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| Content Provider | ACM Digital Library |
|---|---|
| Author | Jiang, Guanxian Li, Jinfeng Cheng, James Huang, Yuzhen Zhao, Yunjian Yang, Fan |
| Abstract | Coarse-grained operators such as map and reduce have been widely used for large-scale data processing. While they are easy to master, over-simplified APIs sometimes hinder programmers from fine-grained control on how computation is performed and hence designing more efficient algorithms. On the other hand, resorting to domain-specific languages (DSLs) is also not a practical solution, since programmers may need to learn how to use many systems that can be very different from each other, and the use of low-level tools may even result in bug-prone programming. In [7] our prior work, we proposed Husky which provides a highly expressive API to solve the above dilemma. It allows developers to program in a variety of patterns, such as MapReduce, GAS, vertex-centric programs, and even asynchronous machine learning. While the Husky C++ engine provides great performance, in this demo proposal we introduce PyHusky and ScHusky, which allow users (e.g., data scientists) without system knowledge and low-level programming skills to leverage the performance of Husky and build high-level applications with ease using Python and Scala. |
| Starting Page | 1619 |
| Ending Page | 1622 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450341974 |
| DOI | 10.1145/3035918.3058735 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-05-09 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Programming model Distributed system Data-parallel processing |
| Content Type | Text |
| Resource Type | Article |
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