Invention Grant
- Patent Title: Dynamic recompilation techniques for machine learning programs
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Application No.: US15581456Application Date: 2017-04-28
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Publication No.: US10534590B2Publication Date: 2020-01-14
- Inventor: Matthias Boehm , Berthold Reinwald , Shirish Tatikonda
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Lieberman & Brandsdorfer, LLC
- Main IPC: G06F8/41
- IPC: G06F8/41

Abstract:
The embodiments described herein relate to recompiling an execution plan of a machine-learning program during runtime. An execution plan of a machine-learning program is compiled. In response to identifying a directed acyclic graph of high-level operations (HOP DAG) for recompilation during runtime, the execution plan is dynamically recompiled. The dynamic recompilation includes updating statistics and dynamically rewriting one or more operators of the identified HOP DAG, recomputing memory estimates of operators of the rewritten HOP DAG based on the updated statistics and rewritten operators, constructing a directed acyclic graph of low-level operations (LOP DAG) corresponding to the rewritten HOP DAG based in part on the recomputed memory estimates, and generating runtime instructions based on the LOP DAG.
Public/Granted literature
- US20170228222A1 DYNAMIC RECOMPILATION TECHNIQUES FOR MACHINE LEARNING PROGRAMS Public/Granted day:2017-08-10
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