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Content Provider | ACM Digital Library |
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Author | Liaw, Richard Wang, Stephanie Jordan, Michael I. Tumanov, Alexey Moritz, Philipp Paul, William Stoica, Ion Nishihara, Robert Niknami, Mehrdad Schleier-Smith, Johann |
Abstract | Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application. |
Starting Page | 106 |
Ending Page | 110 |
Page Count | 5 |
File Format | |
ISBN | 9781450350686 |
DOI | 10.1145/3102980.3102998 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2017-05-07 |
Publisher Place | New York |
Access Restriction | Subscribed |
Content Type | Text |
Resource Type | Article |
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