Reinforcement Learning for Automated Performance Tuning: Initial Evaluation for Sparse Matrix Format Selection
The field of reinforcement learning has developed techniques for choosing beneficial actions within a dynamic environment. Such techniques learn from experience and do not require teaching. This paper explores how reinforcement learning techniques might be used to determine efficient storage formats for sparse matrices. Three different storage formats are considered: coordinate, compressed sparse row, and blocked compressed sparse row. Which format performs best depends heavily on the nature of...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 2008 IEEE International Conferenceon Cluster Computing|
|01_Armstrong_Reinforcement_Learning_for_2008.pdf||324.72 kB||Adobe PDF||Request a copy|
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