1. 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

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

3. A brief introduction
Iterative compilation
Performance
Training cost

4. The cluster-based approach

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

6. 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

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

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

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

10. Results

11. Results (cont.)

12. Results (cont.)

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

14. Questions?

15. Thank you.