مقاله انگلیسی رایگان در مورد مصرف انرژی در بردارهای عدد صحیح متراکم – IEEE 2019

مقاله انگلیسی رایگان در مورد مصرف انرژی در بردارهای عدد صحیح متراکم – IEEE 2019

 

مشخصات مقاله
ترجمه عنوان مقاله مصرف انرژی در بردارهای عدد صحیح متراکم: مطالعه موردی
عنوان انگلیسی مقاله Energy Consumption in Compact Integer Vectors: A Study Case
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۲ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۶۴۱ در سال ۲۰۱۸
شاخص H_index ۵۶ در سال ۲۰۱۹
شاخص SJR ۰٫۶۰۹ در سال ۲۰۱۸
شناسه ISSN ۲۱۶۹-۳۵۳۶
شاخص Quartile (چارک) Q2 در سال ۲۰۱۸
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط مهندسی الگوریتم و محاسبات
نوع ارائه مقاله
ژورنال
مجله / کنفرانس دسترسی – IEEE Access
دانشگاه  Department of Computer Science, Universidad de Chile, Santiago 837-0459, Chile
کلمات کلیدی الگوریتم ها، ساختارهای داده متراکم، فشرده سازی داده، مصرف انرژی، بردارهای عدد صحیح
کلمات کلیدی انگلیسی  Algorithms, compact data structures, data compression, energy consumption, integer vectors
شناسه دیجیتال – doi
https://doi.org/10.1109/ACCESS.2019.2949655
کد محصول  E13917
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I. Introduction
II. Background
III. Study Case: Compressed Integer Vectors
IV. Experimental Evaluation
V. Conclusion and Future Work
Authors
Figures
References

 

بخشی از متن مقاله:
Abstract

In the field of algorithms and data structures analysis and design, most of the researchers focus only on the space/time trade-off, and little attention has been paid to energy consumption. Moreover, most of the efforts in the field of Green Computing have been devoted to hardware-related issues, being green software in its infancy. Optimizing the usage of computing resources, minimizing power consumption or increasing battery life are some of the goals of this field of research. As an attempt to address the most recent sustainability challenges, we must incorporate the energy consumption as a first-class constraint when designing new compact data structures. Thus, as a preliminary work to reach that goal, we first need to understand the factors that impact on the energy consumption and their relation with compression. In this work, we study the energy consumption required by several integer vector representations. We execute typical operations over datasets of different nature. We can see that, as commonly believed, energy consumption is highly related to the time required by the process, but not always. We analyze other parameters, such as number of instructions, number of CPU cycles, memory loads, among others.

Introduction

We are surrounded by digital information, such as the huge amount of data generated on the Internet and also that we are collecting in our daily lives: human generated data, both consciously (such as emails, tweets, pictures, voice) and unconsciously (clicks, likes, follows, logs, . . . ), or observed data (biological, astronomical, etc.). When managing large volumes of digital information, data compression has always been considered to be vitally important. Traditionally, data compression focused on obtaining the smallest representation possible, in order to save space and transmission time, thus, providing a good archival method. However, most of the compression techniques require decompressing the data when they need to be accessed, especially when these accesses are not sequential, and thus, limiting the applicability of data compression. To overcome these issues, compact data structures appeared in the 1990’s and rapidly evolved during early years of the current century [1]. They use compression strategies to reduce the size of the stored data, taking advantage of the patterns existing in the data, but with a key difference: data can be directly managed and queried in compressed form, without requiring prior decompression. The main contribution is that they allow larger datasets fit in faster levels of the memory hierarchy than classical representations, thus, dramatically improving processing times. In addition, many compact data structures are equipped with additional information that, within the same compressed space, acts as index and speeds up queries.

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