Efficient dynamic program monitoring on multi-core platforms.
2012-06
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Efficient dynamic program monitoring on multi-core platforms.
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2012-06
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Abstract
Software security and reliability have become increasingly important in the mod-
ern world. An effective approach to enforcing software security and reliability is to
monitor a program’s execution at run time. However, instrumentation-based imple-
mentation of a dynamic program monitor on single-core systems suffers significant
performance overhead. As multi-core architecture becomes more mainstream, im-
plementing efficient dynamic program monitoring by assigning monitoring activities
onto separate processor cores and thus reducing performance overhead becomes not
only a feasible but an appealing way to enforce software security and reliability. To
achieve efficient and flexible multi-core based dynamic program monitoring, however,
three challenging issues must be carefully considered and adequately addressed: the
hardware support, the monitoring model, and the parallelization of monitoring tasks.
This dissertation proposes novel solutions to these challenging problems. The
hardware support proposed in this dissertation, which is referred to as extraction
logic, selectively extracts execution information from the monitored program and
forwards it to a monitor running on a separate CPU core. The extraction logic is
generic and configurable by the monitor so that it can support a large spectrum of
monitoring tasks. Based on this generic hardware support, this dissertation proposes a novel monitoring model, referred to as the distill-based monitor model. Monitors
in this execution model is generated by special compiler supports. The distill-based monitor model is based on the observation that a monitor needs only partial informa-
tion from the monitored execution and that of this needed information, some can be
easily computed by the monitor from other information that has already been com-
municated. We implemented a code generator and optimization techniques to decide
which set of information to forward and which set to compute so as to minimize the
total execution time of the monitor. This compiler support can optimize a variety
of monitors with diverse monitoring requirements, taking as input the control flow
graph of the monitored program and the set of monitoring requirements.
To parallelize monitoring tasks, this dissertation proposes a novel paralleliza-
tion paradigm built on General-purpose Computing on Graphics Processing Unit
(GPGPU) architecture. In the following chapters, we first propose a generic, purely
software-based GPGPU monitor framework that is flexible enough to support par-
allelization of various kinds of monitoring tasks. Furthermore, we propose software- based optimization techniques built on this framework that effectively take advantage
of various characteristics of monitoring tasks such as taint-propagation and memory-
bug detection, and thus achieve significant performance improvement.
This dissertation reports the performance improvement achieved by the proposed
monitoring model and parallelization paradigm. Relative to the performance of
traditional instrumentation-based monitor for taint-propagation and memory-bug-
detection, the proposed compiler support is able to bring down performance overhead
by 3.7 times and 2.2 times for SPEC2006INT benchmarks. The proposed GPGPU-
based monitor with optimization even achieves more for memory-bug detection, re- ducing performance overhead by 5.2 times.
Description
University of Minnesota Ph.D. dissertation. June 2012. Major: Computer science. Advisor:Professor Antonia Zhai. 1 computer file (PDF); x, 122 pages.
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He, Guojin. (2012). Efficient dynamic program monitoring on multi-core platforms.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/131747.
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