Abstract
The stagnation of single-core performance leaves application developers with software parallelism as the only option to further benefit from Moore’s Law. However, in view of the complexity of writing parallel programs, the parallelization of myriads of sequential legacy programs presents a serious economic challenge. A key task in this process is the identification of suitable parallelization targets in the source code. We have developed a tool called DiscoPoP showing how dependency profiling can be used to automatically identify potential parallelism in sequential programs. Our method is based on the notion of computational units, which are small sections of code following a read-compute-write pattern that can form the atoms of concurrent scheduling. DiscoPoP covers both loop and task parallelism. Experimental results show that reasonable speedups can be achieved by parallelizing sequential programs manually according to our findings. By comparing our findings to known parallel implementations of sequential programs, we demonstrate that we are able to detect the most important code locations to be parallelized.
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Li, Z., Atre, R., Ul-Huda, Z., Jannesari, A., Wolf, F. (2015). DiscoPoP: A Profiling Tool to Identify Parallelization Opportunities. In: Niethammer, C., Gracia, J., Knüpfer, A., Resch, M., Nagel, W. (eds) Tools for High Performance Computing 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-16012-2_3
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DOI: https://doi.org/10.1007/978-3-319-16012-2_3
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