Collecting Performance Data with PAPI-C

By Daniel Terpstra, Heike Jagode, Haihang You, Jack J. Dongarra. Parallel Tools Workshop, 2009

394 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/f4134d5ef9e712a77b1401abbdd6e520dd74e215

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Eva García-Martín
2. Håkan Grahn
3. Crefeda Faviola Rodrigues
4. Graham D. Riley

In Hardware and Architecture

1. Eva García-Martín
2. Håkan Grahn
3. Crefeda Faviola Rodrigues
4. Graham D. Riley

In Information Systems

1. Eva García-Martín
2. Håkan Grahn
3. Crefeda Faviola Rodrigues
4. Graham D. Riley

Citations in the corpus

• Estimation of energy consumption in machine learning (2019, background): “There have been several studies that validate RAPL’s interface [19,32], and several tools available that make use of such interface to provide energy measurements, such as PAPI (Performance API) [75].”