Automatic Instrumentation of Dataflow Applications using PAPI

Madroñal Quintín, Daniel and Morvan, Antoine and Lazcano López, Raquel and Salvador Perea, Rubén and Desnos, Karol and Juárez Martínez, Eduardo and Sanz, César (2018). Automatic Instrumentation of Dataflow Applications using PAPI. In: "CF ’18: Computing Frontiers Conference", 8-11 Mayo de 2018, Ischya, Italia.


Title: Automatic Instrumentation of Dataflow Applications using PAPI
  • Madroñal Quintín, Daniel
  • Morvan, Antoine
  • Lazcano López, Raquel
  • Salvador Perea, Rubén
  • Desnos, Karol
  • Juárez Martínez, Eduardo
  • Sanz, César
Item Type: Presentation at Congress or Conference (Poster)
Event Title: CF ’18: Computing Frontiers Conference
Event Dates: 8-11 Mayo de 2018
Event Location: Ischya, Italia
Title of Book: CF ’18: Computing Frontiers Conference
Date: 8 May 2018
Faculty: Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad (CITSEM) (UPM)
Department: Otro
Creative Commons Licenses: None

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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.

Funding Projects

Horizon 2020EH160010411CERBEROUnspecifiedCross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments

More information

Item ID: 51789
DC Identifier:
OAI Identifier:
DOI: 10.1145/3203217.3209886
Deposited by: D Daniel Madroñal Quintín
Deposited on: 30 Jul 2018 19:45
Last Modified: 30 Jul 2018 19:47
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