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Applications
Tangram is flexible and can express a wide variety of workloads efficiently. Here, we provide a subset of applications that can be supported by Tangram as examples. In fact, Tangram can support more different applications.
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Bulk Processing
As MapUpdate is a superset of MapReduce, it should be able to support any MapReduce-style bulk processing applications. We provide two simple examples:
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Machine Learning
Tangram supports parameter-server-style distributed machine learning naturally with the MapUpdate API. Parameter-server is the de facto solutions for distributed machine learning. Please refer to the PS and Bosen papers for more information. Tangram also supports asynchronous training with staleness.
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Graph Analytics
Tangram also naturally supports vertex-centric graph analytics.
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Other Asynchronous Workloads