Presentation
Overhead-Guided Instrumentation Refinement
DescriptionInstrumentation is a widely used technique for gathering performance data.
However, excessive instrumentation can lead to significant runtime overheads, potentially skewing performance analysis results.
In this work, we propose a novel approach to automatically generate and refine instrumentation configurations (ICs) to maximize measurement coverage while adhering to a user-defined overhead budget.
Our approach formulates the problem of selecting instrumented functions as a binary knapsack problem, integrating dynamic profile data and static call-graph information to estimate costs.
We implement this approach within the PIRA profiling infrastructure and
demonstrate its effectiveness with the LULESH, AMG2013, MILC and ASTAR proxy applications, achieving relevant hot spot coverage while staying within the specified overhead limit.
However, excessive instrumentation can lead to significant runtime overheads, potentially skewing performance analysis results.
In this work, we propose a novel approach to automatically generate and refine instrumentation configurations (ICs) to maximize measurement coverage while adhering to a user-defined overhead budget.
Our approach formulates the problem of selecting instrumented functions as a binary knapsack problem, integrating dynamic profile data and static call-graph information to estimate costs.
We implement this approach within the PIRA profiling infrastructure and
demonstrate its effectiveness with the LULESH, AMG2013, MILC and ASTAR proxy applications, achieving relevant hot spot coverage while staying within the specified overhead limit.
