Managing low-level architectural features for controlling performance and power consumption is a growing demand in the parallel computing community. Such features include, but are not limited to: energy profiling, platform topology analysis, CPU cores disabling and frequency scaling. However, these low-level mechanisms are usually managed by specific tools, without any interaction between each other, thus hampering their usability. More important, most existing tools can only be used through a command line interface and they do not provide any API. Moreover, in most cases, they only allow monitoring and managing the same machine on which the tools are used. MAMMUT provides and integrates architectural management utilities through a high-level and easy-to-use object-oriented interface. By using MAMMUT, is possible to link together different collected information and to exploit them on both local and remote systems, to build architecture-aware applications.

Mammut: High-level management of system knobs and sensors

DE SENSI, DANIELE;TORQUATI, MASSIMO;DANELUTTO, MARCO
2017-01-01

Abstract

Managing low-level architectural features for controlling performance and power consumption is a growing demand in the parallel computing community. Such features include, but are not limited to: energy profiling, platform topology analysis, CPU cores disabling and frequency scaling. However, these low-level mechanisms are usually managed by specific tools, without any interaction between each other, thus hampering their usability. More important, most existing tools can only be used through a command line interface and they do not provide any API. Moreover, in most cases, they only allow monitoring and managing the same machine on which the tools are used. MAMMUT provides and integrates architectural management utilities through a high-level and easy-to-use object-oriented interface. By using MAMMUT, is possible to link together different collected information and to exploit them on both local and remote systems, to build architecture-aware applications.
2017
DE SENSI, Daniele; Torquati, Massimo; Danelutto, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/873289
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