Reducing Training Time in a One-shot Machine Learning-based Compiler

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Presentation transcript:

Reducing Training Time in a One-shot Machine Learning-based Compiler John Thomson, Michael O'Boyle, Grigori Bursin, Björn Franke Presented by: Muhsin Zahid UGUR Dept of Computer & Information Sciences University of Delaware

A brief introduction of the paper The cluster-based approach Results

A brief introduction Iterative compilation Performance Training cost

The cluster-based approach

The cluster-based approach (the steps in detail) Clustering Clustered using GustafsonKessel algorithm Distances are minimized

The cluster-based approach (the steps in detail) - cont. Training Find the best optimization settings Build a model One-shot compilation Use a nearest neighbor model Deployment Extracted features input to nearest neighbor classifier Benchmark compiled and executed

Cluster approach 6 typical programs represent the clusters Select 4000 random flag settings Best performing one recorded

Standard Random Training Selection Use random selection to select programs to train on 6 benchmarks Robust mean performance

Generating the upper bound Apply 4000 different optimizations A reasonable upper bound limit

Results

Results (cont.)

Results (cont.)

Conclusion Reduce the amount of training Better characterize the program-space 1.14 speedup on EEMBCv2

Questions?

Thank you.