مشخصات مقاله | |
ترجمه عنوان مقاله | مدلسازی استاتیک واحد پردازش گرافیکی (GPU) با استفاده از اجرای رشته ای موازی (PTX) و یادگیری ساختاری عمیق |
عنوان انگلیسی مقاله | GPU Static Modeling Using PTX and Deep Structured Learning |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 12 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 | |
ایمپکت فاکتور(IF) |
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 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر |
گرایش های مرتبط | معماری سیستم های کامپیوتری |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisbon, Portugal |
کلمات کلیدی | واحد پردازش گرافیکی، مقیاس بندی ولتاژ و فرکانس پویا، مدلسازی، عوامل مقیاس بندی، صرفه جو در مصرف انرژی |
کلمات کلیدی انگلیسی | GPU, DVFS, modeling, scaling-factors, energy savings |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2951218 |
کد محصول | E13968 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract I. Introduction II. Background and Motivation III. PTX-Based Modeling IV. Experimental Results V. Conclusion Authors Figures References |
بخشی از متن مقاله: |
Abstract
In the quest for exascale computing, energy-efficiency is a fundamental goal in highperformance computing systems, typically achieved via dynamic voltage and frequency scaling (DVFS). However, this type of mechanism relies on having accurate methods of predicting the performance and power/energy consumption of such systems. Unlike previous works in the literature, this research focuses on creating novel GPU predictive models that do not require run-time information from the applications. The proposed models, implemented using recurrent neural networks, take into account the sequence of GPU assembly instructions (PTX) and can accurately predict changes in the execution time, power and energy consumption of applications when the frequencies of different GPU domains (core and memory) are scaled. Validated with 24 applications on GPUs from different NVIDIA microarchitectures (Turing, Volta, Pascal and Maxwell), the proposed models attain a significant accuracy. Particularly, the obtained power consumption scaling model provides an average error rate of 7.9% (Tesla T4), 6.7% (Titan V), 5.9% (Titan Xp) and 5.4% (GTX Titan X), which is comparable to state-of-the-art run-time counter-based models. When using the models to select the minimum-energy frequency configuration, significant energy savings can be attained: 8.0% (Tesla T4), 6.0% (Titan V), 29.0% (Titan Xp) and 11.5% (GTX Titan X). Introduction Over the past decade, the high-performance computing (HPC) area has observed a noticeable upsurge in the utilization of accelerators, more specifically graphics processing units (GPUs). The energy efficiency of these devices can have a large impact on the total cost of large-scale computer clusters. As an example, the Summit supercomputer (number one system of June’2019 Top500 list [1]), uses a total of 27 648 NVIDIA Volta GPUs to achieve a peak performance of almost 200 petaflops. For that, it requires a power supply of 13 million watts, which corresponds to an estimated cost of 17 million dollars per year (on power supply alone) [2]. The magnitude of such values highlights the importance of effective mechanisms to maximize the energy efficiency of these systems, as a mere 5% decrease in the energy consumption could generate savings of around 1 million dollars. One example of such mechanisms is the dynamic voltage and frequency scaling (DVFS), which allows placing devices into lower performance/power states. When carefully applied to match the needs of the executing applications, DVFS can lead to significant power and energy savings, sometimes with minimum impact on performance [3], [4]. A recent study showed that using DVFS techniques in GPUs executing deep neural networks applications can provide energy savings up to 23% during training and 26% during inference phases [5]. |