مصرف انرژی در بردارهای عدد صحیح متراکم
ترجمه نشده

مصرف انرژی در بردارهای عدد صحیح متراکم

عنوان فارسی مقاله: مصرف انرژی در بردارهای عدد صحیح متراکم: مطالعه موردی
عنوان انگلیسی مقاله: Energy Consumption in Compact Integer Vectors: A Study Case
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: 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
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2949655
دانشگاه: Department of Computer Science, Universidad de Chile, Santiago 837-0459, Chile
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13917
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Background


III. Study Case: Compressed Integer Vectors


IV. Experimental Evaluation


V. Conclusion and Future Work


Authors


Figures


References

نمونه متن انگلیسی 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.

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

مصرف انرژی در بردارهای عدد صحیح متراکم
نوشته های مرتبط
مقالات جدید
نماد اعتماد الکترونیکی
پیوندها