Publication: Spatial and Temporal Cache Sharing Analysis in Tasks
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2016-02
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Abstract
Understanding performance of large scale multicore systems is crucial for getting faster execution times
and optimize workload efficiency, but it is becoming harder due to the increased complexity of hardware
architectures. Cache sharing is a key component for performance in modern architectures, and it has been
the focus of performance analysis tools and techniques in recent years. At the same time, new programming
models have been introduced to aid the programmer dealing with the complexity of large scale systems,
simplifying the coding process and making applications more scalable regardless of resource sharing. Taskbased
runtime systems are one example of this that became popular recently. In this work we develop models
to tackle performance analysis of shared resources in the task-based context, and for that we study cache
sharing both in temporal and spatial ways. In temporal cache sharing, the effect of data reused over time by
the tasks executed is modeled to predict different scenarios resulting in a tool called StatTask. In spatial
cache sharing, the effect of tasks fighting for the cache at a given point in time through their execution is
quantified and used to model their behavior on arbitrary cache sizes. Finally, we explain how these tools
set up a unique and solid platform to improve runtime systems schedulers, maximizing performance of
execution of large-scale task-based applications.
Description
Proceedings of the First PhD Symposium on Sustainable Ultrascale
Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.
Keywords
Task-based runtime systems, Cache sharing, Performance analysis, NESUS
Bibliographic citation
Carretero Pérez, Jesús; et.al. (eds.). (2016). Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016). Timisoara, Romania. Universidad Carlos III de Madrid, ARCOS. Pp. 59-63.