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Conference Papers Year : 2018

Automatic instrumentation of dataflow applications using PAPI


The widening of the complexity-productivity gap witnessed in the last years is becoming unaffordable from the application development point of view. New design methods try to automate most designers tasks in order to bridge this gap. In addition, new Models of Computation (MoC), as those dataflow-based, ease the expression of parallelism within applications and lead to higher productivity. Rapid prototyping design tools offer fast estimations of the soundness of design choices. A key step when prototyping an application is to have representative performance indicators to estimate the validity of the design choices. Such indicators can be obtained using hardware information through the Performance API (PAPI). In this work, PAPI and a dataflow MoC are integrated within a Y-chart design flow. The implementation takes the form of a dedicated automatic code generation scheme within the Preesm tool. Preliminary results show that depending on the complexity of the application, the computation time overhead due to monitoring varies from being almost negligible to more than 50%. Also, on top of offering accurate hardware performance indicators, the extracted values can be combined to estimate power or energy consumption. © 2018 Association for Computing Machinery.

Dates and versions

hal-01881032 , version 1 (25-09-2018)



D. Madronal, A. Morvan, R. Lazcano, R. Salvador, Karol Desnos, et al.. Automatic instrumentation of dataflow applications using PAPI. 15th ACM International Conference on Computing Frontiers, CF 2018, May 2018, Ischia, Italy. pp.232-235, ⟨10.1145/3203217.3209886⟩. ⟨hal-01881032⟩
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