The Experts below are selected from a list of 9216 Experts worldwide ranked by ideXlab platform
Lu Peng - One of the best experts on this subject based on the ideXlab platform.
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versatile prediction and fast estimation of architectural vulnerability factor from Processor performance metrics
High-Performance Computer Architecture, 2009Co-Authors: Lide Duan, Lu PengAbstract:The shrinking Processor Feature size, lower threshold voltage and increasing clock frequency make modern Processors highly vulnerable to transient faults. Architectural Vulnerability Factor (AVF) reflects the possibility that a transient fault eventually causes a visible error in the program output, and it indicates a system's susceptibility to transient faults. Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system performance and reliability. However, tracking the AVF during program execution is extremely costly, which makes accurate AVF prediction extraordinarily attractive to computer architects. In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and Processor configurations between a key Processor structure's AVF and various performance metrics. The proposed method not only makes an accurate prediction but quantitatively illustrates individual performance variable's importance to the AVF. Moreover, to reduce the prediction complexity, we also utilize a technique named Patient Rule Induction Method to extract some simple selecting rules on important metrics. Applying these rules during run time can fast identify execution intervals with a relatively high AVF.
Lide Duan - One of the best experts on this subject based on the ideXlab platform.
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versatile prediction and fast estimation of architectural vulnerability factor from Processor performance metrics
High-Performance Computer Architecture, 2009Co-Authors: Lide Duan, Lu PengAbstract:The shrinking Processor Feature size, lower threshold voltage and increasing clock frequency make modern Processors highly vulnerable to transient faults. Architectural Vulnerability Factor (AVF) reflects the possibility that a transient fault eventually causes a visible error in the program output, and it indicates a system's susceptibility to transient faults. Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system performance and reliability. However, tracking the AVF during program execution is extremely costly, which makes accurate AVF prediction extraordinarily attractive to computer architects. In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and Processor configurations between a key Processor structure's AVF and various performance metrics. The proposed method not only makes an accurate prediction but quantitatively illustrates individual performance variable's importance to the AVF. Moreover, to reduce the prediction complexity, we also utilize a technique named Patient Rule Induction Method to extract some simple selecting rules on important metrics. Applying these rules during run time can fast identify execution intervals with a relatively high AVF.
Sankardas Roy - One of the best experts on this subject based on the ideXlab platform.
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practical always on taint tracking on mobile devices
Workshop on Hot Topics in Operating Systems, 2015Co-Authors: Justin Paupore, Earlence Fernandes, Atul Prakash, Sankardas RoyAbstract:Taint tracking is a crucial yet expensive security primitive. In the context of mobile devices, given the volume of sensitive data being generated and manipulated, taint tracking is an important aspect of defense in depth, yet is not widely adopted due to performance and energy constraints. Existing work has proposed several forms of optimization for desktop based systems -- software-only mechanisms, static analysis, hybrid analysis, and hardware-assisted techniques. This paper makes the case for an always-on taint tracking system for mobile devices that embraces the unique properties of mobile operating systems -- interpreted runtimes, well-defined APIs, and an overlooked ARM Processor Feature. Our proposed system combines precise static analysis on Java code and real-time instruction trace support widely available on ARM Processors to enable efficient taint tracking.
Justin Paupore - One of the best experts on this subject based on the ideXlab platform.
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practical always on taint tracking on mobile devices
Workshop on Hot Topics in Operating Systems, 2015Co-Authors: Justin Paupore, Earlence Fernandes, Atul Prakash, Sankardas RoyAbstract:Taint tracking is a crucial yet expensive security primitive. In the context of mobile devices, given the volume of sensitive data being generated and manipulated, taint tracking is an important aspect of defense in depth, yet is not widely adopted due to performance and energy constraints. Existing work has proposed several forms of optimization for desktop based systems -- software-only mechanisms, static analysis, hybrid analysis, and hardware-assisted techniques. This paper makes the case for an always-on taint tracking system for mobile devices that embraces the unique properties of mobile operating systems -- interpreted runtimes, well-defined APIs, and an overlooked ARM Processor Feature. Our proposed system combines precise static analysis on Java code and real-time instruction trace support widely available on ARM Processors to enable efficient taint tracking.
Earlence Fernandes - One of the best experts on this subject based on the ideXlab platform.
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practical always on taint tracking on mobile devices
Workshop on Hot Topics in Operating Systems, 2015Co-Authors: Justin Paupore, Earlence Fernandes, Atul Prakash, Sankardas RoyAbstract:Taint tracking is a crucial yet expensive security primitive. In the context of mobile devices, given the volume of sensitive data being generated and manipulated, taint tracking is an important aspect of defense in depth, yet is not widely adopted due to performance and energy constraints. Existing work has proposed several forms of optimization for desktop based systems -- software-only mechanisms, static analysis, hybrid analysis, and hardware-assisted techniques. This paper makes the case for an always-on taint tracking system for mobile devices that embraces the unique properties of mobile operating systems -- interpreted runtimes, well-defined APIs, and an overlooked ARM Processor Feature. Our proposed system combines precise static analysis on Java code and real-time instruction trace support widely available on ARM Processors to enable efficient taint tracking.