Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
IEEE Transactions on Computers
Determining Asynchronous Acyclic Pipeline Execution Times
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Exploring the Probabilistic Design Space of Multimedia Systems
RSP '03 Proceedings of the 14th IEEE International Workshop on Rapid System Prototyping (RSP'03)
WCET Analysis of Probabilistic Hard Real-Time Systems
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Stochastic Analysis of Periodic Real-Time Systems
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Worst case timing analysis of input dependent data cache behavior
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Proceedings of the 44th annual Design Automation Conference
Execution-time Prediction for Dynamic Streaming Applications with Task-level Parallelism
DSD '07 Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools
A Practical Approach to Exploiting Coarse-Grained Pipeline Parallelism in C Programs
Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture
The worst-case execution-time problem—overview of methods and survey of tools
ACM Transactions on Embedded Computing Systems (TECS)
A method for estimating the execution time of a parallel task on a grid node
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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This paper addresses the problem of execution time estimation for tasks in a software pipeline independent of the application structure or the underlying architecture. A regression model is developed to obtain the estimates from previously observed data. To improve the quality of the estimates execution times of predecessor task in a software pipeline is exploited. Since the Model order (number of past observations required to obtain optimal estimate) cannot be determined at design time and to circumvent this, we propose means to dynamically update the order and hence obtain a critical-fit model without resorting to analytical benchmarking or calibration runs. The estimation scheme comprises of two estimation methods, namely 'Wiener-Hopf' and Order-recursive estimation. The selection of the estimation method is automatic and depends on the required quality of the estimate against a user selectable threshold. In order recursion, new model order is obtained in conjunction to estimates, so order recursion solve the system both for order and estimate simultaneously. We experimented on two multicore platforms using H.264 decoder, a control dominant, computationally demanding application. Results show that estimates obtained by our method are up to 39% better in case of the first task in the software pipeline. The estimate quality improves significantly for the task with predecessor(s) in pipeline and comparison shows up to 54% improvement in estimation results.