Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution Session: Performance Prediction and Analysis Event Type: Paper Time: 11:00am - 11:30am Session Chair: Satoshi Matsuoka Speaker(s): Leo T. Yang, Xiaosong Ma, Frank Mueller Location: 606-607 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 paper, 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. This paper can be found in the ACM and IEEE Digital Libaries Click here for ACM Click here for IEEE Chair/Speaker Details: Satoshi Matsuoka (Chair) Tokyo Institute of Technology Leo T. Yang NCSU Xiaosong Ma NCSU Frank Mueller NCSU