A structured approach to the simulation, analysis and characterization of smartphone applications

Sunwoo, Dam; Wang, William Yang; Ghosh, Mrinmoy; Sudanthi, Chander; Blake, Geoffrey; Emmons, Christopher D.; Paver, N.C.

doi:10.1109/iiswc.2013.6704677

Cited by 25 publications

(12 citation statements)

References 18 publications

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“…Providing user inputs and studying the system is not an easy task due to the associated non-determinism; for that, one possible method is to capture user inputs that are sent to the OS, replay it exactly while evaluating the system [33,40]. In our infrastructure, we use Android emulator as the front end, where one can install almost all applications available on Google Play and provide inputs like the way it is done on SoCs.…”

Section: Trace-based Simulationmentioning

confidence: 99%

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GemDroid

Nachiappan

Yedlapalli

Soundararajan

et al. 2014

The 2014 ACM International Conference on Measurement and Modeling of Computer Systems

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As the demand for feature-rich mobile systems such as smartphones and tablets has outpaced other computing systems and is expected to continue at a faster rate, it is projected that SoCs with tens of cores and hundreds of IPs (or accelerator) will be designed to provide unprecedented level of features and functionality in future. Design of such mobile systems with required QoS and power budgets along with other design constraints will be a daunting task for computer architects since any ad hoc, piece-meal solution is unlikely to result in an optimal design. This requires early exploration of the complete design space to understand the system-level design trade-offs. To the best of our knowledge, there is no such publicly available tool to conduct a holistic evaluation of mobile platforms consisting of cores, IPs and system software.This paper presents GemDroid, a comprehensive simulation infrastructure to address these concerns. GemDroid has been designed by integrating the Android open-source emulator for facilitating execution of mobile applications, the GEM5 core simulator for analyzing the CPU and memory centric designs, and models for several IPs to collectively study their impact on system-level performance and power. Analyzing a spectrum of applications with GemDroid, we observed that the memory subsystem is a vital cog in the mobile platform because, it needs to handle both core and IP traffic, which have very different characteristics. Consequently, we present a heterogeneous memory controller (HMC) design, where we divide the memory physically into two address regions, where the first region with one memory controller (MC) handles core-specific application data and the second region with another MC handles all IP related data. The proposed modifications to the memory controller design results in an average 25% reduction in execution time for CPU bound applications, up to 11% reduction in frame drops, and on average 17% reduction in CPU busy time for on-screen (IP bound) applications.

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Section: Trace-based Simulationmentioning

confidence: 99%

“…Another recent work by Sunwoo et al [40] proposes an infrastructure to simulate smartphone cores, by integrating the architectural simulator GEM5 and OS, to study emerging smartphone workloads. Again this study is only core centric and lacks IP analyses.…”

Section: Related Workmentioning

confidence: 99%

GemDroid

Nachiappan

Yedlapalli

Soundararajan

et al. 2014

The 2014 ACM International Conference on Measurement and Modeling of Computer Systems

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“…Using their tool they are capable of reproducing bugs in popular ANDROID applications, but their study does not focus on user perception. Sunwoo et al [9] study several existing smartphone benchmarks and applications including ANDEBENCH, CAFFEINEMARK, RL BENCHMARK, ANGRY BIRDS, and KINGSOFTOFFICE with the aim to measure the performance of the DALVIK virtual machine, SQLITE and the OS. They use the GEM5 simulator and an AUTOGUI system, which captures user input and subsequently synchronizes service of input by evaluating the frame buffer.…”

Section: A Interactive Mobile Workloadsmentioning

confidence: 99%

Measuring QoE of interactive workloads and characterising frequency governors on mobile devices

Seeker

Petoumenos

Leather

et al. 2014

2014 IEEE International Symposium on Workload Characterization (IISWC)

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Mobile computing devices such as smartphones and tablets have become tightly integrated with many people's life, both at work and at home. Users spend large amounts of time interacting with their mobile device and demand an excellent user experience in terms of responsiveness, whilst simultaneously expecting a long battery life between charging cycles. Frequency governors, responsible for increasing or decreasing the CPU clock frequency depending on the current workload and external events, try to balance the two contrasting goals of high performance and low energy consumption. However, despite their critical role in providing energy efficiency it is difficult to measure the effectiveness of frequency governors in an interactive environment. In this paper we develop a novel methodology for creating repeatable, fully automated, realistic, workloads that can accurately measure time lag in interactive applications resulting from non-optimally selected operating frequencies. We also introduce a new metric capturing the user experience for different ANDROID frequency governors. We evaluate interactive workloads to demonstrate how our approach enables us to automatically record and replay sequences of user interactions for different system configurations. We demonstrate that none of the available ANDROID frequency governors performs particularly well, but leave substantial room for improvement. We show that energy savings of up to 27% are possible, whilst delivering a user experience that is better than that provided by the standard ANDROID frequency governor. We also show that it is possible to save 47% energy with performance that is indistinguishable from permanently running the CPU at the highest frequency.

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“…[18] present a workload characterization and memorylevel analysis of internet and media-centric applications for an embedded system used for mobile applications. Sunwoo et al [19] propose a methodology to tractably explore the processor design space and to characterize applications in a full-system simulation environment. Zhang et al [20] study the performance of mobile applications using multicore CPUs and develop a new CPU power model with a high accuracy.…”

Section: A Mobile Workload Characterizationmentioning

confidence: 99%

A study of mobile device utilization

Cao

Gutierrez

Rajan

et al. 2015

2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

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Mobile devices are becoming more powerful and versatile than ever, calling for better embedded processors. Following the trend in desktop CPUs, microprocessor vendors are trying to meet such needs by increasing the number of cores in mobile device SoCs. However, increasing the number does not translate proportionally into performance gain and power reduction. In the past, studies have shown that there exists little parallelism to be exploited by a multi-core processor in desktop platform applications, and many cores sit idle during runtime. In this paper, we investigate whether the same is true for current mobile applications.We analyze the behavior of a broad range of commonly used mobile applications on real devices. We measure their Thread Level Parallelism (TLP), which is the machine utilization over the non-idle runtime. Our results demonstrate that mobile applications are utilizing less than 2 cores on average, even with background applications running concurrently. We observe a diminishing return on TLP with increasing the number of cores, and low TLP even with heavy-load scenarios. These studies suggest that having many powerful cores is over-provisioning. Further analysis of TLP behavior and big-little core energy efficiency suggests that current mobile workloads can benefit from an architecture that has the flexibility to accommodate both high performance and good energy-efficiency for different application phases.

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A structured approach to the simulation, analysis and characterization of smartphone applications

Cited by 25 publications

References 18 publications

GemDroid

GemDroid

Measuring QoE of interactive workloads and characterising frequency governors on mobile devices

A study of mobile device utilization

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