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
N1 - Funding Information:
This work was financially supported by Ministry of Science and Technology of Taiwan under grants MOST No. 105-2221-E-002 -143 -MY2 and No. 106-3114-E-002 -008.
Publisher Copyright:
© 2018 IEEE.
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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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.
T2 - 24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
Y2 - 29 August 2018 through 31 August 2018
ER -