, 256 bits) AVX2 (2013) F16C (2009) XOP (2009) FMA FMA4 (2011) FMA3 (2012) AVX-512 (2015, 512 bits) Yanjie Song (S.I.S.T.) CS 110 Discussion 15 2020.05.07 6 / 21 SIMD extensions in other ISAs There are SIMD instructions for other ISAs as well, e.g. -
Three Ways of Seeing Improved Health and Productivity
Three ways of seeing Key Features Galaxy Watch3 improved health and The Galaxy Watch3 is a premium solution that’s B2B-ready, with days of power and a rotating bezel that allows easy productivity. navigation even while wearing gloves. • Onboard GPS, motion, activity and heart-rate sensors • Battery lasts up to 56 hours (45mm model)2 • Carrier-agnostic LTE3 Take a look at the Samsung Galaxy • Tested to MIL-STD-810G standards,4 IP685, rated at 5 ATM Watch3, Galaxy Watch Active2, and Galaxy Watch Active. Galaxy Watch Active2 The premium Galaxy Watch3, the versatile Galaxy Watch Active, With a focus on wellness, the Galaxy Watch Active2 features and the health-oriented Galaxy Watch Active2 offer greater a digital touch bezel plus advanced sensors that enable health and productivity to virtually any enterprise. They’re more accurate blood pressure tracking, ECG tracking, 1 protected by Samsung Knox . And they’re all customizable to heart rate tracking, alerts, and fall detection. incorporate your company’s branding. Be more nimble. Be • Advanced sensors include heart rate tracker, ECG sensor, and 32G high more productive. Samsung Galaxy watches make it possible. sampling rate accelerometer and gyro • Battery lasts up to 60 hours (44mm model)2 • Carrier-agnostic LTE3 • Tested to MIL-STD-810G standards,4 IP685, rated at 5 ATM Galaxy Watch Active The Galaxy Watch Active offers secure communications in fast-paced environments, and supports corporate efficiency, productivity, health, and safety initiatives. • Advanced sleep tracking helps improve stress levels and sleep patterns • Battery lasts up to 45 hours2 • Tested to MIL-STD-810G standards,4 IP685, rated at 5 ATM Contact Us: samsung.com/wearablesforbiz Galaxy Watch3 Galaxy Watch Active2 Galaxy Watch Active “1.77”” x 1.82”” x 0.44”” (45.0 x 46.2 x 11.1 mm) 1.73" x 1.73" x 0.43" (44 x 44 x 10.9mm) Dimensions 1.56” x 1.56” x 0.41” (39.5 x 39.5 x 10.5mm) 1.61”” x 1.67”” x 0.44”” (41.0 x 42.5 x 11.3 mm)” 1.57" x 1.73" x 0.43" (40 x 40 x 10.9mm) Physical Weight 1.90 oz (53.8 g) /1.70 oz (48.2g) 1.7 oz. -
PGI Compilers
USER'S GUIDE FOR X86-64 CPUS Version 2019 TABLE OF CONTENTS Preface............................................................................................................ xii Audience Description......................................................................................... xii Compatibility and Conformance to Standards............................................................xii Organization................................................................................................... xiii Hardware and Software Constraints.......................................................................xiv Conventions.................................................................................................... xiv Terms............................................................................................................ xv Related Publications.........................................................................................xvii Chapter 1. Getting Started.....................................................................................1 1.1. Overview................................................................................................... 1 1.2. Creating an Example..................................................................................... 2 1.3. Invoking the Command-level PGI Compilers......................................................... 2 1.3.1. Command-line Syntax...............................................................................2 1.3.2. Command-line Options............................................................................ -
Intel Hardware Intrinsics in .NET Core
Han Lee, Intel Corporation [email protected] Notices and Disclaimers No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade. This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the OEM or retailer. The products and services described may contain defects or errors known as errata which may cause deviations from published specifications. Current characterized errata are available on request. No product or component can be absolutely secure. Copies of documents which have an order number and are referenced in this document may be obtained by calling 1-800-548- 4725 or by visiting www.intel.com/design/literature.htm. Intel, the Intel logo, and other Intel product and solution names in this presentation are trademarks of Intel *Other names and brands may be claimed as the property of others © Intel Corporation. 2 What Do These Have in Common? Domain Example Image processing Color extraction High performance computing (HPC) Matrix multiplication Data processing Hamming code Text processing UTF-8 conversion Data structures Bit array Machine learning Classification For performance sensitive code, consider using Intel® hardware intrinsics 3 Objectives . -
Survey and Benchmarking of Machine Learning Accelerators
