摘要
Designing a system usually acquires lots of instincts and knowledge to harmonize the computing resources under the paradigm of application specific heterogeneous systems. This paper presents a phase-based profiling mechanism to speed up the process of learning how application behaviors perform on the hardware and vice versa. By analyzing program phases, performance information can be gathered in a way that highlights the performance of high-level tasks in an application running on different hardware settings. We evaluated our phase-based profiling framework using QEMU, employing approximate timing models and mechanisms to track functions/events in programs and operating systems of the guest system. Furthermore, by using timing simulations, it is possible to escape the confined boundaries of real-world machine based systems, and to rapidly explore the impact of hardware parameters on the system performance. In our experimental results, phase-based profiling yields useful information of the runtime behaviors and performance of a program, allowing developers to discover program bottlenecks, and predicts the performance of optimization ideas on the software and/or underlying hardware. Our results suggest that incorporating phase profiling with the timing approximate simulator helps to facilitate hardware and software co-design.
| 原文 | English |
|---|---|
| 主出版物標題 | Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 |
| 發行者 | Institute of Electrical and Electronics Engineers Inc. |
| 頁面 | 101-110 |
| 頁數 | 10 |
| ISBN（電子） | 9781538677599 |
| DOIs | |
| 出版狀態 | Published - 2019 一月 9 |
| 事件 | 24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 - Hakodate, Japan 持續時間: 2018 八月 29 → 2018 八月 31 |
出版系列
| 名字 | Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 |
|---|
Conference
| Conference | 24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 |
|---|---|
| 國家 | Japan |
| 城市 | Hakodate |
| 期間 | 18-08-29 → 18-08-31 |
指紋
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
引用此文
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Phase-based profiling and performance prediction with timing approximate simulators. / Yeh, Chih Wei; Tu, Chia Heng; Liang, Yi Chuan; Hung, Shih Hao.
Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 101-110 8607239 (Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018).研究成果: Conference contribution
TY - GEN
T1 - Phase-based profiling and performance prediction with timing approximate simulators
AU - Yeh, Chih Wei
AU - Tu, Chia Heng
AU - Liang, Yi Chuan
AU - Hung, Shih Hao
PY - 2019/1/9
Y1 - 2019/1/9
N2 - Designing a system usually acquires lots of instincts and knowledge to harmonize the computing resources under the paradigm of application specific heterogeneous systems. This paper presents a phase-based profiling mechanism to speed up the process of learning how application behaviors perform on the hardware and vice versa. By analyzing program phases, performance information can be gathered in a way that highlights the performance of high-level tasks in an application running on different hardware settings. We evaluated our phase-based profiling framework using QEMU, employing approximate timing models and mechanisms to track functions/events in programs and operating systems of the guest system. Furthermore, by using timing simulations, it is possible to escape the confined boundaries of real-world machine based systems, and to rapidly explore the impact of hardware parameters on the system performance. In our experimental results, phase-based profiling yields useful information of the runtime behaviors and performance of a program, allowing developers to discover program bottlenecks, and predicts the performance of optimization ideas on the software and/or underlying hardware. Our results suggest that incorporating phase profiling with the timing approximate simulator helps to facilitate hardware and software co-design.
AB - Designing a system usually acquires lots of instincts and knowledge to harmonize the computing resources under the paradigm of application specific heterogeneous systems. This paper presents a phase-based profiling mechanism to speed up the process of learning how application behaviors perform on the hardware and vice versa. By analyzing program phases, performance information can be gathered in a way that highlights the performance of high-level tasks in an application running on different hardware settings. We evaluated our phase-based profiling framework using QEMU, employing approximate timing models and mechanisms to track functions/events in programs and operating systems of the guest system. Furthermore, by using timing simulations, it is possible to escape the confined boundaries of real-world machine based systems, and to rapidly explore the impact of hardware parameters on the system performance. In our experimental results, phase-based profiling yields useful information of the runtime behaviors and performance of a program, allowing developers to discover program bottlenecks, and predicts the performance of optimization ideas on the software and/or underlying hardware. Our results suggest that incorporating phase profiling with the timing approximate simulator helps to facilitate hardware and software co-design.
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UR - http://www.scopus.com/inward/citedby.url?scp=85061809410&partnerID=8YFLogxK
U2 - 10.1109/RTCSA.2018.00021
DO - 10.1109/RTCSA.2018.00021
M3 - Conference contribution
AN - SCOPUS:85061809410
T3 - Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
SP - 101
EP - 110
BT - Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -