Title:
ThreadMarks: A Framework for Input-Aware Prediction of Parallel Application Behavior
ThreadMarks: A Framework for Input-Aware Prediction of Parallel Application Behavior
Author(s)
Wu, Haicheng
Hong, Kirak
Clark, Nathan
Hong, Kirak
Clark, Nathan
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Abstract
Chip-multiprocessors (CMPs) are quickly becoming entrenched as the main-stream architectural platform in computer
systems. One of the critical challenges facing CMPs is designing applications to effectively leverage the computational
resources they provide. Modifying applications to effectively run on CMPs requires understanding the
bottlenecks in applications, which necessitates a detailed understanding of architectural features. Unfortunately,
identifying bottlenecks is complex and often requires enumerating a wide range of behaviors.
To assist in identifying bottlenecks, this paper presents a framework for developing analytical models based on
dynamic program behaviors. That is, given a program and set of training inputs, the framework will generate several
analytical models that accurately predict online program behaviors such as memory utilization and synchronization
overhead, while taking program input into consideration. These models can prove invaluable for online optimization
systems and input-specific analysis of program behavior. We demonstrate that this framework is practical and accurate
on a wide range of synthetic and real-world parallel applications over various workloads.
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Date Issued
2011
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Text
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Technical Report