Browsing by Author "Chang, Kuei-Chung"

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Application Performance Profiling in Android Dalvik Virtual Machines
(2016-05-01)
Chang, Hung-Cheng
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Chang, Kuei-Chung
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Lin, Ying-Dar
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Lai, Yuan-Cheng
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資訊工程學系
Most Android applications are written in JAVA and run on a Dalvik virtual machine. For smartphone vendors and users who wish to know the performance of an application on a particular smartphone but cannot obtain the source code, we propose a new technique, Dalvik Profiler for Applications (DPA), to profile an Android application on a Dalvik virtual machine without the support of source code. Within a Dalvik virtual machine, we determine the entry and exit locations of a method, log its execution time, and analyze the log to determine the performance of the application. Our experimental results show an error ratio of less than 5% from the baseline tool Traceview which instruments source code. The results also show some interesting behaviors of applications and smartphones: the performance of some smartphones with higher hardware specifications is 1.5 times less than the phones with lower specifications. DPA is now publicly available as an open source tool.
CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation
(2014-06-09)
Yu, Chin-Sheng
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Cheng, Chih-Wen
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Su, Wen-Chi
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Chang, Kuei-Chung
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Huang, Shao-Wei
CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to search for homologous sequences that are GO annotated in an in-house database derived from the UniProt KnowledgeBase database. At the same time, CELLO attempts predict at least one subcellular localization on the basis of the species in which the protein is found. When homologs for the query sequence have been identified, the number of terms found for each of their GO categories, i.e., cellular compartment, molecular function, and biological process, are summed and presented as pie charts representing possible functional annotations for the queried protein. Although the experimental subcellular localization of a protein may not be known, and thus not annotated, CELLO can confidentially suggest a subcellular localization. CELLO2GO should be a useful tool for research involving complex subcellular systems because it combines CELLO and BLAST into one platform and its output is easily manipulated such that the user-specific questions may be readily addressed.
Maintaining Performance on Power Gating of Microprocessor Functional Units by Using a Predictive Pre-Wakeup Strategy
(2011-10-01)
Yeh, Chang-Ching
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Chang, Kuei-Chung
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Chen, Tien-Fu
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Yeh, Chingwei
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資訊工程學系
Power gating is an effective technique for reducing leakage power in deep submicron CMOS technology. Microarchitectural techniques for power gating of functional units have been developed by detecting suitable idle regions and turning them off to reduce leakage energy consumption; however, wakeup of functional units is needed when instructions are ready for execution such that wakeup overhead is naturally incurred. This study presents time-based power gating with reference pre-wakeup (PGRP), a novel predictive strategy that detects suitable idle periods for power gating and then enables pre-wakeup of needed functional units for avoiding wakeup overhead. The key insight is that most wakeups are repeated due to program locality. Thus, the pre-wakeup predictor learns the wakeup events and selects which prior branch instruction can provide early wakeup (wakeup patterns are visible); these information are then used to adequately prepare available functional units for instruction execution. Simulation results with benchmarks from SPEC2000 applications show that substantial leakage energy reduction with negligible performance degradation (0.38% on average) is worthwhile.
Neural fuzzy based indoor localization by kalman filtering with propagation channel Modeling
(2007)
Wu, Bing-Fei
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Jen, Cheng-Lung
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Chang, Kuei-Chung
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電控工程研究所
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Institute of Electrical and Control Engineering
In this study, an indoor localization based on the received signal strength indication (RSSI) in wireless sensor networks (WSN) is proposed. The presented approach proceeds in two phases: the first phase is based on the recorded received signal strength at the certain location. The interpolation, curve fitting and an adaptive neural fuzzy inference system (ANFIS) are used to develop the indoor propagation model, respectively. Thus the strength of the received radio signal can be converted to a physical distance approximately; in the second phase, based on the available distances from the positions localized in the test bed are estimated by using an extended Kalman filter (EKF). In comparison among the propagation models based on the interpolation, ANFIS and curve fitting, the experimental results show that the proposed approach provides a precise performance.
Reconfigurable Multi-Resolution Performance Profiling in Android Applications
(2013-09-01)
Lin, Ying-Dar
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Chang, Kuei-Chung
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Lai, Yuan-Cheng
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Lai, Yu-Sheng
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資訊工程學系
The computing of applications in embedded devices suffers tight constraints on computation and energy resources. Thus, it is important that applications running on these resource-constrained devices are aware of the energy constraint and are able to execute efficiently. The existing execution time and energy profiling tools could help developers to identify the bottlenecks of applications. However, the profiling tools need large space to store detailed profiling data at runtime, which is a hard demand upon embedded devices. In this article, a reconfigurable multi-resolution profiling (RMP) approach is proposed to handle this issue on embedded devices. It first instruments all profiling points into source code of the target application and framework. Developers can narrow down the causes of bottleneck by adjusting the profiling scope using the configuration tool step by step without recompiling the profiled targets. RMP has been implemented as an open source tool on Android systems. Experiment results show that the required log space using RMP for a web browser application is 25 times smaller than that of Android debug class, and the profiling error rate of execution time is proven 24 times lower than that of debug class. Besides, the CPU and memory overheads of RMP are only 5% and 6.53% for the browsing scenario, respectively.