Static scheduling of synchronous data flow programs for digital signal processing
IEEE Transactions on Computers
Profile-assisted instruction scheduling
International Journal of Parallel Programming
Resource constrained scheduling of uniform algorithms
Journal of VLSI Signal Processing Systems
A tool for performance estimation of networked embedded end-systems
DAC '98 Proceedings of the 35th annual Design Automation Conference
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Static performance prediction of data-dependent programs
Proceedings of the 2nd international workshop on Software and performance
Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
IEEE Transactions on Parallel and Distributed Systems
Approximation algorithms for scheduling problems
Approximation algorithms for scheduling problems
Statistical optimization of leakage power considering process variations using dual-Vth and sizing
Proceedings of the 41st annual Design Automation Conference
A New Statistical Optimization Algorithm for Gate Sizing
ICCD '04 Proceedings of the IEEE International Conference on Computer Design
Methods for evaluating and covering the design space during early design development
Integration, the VLSI Journal
Computers and Operations Research
Real-Time Applications with Stochastic Task Execution Times: Analysis and Optimisation
Real-Time Applications with Stochastic Task Execution Times: Analysis and Optimisation
Practical Multiprocessor Scheduling Algorithms for Efficient Parallel Processing
IEEE Transactions on Computers
Task allocation and scheduling of concurrent applications to multiprocessor systems
Task allocation and scheduling of concurrent applications to multiprocessor systems
Proactive algorithms for job shop scheduling with probabilistic durations
Journal of Artificial Intelligence Research
First-Order Incremental Block-Based Statistical Timing Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Hybrid dynamic energy and thermal management in heterogeneous embedded multiprocessor SoCs
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Towards an approach and framework for test-execution plan derivation
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
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We present a statistical optimization approach for scheduling a task dependence graph 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, or average-case analysis 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 of execution times of different tasks. 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 a H.264 video processing application. We present two scheduling algorithms based on statistical makespan analysis. The first is a heuristic based on a critical path analysis of the task dependence graph. The other is a simulated annealing algorithm using incremental timing analysis. Both algorithms take as input the required statistical guarantee, and can thus be easily re-used for different required guarantees. We show that optimization methods based on statistical analysis show a 25-30% improvement in makespan over methods based on static worst-case analysis.