1 Survey and Benchmarking of Machine Learning Accelerators Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, and Jeremy Kepner MIT Lincoln Laboratory Supercomputing Center Lexington, MA, USA freuther,pmichaleas,michael.jones,vijayg,sid,[email protected] Abstract—Advances in multicore processors and accelerators components play a major role in the success or failure of an have opened the flood gates to greater exploration and application AI system. of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore’s Law, have prompted an explosion of processors and accelerators that promise even greater computational and ma- chine learning capabilities. These processors and accelerators are coming in many forms, from CPUs and GPUs to ASICs, FPGAs, and dataflow accelerators. This paper surveys the current state of these processors and accelerators that have been publicly announced with performance and power consumption numbers. The performance and power values are plotted on a scatter graph and a number of dimensions and observations from the trends on this plot are discussed and analyzed. For instance, there are interesting trends in the plot regarding power consumption, numerical precision, and inference versus training. We then select and benchmark two commercially- available low size, weight, and power (SWaP) accelerators as these processors are the most interesting for embedded and Fig. 1. Canonical AI architecture consists of sensors, data conditioning, mobile machine learning inference applications that are most algorithms, modern computing, robust AI, human-machine teaming, and users (missions). Each step is critical in developing end-to-end AI applications and applicable to the DoD and other SWaP constrained users. -
2016 Comparison of Application Processor (AP) Packaging 1 Table of Contents
Application Processor for Smartphone Packaging Technology & Cost Review Advanced Packaging report by Stéphane ELISABETH November 2016 21 rue la Noue Bras de Fer 44200 NANTES - FRANCE +33 2 40 18 09 16 [email protected] www.systemplus.fr ©2016 System Plus Consulting | 2016 Comparison of Application Processor (AP) packaging 1 Table of Contents Overview / Introduction 3 Manufacturing Process Flow 73 o Executive Summary o Global Overview o AP I/O count & Footprint o Package Fabrication Unit o AP I/O count & L/S width Cost Analysis 76 o Reverse Costing Methodology o Synthesis of the cost analysis Company Profile 10 o Main Steps of Economic Analysis o Apple, Huawei, Samsung, Qualcomm o Yields Explanation & Hypotheses o Apple, Huawei, Samsung, Qualcomm AP history o Die cost 81 o Apple, Huawei, Samsung, Qualcomm Supply Chain History Front-End Cost o iPhone 7 Plus, Huawei P9, Samsung Galaxy S7 Teardown Wafer & Die Cost o PoP Packaging Technology o Package Manufacturing Cost 83 Physical Analysis 32 A10 Package Cost Breakdown o Synthesis of the Physical Analysis Kirin 955 Package Cost Breakdown o Physical Analysis Methodology Exynos 8 Package Cost Breakdown o A10, Kirin 955, Exynos 8, Snapdragon 820 Package 34 Snapdragon 820 Package Cost Breakdown Package Views & Dimensions o Component Cost 88 Wafer/Panel & Component Cost Package Opening Component Cost Breakdown Package Opening Comparison o PoP Cross-Section 47 Company services 91 Package Details Cross-Section Package Cross-Section Comparison o Die & Land-Side Decoupling Capacitor Comparison 66 Die View & Dimensions LSC Capacitor View & Dimensions LSC Capacitor Footprint Embedded LSC Capacitor Cross-Section Soldered LSC Capacitor Cross-Section o Summary of the physical Data 72 ©2016 System Plus Consulting | 2016 Comparison of Application Processor (AP) packaging 2 Executive Summary Overview / Introduction o Executive Summary • Located under the DRAM chip on the main board, the application processors (AP) are packaged using PoP o Reverse Costing Methodology technology. -
Optimizing Subroutines in Assembly Language an Optimization Guide for X86 Platforms
2. Optimizing subroutines in assembly language An optimization guide for x86 platforms By Agner Fog. Copenhagen University College of Engineering. Copyright © 1996 - 2012. Last updated 2012-02-29. Contents 1 Introduction ....................................................................................................................... 4 1.1 Reasons for using assembly code .............................................................................. 5 1.2 Reasons for not using assembly code ........................................................................ 5 1.3 Microprocessors covered by this manual .................................................................... 6 1.4 Operating systems covered by this manual................................................................. 7 2 Before you start................................................................................................................. 7 2.1 Things to decide before you start programming .......................................................... 7 2.2 Make a test strategy.................................................................................................... 9 2.3 Common coding pitfalls............................................................................................. 10 3 The basics of assembly coding........................................................................................ 12 3.1 Assemblers available ................................................................................................ 12 3.2 Register set -
Android Vs Ios
