Evaluating Execution Time Predictability of Task-Based Programs on Multi-Core Processors

• Published in: Euro-Par 2014: Parallel Processing Workshops pp. 218 - 229
• Main Authors: Grass, Thomas, Rico, Alejandro, Casas, Marc, Moreto, Miquel, Ramirez, Alex
• Format: Book Chapter Publication
• Language: English
• Published: Cham Springer International Publishing 2015; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Arquitectura de computadors; Arquitectures paral·leles; Execution Time Predictability; Informàtica; Multi-Core; Multiprocessadors; Multiprocessors; Parallel processing (Electronic computers); Processament en paral·lel (Ordinadors); Task-Based Programming Models; Àrees temàtiques de la UPC
• ISBN: 3319143123; 9783319143125
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-14313-2_19

Task-based programming models are becoming increasingly important, as they can reduce the synchronization costs of parallel programs on multi-cores. Instances of the same task type in task-based programs consist of the same code, which leads us to the hypothesis that their performance should be regular and thus their execution time should be predictable. We evaluate this hypothesis for a set of 12 task-based programs on 4 different machines: a high-end Intel SandyBridge, an IBM POWER7, an ARM Cortex-A9 and an ARM Cortex-A15. We show, that predicting execution time assuming performance regularity can lead to errors of up to 92%. We identify and analyze three sources of execution time impredictability: input dependence, multiple behaviors per task type and resource sharing. We present two models based on linear interpolation and clustering, reducing the prediction error to less than 12% for input dependent task types and to less than 2% for task types with multiple classes of behavior. All in all, this work invalidates the assumption that performance is always regular across instances of the same task type and quantifies its variability on a wide range of benchmarks and multi-core systems.