PADDLE: Performance Analysis Using a Data-Driven Learning Environment
2018
The use of
machine learningtechniques to model execution time and power consumption, and, more generally, to characterize performance data is gaining traction in the HPC community. Although this signifies huge potential for automating complex inference tasks, a typical analytics pipeline requires selecting and extensively tuning multiple components ranging from
feature learningto statistical inferencing to visualization. Further, the algorithmic solutions often do not generalize between problems, thereby making it cumbersome to design and validate
machine learningtechniques in practice. In order to address these challenges, we propose a unified
machine learningframework,
PADDLE, which is specifically designed for problems encountered during analysis of HPC data. The proposed framework uses an information-theoretic approach for hierarchical
feature learningand can produce highly robust and interpretable models. We present user-centric workflows for using
PADDLEand demonstrate its effectiveness in different scenarios: (a) identifying causes of network congestion; (b) determining the best performing linear solver for sparse matrices; and (c) comparing performance characteristics of parent and proxy application pairs.
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