Android vs iOS By Mohammad Daraghmeh Jack DeGonzaque AGENDA ● Android ○ History ● Samsung S6 ○ System Architecture ○ Processor ○ Performance Metrics ● iOS ○ History ● iPhone 6 ○ System Architecture ○ Processor ○ Performance Metrics ● Samsung S6 vs iPhone 6 Android Android History ● The Android OS was created mainly by three amazing people Andy Rubin, Rich Miner, Nick Sears, and Chris White. ○ Initial development for the OS was to create an operating system for digital cameras and PC integration. ○ After gauging the size of the market for such a product, Rubin and his colleagues decided to target the booming smartphone market. ● In 2005, Google also wanted to venture into the smartphone market and did so by acquiring Android Inc. ○ The primary directive was to develop technologies that are developed and distributed at a significantly lower cost to make it more accessible. ○ In 2008, the first Android running smartphone, the HTC Dream, was released. Android History (Cont.) ● The Android operating system has become one of the most popular operating systems. ○ According to research firm, called Gartner, more than a billion Android devices were sold in 2014, which is roughly five times more than Apple iOS devices sold and three times more Windows machines sold. ● Their attribute to success stems from the fact that Google does not charge for Android, and that most phone manufacturers are making cost effective phones, which results in affordable smartphones and internet services at low costs for consumers to enjoy. SAMSUNG GALAXY S6 System Architecture ● Samsung S6 uses the Exynos 7420 processor, which is developed by Samsung as well. ○ The Exynos 7420 is a 78 mm^2 SoC comprised of 8 cores connected to two L2 cache instances. -
Digital Forensic Analysis of Smart Watches
TALLINN UNIVERSITY OF TECHNOLOGY School of Information Technologies Kehinde Omotola Adebayo (174449IVSB) DIGITAL FORENSIC ANALYSIS OF SMART WATCHES Bachelor’s Thesis Supervisor: Hayretdin Bahsi Research Professor Tallinn 2020 TALLINNA TEHNIKAÜLIKOOL Infotehnoloogia teaduskond Kehinde Omotola Adebayo (174449IVSB) NUTIKELLADE DIGITAALKRIMINALISTIKA Bachelor’s Thesis Juhendaja: Hayretdin Bahsi Research Professor Tallinn 2020 Author’s declaration of originality I hereby certify that I am the sole author of this thesis. All the used materials, references to the literature and the work of others have been referred to. This thesis has not been presented for examination anywhere else. Author: Kehinde Omotola Adebayo 30.04.2020 3 Abstract As wearable technology is becoming increasingly popular amongst consumers and projected to continue to increase in popularity they become probable significant source of digital evidence. One category of wearable technology is smart watches and they provide capabilities to receive instant messaging, SMS, email notifications, answering of calls, internet browsing, fitness tracking etc. which can be a great source of digital artefacts. The aim of this thesis is to analyze Samsung Gear S3 Frontier and Fitbit Versa Smartwatches, after which we present findings alongside the limitations encountered. Our result shows that we can recover significant artefacts from the Samsung Gear S3 Frontier, also more data can be recovered from Samsung Gear S3 Frontier than the accompanying mobile phone. We recovered significant data that can serve as digital evidence, we also provided a mapping that would enable investigators and forensic examiners work faster as they are shown where to look for information in the course of an investigation. We also presented the result of investigating Fitbit Versa significant artefacts like Heart rate, sleep, exercise and personal data like age, weight and height of the user of the device, this shows this device contains artefacts that might prove useful for forensic investigators and examiners. -
Automatic SIMD Vectorization of Fast Fourier Transforms for the Larrabee and AVX Instruction Sets
Automatic SIMD Vectorization of Fast Fourier Transforms for the Larrabee and AVX Instruction Sets Daniel S. McFarlin Volodymyr Arbatov Franz Franchetti Department of Electrical and Department of Electrical and Department of Electrical and Computer Engineering Computer Engineering Computer Engineering Carnegie Mellon University Carnegie Mellon University Carnegie Mellon University Pittsburgh, PA USA 15213 Pittsburgh, PA USA 15213 Pittsburgh, PA USA 15213 [email protected] [email protected] [email protected] Markus Püschel Department of Computer Science ETH Zurich 8092 Zurich, Switzerland [email protected] ABSTRACT General Terms The well-known shift to parallelism in CPUs is often associated Performance with multicores. However another trend is equally salient: the increasing parallelism in per-core single-instruction multiple-date Keywords (SIMD) vector units. Intel’s SSE and IBM’s VMX (compatible to Autovectorization, super-optimization, SIMD, program generation, AltiVec) both offer 4-way (single precision) floating point, but the Fourier transform recent Intel instruction sets AVX and Larrabee (LRB) offer 8-way and 16-way, respectively. Compilation and optimization for vector extensions is hard, and often the achievable speed-up by using vec- 1. Introduction torizing compilers is small compared to hand-optimization using Power and area constraints are increasingly dictating microar- intrinsic function interfaces. Unfortunately, the complexity of these chitectural developments in the commodity and high-performance intrinsics interfaces increases considerably with the vector length, (HPC) CPU space. Consequently, the once dominant approach of making hand-optimization a nightmare. In this paper, we present a dynamically extracting instruction-level parallelism (ILP) through peephole-based vectorization system that takes as input the vector monolithic out-of-order microarchitectures is being supplanted by instruction semantics and outputs a library of basic data reorgani- designs with simpler, replicable architectural features.