Title: Cross-Platform Performance Prediction Using Partial Execution

Abstract:

Performance prediction across platforms is increasingly important as
developers can choose from a wide range of execution platforms. The main
challenge remains to perform accurate predictions at a low-cost across
different architectures.

In this project, we derive an affordable method approaching cross-platform
performance translation based on relative performance between two
platforms. We argue that relative performance can be observed without
running a parallel application in full. We show that it suffices to
observe very short partial executions of an application since most
parallel codes are iterative and behave predictably manner after a minimal
startup period. This novel prediction approach is observation-based. It
does not require program modeling, code analysis, or architectural
simulation. Our performance results using real platforms and production
codes demonstrate that prediction derived from partial executions can
yield high accuracy at a low cost. We also assess the limitations of our
model and identify future research directions on observation-based
performance prediction.