Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences


UC Berkeley


2009 Research Summary

Scheduling Applications with Variable Task Execution Times onto Heterogeneous Multiprocessors

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Nadathur Rajagopalan Satish and Kurt Keutzer

Gigascale Systems Research Center

We investigate statistical optimization approaches for scheduling applications represented as task dependence graphs with variable task execution times onto a heterogeneous multiprocessor system. Scheduling methods in the presence of variations typically rely on worst-case timing estimates for hard real-time applications, and average-case estimates for other applications. However, a large class of soft real-time applications require only statistical guarantees on latency and throughput. We present a general statistical model that captures the probability distributions of task execution times as well as the correlations between them. We use a Monte-Carlo based technique to perform makespan analysis of different schedules based on this model. This approach can be used to analyze the variability present in a variety of soft real-time applications, including the H.264 video processing application.

We present two scheduling algorithms based on statistical makespan analysis--one a list-scheduling based heuristic and the other a simulated annealing approach. Both algorithms take into account the required statistical guarantee. We show that optimization approaches based on statistical analysis show up to a 30% improvement in makespan over methods based on static worst-case analysis.