Modeling program resource demand using inherent program characteristics

Authors:
Jian Chen;Lizy Kurian John;Dimitris Kaseridis
Affiliations:
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA;Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA;Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
Venue:
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Year:
2011

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Abstract

The workloads in modern Chip-multiprocessors (CMP) are becoming increasingly diversified, creating different resource demands on hardware substrate. It is necessary to allocate hardware resources based on the needs of the workloads in order to improve system efficiency and/or ensure Quality-of-Service (QoS) at certain performance levels. Therefore, it is extremely important to identify the resource demand of the workload in terms of the performance and power efficiency. Existing models are inappropriate for estimating resource demands as they require either partial simulations or time-consuming training. This paper presents an integrated framework that is able to identify the single-resource or multi-resource demands on an array of hardware resources ranging from the issue width of the processor to the memory bandwidth. With an analytical model based on program inherent characteristics, this framework does not require any detailed simulation or training yet is still able to capture the performance trend of the program accurately. Our experiment shows that the proposed framework on average provides no larger than 8.6% error to any given performance target for multi-resource demand estimation. By using the proposed performance model, the framework identifies the multi-resource demands up to 40X faster compared to the state-of-the-art analytical model. The proposed framework can be applied in workload capacity planning, hardware resource adaptation as well as coordinated resource management for QoS in CMP systems.