TY - GEN
T1 - Reinforcement learning for automated performance tuning
T2 - 2008 IEEE International Conference on Cluster Computing, ICCC 2008
AU - Armstrong, Warren
AU - Rendell, Alistair P.
PY - 2008
Y1 - 2008
N2 - 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 the matrix and the computer system being used. To test the above a program has been written to generate a series of sparse matrices, where any given matrix performs optimally using one of the three different storage types. For each matrix several sparse matrix vector products are performed. The goal of the learning agent is to predict the optimal sparse matrix storage format for that matrix. The proposed agent uses five attributes of the sparse matrix: the number of rows, the number of columns, the number of non-zero elements, the standard deviation of non-zeroes per row and the mean number of neighbours. The agent is characterized by two parameters: an exploration rate and a parameter that determines how the state space is partitioned. The ability of the agent to successfully predict the optimal storage format is analyzed for a series of 1,000 automatically generated test matrices.
AB - 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 the matrix and the computer system being used. To test the above a program has been written to generate a series of sparse matrices, where any given matrix performs optimally using one of the three different storage types. For each matrix several sparse matrix vector products are performed. The goal of the learning agent is to predict the optimal sparse matrix storage format for that matrix. The proposed agent uses five attributes of the sparse matrix: the number of rows, the number of columns, the number of non-zero elements, the standard deviation of non-zeroes per row and the mean number of neighbours. The agent is characterized by two parameters: an exploration rate and a parameter that determines how the state space is partitioned. The ability of the agent to successfully predict the optimal storage format is analyzed for a series of 1,000 automatically generated test matrices.
UR - http://www.scopus.com/inward/record.url?scp=57949097109&partnerID=8YFLogxK
U2 - 10.1109/CLUSTR.2008.4663802
DO - 10.1109/CLUSTR.2008.4663802
M3 - Conference contribution
SN - 9781424426409
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
SP - 411
EP - 420
BT - Proceedings of the 2008 IEEE International Conference on Cluster Computing, CCGRID 2008
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 September 2008 through 1 October 2008
ER -