SD3 : A Scalable Approach to Dynamic Data-Dependence Profiling

Provided by: Georgia Institute of Technology
Topic: Data Centers
Format: PDF
As multi-core processors are deployed in mainstream computing, the need for software tools to help parallelize programs is increasing dramatically. Data-dependence profiling is an important technique to exploit parallelism in programs. More specifically, manual or automatic parallelization can use the outcomes of data-dependence profiling to guide where to parallelize in a program. However, state-of-the-art data-dependence profiling techniques are not scalable as they suffer from two major issues when profiling large and long-running applications: runtime overhead and memory overhead. Existing data-dependence profilers are either unable to profile large-scale applications or only report very limited information.

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