SD3: A Scalable Approach to Dynamic Data-Dependence Profiling

As multicore 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: (1) runtime overhead and (2) memory overhead. Existing data-dependence profilers are either unable to profile large-scale applications or only report very limited information. In this paper, we propose a scalable approach to data-dependence profiling that addresses both runtime and memory overhead in a single framework. Our technique, called SD
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, reduces the runtime overhead by parallelizing the dependence profiling step itself. To reduce the memory overhead, we compress memory accesses that exhibit stride patterns and compute data dependences directly in a compressed format. We demonstrate that SD
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reduces the runtime overhead when profiling SPEC 2006 by a factor of 4.1× and 9.7× on eight cores and 32 cores, respectively. For the memory overhead, we successfully profile SPEC 2006 with the reference input, while the previous approaches fail even with the train input. In some cases, we observe more than a 20× improvement in memory consumption and a 16× speedup in profiling time when 32 cores are used.