Understanding the behavior of current and future workloads is key for designers of future computer systems. If target workload characteristics are available, computer designers can use this information to optimize the system. This can lead to a chicken-and-egg problem: how does one characterize application behavior for an architecture that is a moving target and for which sophisticated modeling tools do not yet exist? We present a multi-pronged approach to benchmark characterization early in the design cycle. We collect statistics from multiple sources and combine them to create a comprehensive view of application behavior. We assume a fixed part of the system (service core) and a "to-be-designed" part that will gradually be developed under the measurements taken on the fixed part. Data are collected from measurements taken on existing hardware and statistics are obtained via emulation tools. These are supplemented with statistics extracted from traces and ILP information generated by the compiler. Although the motivation for this work is the classification of workloads for an embedded, reconfigurable, parallel architecture, the methodology can easily be adapted to other platforms. © 2010 IEEE.
Puzović, N., Mckee, S.A., Eres, R., Zaks, A., Gai, P., Wong, S., et al. (2010). A Multi-Pronged Approach to Benchmark Characterization. In IEEE Int.l Conf. on Cluster Computing (pp.1-4). IEEE [10.1109/CLUSTERWKSP.2010.5613090].
A Multi-Pronged Approach to Benchmark Characterization
Giorgi, Roberto
2010-01-01
Abstract
Understanding the behavior of current and future workloads is key for designers of future computer systems. If target workload characteristics are available, computer designers can use this information to optimize the system. This can lead to a chicken-and-egg problem: how does one characterize application behavior for an architecture that is a moving target and for which sophisticated modeling tools do not yet exist? We present a multi-pronged approach to benchmark characterization early in the design cycle. We collect statistics from multiple sources and combine them to create a comprehensive view of application behavior. We assume a fixed part of the system (service core) and a "to-be-designed" part that will gradually be developed under the measurements taken on the fixed part. Data are collected from measurements taken on existing hardware and statistics are obtained via emulation tools. These are supplemented with statistics extracted from traces and ILP information generated by the compiler. Although the motivation for this work is the classification of workloads for an embedded, reconfigurable, parallel architecture, the methodology can easily be adapted to other platforms. © 2010 IEEE.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/46804
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo