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Architecture and Advantages of SIMD in Multimedia Applications Journal of Xi'an University of Architecture & Technology ISSN No : 1006-7930 Architecture and Advantages of SIMD in Multimedia Applications Sarah M. Al-sudany, Department of Computer Engineering University of Technology, Baghdad-Iraq E-mail: [email protected] Ahmed S. Al-Araji Department of Computer Engineering University of Technology, Baghdad-Iraq E-mail:[email protected] Bassam M. Saeed Department of Computer Engineering University of Technology, Baghdad-Iraq E-mail: [email protected] Abstract— In this paper, we identified the single instruction multi-data architecture (SIMD) that is a method of computing parallelism. Most modern processor designs contain SIMD in order to increase performance of the computer. The aims of this work are to describe the classification of SIMD architecture in computer systems that it depends on implementation when dealing with performance time and to utilize the efficiency SIMD in multicore and multi-processing for able computer system when implementing a program for better performance. This can be achieved by studying the basic principle of SIMD architecture and emphasized two types of SIMD array Processors: array processor and vector processor then identified advantages and disadvantages of these types as well as focusing on the types of SIMD architecture, true and pipelined SIMD. This paper provides an overview of the characteristic multimedia extensions to SIMD then analyze the development in the use of multimedia extensions in the applications that need speeding up processing such as digital signal processing (DSP), image processing and mobile application. Keywords— Array Processing Architecture, SIMD Architecture, Vector Processing Architectur. I. INTRODUCTION The architecture of the microprocessor was usually motivated by higher demands. Several design methods were used to remove numerous forms of applications parallel [1]. Computer engineering has achieved technological developments in recent years which made a great mutual impact, especially in the dissemination of single- instruction multi data (SIMD) for individual education [2]. For example, in the early 1970s, the first use of the SIMD instructions was used with CDC Star 100 and TI ASC machines which were able to perform the same functionality on a batch of data . A new era has begun in the use of SIMD to process the data in parallel with thought machines CM-1 and CM-2 considered as highly parallel processing supercomputers. Many desktop computers are doing many tasks nowadays such as video processing and gaming in real time, with a bundle of digital information. Companies were therefore trying to use this architecture on desktop computers [3]. Sun Microsystems introduced SIMD integer instructions in the 1995 UltraSPARC I microprocessor in a VIS (visual instruction set) extension. MIPS (MIPS Digital Media extension) introduced the MDMX. By adding MMX extensions to the x86 architecture in 1996, Intel made SIMD widely available. Motorola then implemented AltiVec's PowerPC system, which was also used in POWER systems by IBM [4]. This caused the Intel's to react which was SSE. SSE and its extensions are now more used than the others. The purpose of introducing SIMD extensions is that it especially applies to common tasks such as adjusting contrast in a digital image, adjusting digital audio volume, and other audio tools [5]. Most modern central processing unit (CPU) designs including SIMD instructions to improve multimedia usage performance. SIMD capabilities of processors must be taken into account during standardization. For example, a reasonable intermediate calculation accuracy is determined and Volume XII, Issue VI, 2020 Page No: 1452 Journal of Xi'an University of Architecture & Technology ISSN No : 1006-7930 the sample dependencies are done through a clear effort. Parallel computer systems, on different pieces of data execute the same method [6]. The SIMD Accelerator thus only needs interchangeable working modules with a very basic control mechanism. The improved performance and low complexity of SIMD accelerators have contributed to SIMD operation, as its name suggests, through operating with a single direction on several data elements in parallel [7]. Therefore, the first move in having SIMD to be introduced is to encrypt several data elements that can be operated in parallel with vector-compile-time vector instructions. The compilers execute software review in order to identify various scalar three instructions executing the same operation [8]. Many of these instructions are combined into a common instruction named the vector / SIMD instructions, based on the form of instruction and the appearance of the SIMD accelerator [9] Vectorized instructions, and then implemented on the SIMD accelerator. Some of these implementations are described below: The cumulative number of instructions on executing a programme reduces due to single vector instructions for coding different operations. These effects are decreased guidance on specifications for cache capacity. Instead, it improves the access rate to the help instruction cache and improves performance [10]. If there are fewer instructions, it can reduce the amount of execution that the front-end processor needs to perform. It requires receiving guidance for decoding and its lower timetable. The back-end will therefore delete less command. This ensures improved energy quality [11]. Less instruction decreases the amount of work the processor has to do at the front end. The instructions need to be decoded and organised for less instruction. However, there is less back-end instruction. This mechanism leads to the improvement of energy efficiency. Different operations embedded in an order allow for a broad variety of successful operations. It leads to efficient manual scheduling. Therefore, design of the SIMD architecture gives computational design several improvement benefits such as the execuition time performance, scalability of data size, cost saving and provides concurrently. The huge performance benefits can only be seen in the world of graphical applications with a SIMD technique, and at the same time, many problems can be benefited from the SIMD technique. Even when working with consumer applications, being cautious about using memory and CPU can bring enormous benefits to the user experience as you learn more about software engineering. This paper is organized according to the following: Section II, describes the basic principle of SIMD architecture; Section III introduces the implementing of SIMD architecture; Section IV explains the multimedia applications; Conclusions are cited in section V. II. BASIC PRINCIPLES OF SIMD ARCHITECTURE When researching SIMD architecture, it is necessary to know the rules work of Data parallelism. Because of the core work of SIMD component depends on the characteristic of parallelism, it is important to know the SIMD processing, this research will investigate Attached array processors and SIMD array processors, especially knowing the term of vector processing and array processing, and focus on the difference between these architectures in regard to each distinct application. It will search and review each of these topics so that it becomes easy then to enter into the classification of applications that use SIMD architecture. 2.1 Data Parallelism Data parallelism aimed to improve processing speed dependent on data set storage capacity in overlapping computing flows. For examples, the method of consolidation of customer address gathers an address and tries to turn it into a regular type. It function is adaptable to data parallelism and can be optimised by adding eight 32 bit standardisation processes for the addresses by a factor of 8 and streaming a part of information for each case as in Fig. 1. Figure 1. The data parallelism in SIMD. Volume XII, Issue VI, 2020 Page No: 1453 Journal of Xi'an University of Architecture & Technology ISSN No : 1006-7930 This method is a more precise and stronger parallel by continuously applying the same limited range of tasks to several data sources, to increase our output. However, big gains can be made on today's processors if it is possible to know when and how to implement SIMD. As with all performance improvements, we should measure gains on a typical target device before putting it into production. Although some types of architecture use SIMD with minimal code changes, other custom algorithms bring additional complexity, so we should also consider this trade off against expected performance gains [12]. 2.2 SIMD Processing Array processors were called multiprocessors were vector processors for some time; this performs on a broad variety of data and can boost computer efficiency. There are two types of array processors: Attached array processors. SIMD array processors. An attached array processor is a processor that is attached to a general-purpose computer and its function is to boost and enhance the computer's performance in numerical computational operations. It achieves high performance by handling multiple functional units in parallel, therefore, this review interested in the second type of processors. Which is a single computer system that has multiple processors running in parallel. Processing units built to operate under the control of a common control unit, have the duty to provide a single instruction stream in addition to multiple data flows. The diagram below shows a general block of an array processor as in Fig. 2. Figure 2. General block of an array processor. It comprises
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