VM performance evaluation with functional models

Sinschek, Jan; Sewe, Andreas; Mezini, Mira

doi:10.1145/1711506.1711512

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Besides, JVMs have an interpreter equipped with a dynamic compiler that performs an optimized compilation of the parts of the program that are most frequently used . Notwithstanding, this poses several challenges when benchmarking Java codes due to ‘noise’ being added by these mechanisms: Performance evaluation in JVMs is difficult because they act as an abstraction layer, which could change results across different runs. Different Java compilers make different optimizations. Some compilers might detect dead code (code not affecting the output) and delete it.…”

Section: Experiments Developmentmentioning

confidence: 99%

Improving scientific application execution on android mobile devices via code refactorings

2016

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The increasing number of mobile devices with ever-growing capabilities makes them useful for running scientific applications. However, these applications have high computational demands, whereas mobile devices have limited capabilities when compared with non-mobile devices. More importantly, mobile devices rely on batteries for their power supply. We initially measure the battery consumption of different versions of known micro-benchmarks representing common programming primitives found in scientific applications. Then, we analyze the performance of such micro-benchmarks in CPU-intensive mobile applications. We apply good programming practices and code refactorings to reduce battery consumption of scientific mobile applications. Our results show the reduction in energy usage from applying these refactorings to three scientific applications, and we consequently propose guidelines for high-performance computing applications. Our focus is on Android, the dominant mobile operating system. As a long-term contribution, our results represent one more step in the progress towards hybrid distributed infrastructures comprising fixed and mobile nodes, that is, the so-called mobile grids.In general, this device is more powerful than a smartphone. Furthermore, smartphones and tablets offer interesting hardware features such as sensors, GPS, and accelerometers.Additionally, it is important to mention the accelerated evolution of mobile devices. We can observe the constant progress in smartphones by comparing the Samsung Galaxy S, which was launched on September 9, 2010, with its successor the Samsung Galaxy SII. The Samsung Galaxy S used the Samsung S5PC110 processor, which combined a 45-nm 1-GHz ARM Cortex-A8-based CPU with 512 MB of RAM. Also, it has a 2 GB of internal storage. Regarding the battery life, it has a talk time of up to 6 h and a standby time of up to 12.5 days. The Samsung Galaxy S supports Bluetooth 3.0, WiFi 802.11b/g/n, and 3G data up to 7.2 Mbit/s. In contrast, the Galaxy S II has a 1.2-GHz dual-core processor, 1 GB of RAM, and 16 GB of internal mass storage. It has a battery standby time of up to 10.5 days and talk time of up to 8 h. Subsequent models that followed have even more capacity.Battery consumption in mobile devices is one of the most important problems in mobile computing research. The example earlier shows that while computational capacity has increased rapidly, battery power has increased slowly [3,4]. This paper aims to assess the energy capabilities of mobile devices using different micro-benchmarks commonly found in scientific applications and to show the impact of several known code refactorings to save battery in mobile devices. This means that we chose refactorings that have been proposed to improve maintainability, readability, and so on and we evaluated their impact in battery consumption. Currently mobile devices are not only devices for connection/access to external computing resources, but they are becoming core nodes of computational infrastructures in scientific projects [1]. Ther...

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