Tuning pipelined scientific data analyses for efficient multicore execution
Scientific data analyses often apply a pipelined sequence of computational tasks to independent datasets. Each task in the pipeline captures and processes a dataset element, may be dependent on other tasks in the pipeline, may have a different computational complexity and may be filtered out from progressing in the pipeline. The goal of this work is to develop an efficient scheduler that automatically (i) manages a parallel data reading and an adequate data structure creation, (ii) adaptively defines the most efficient order of pipeline execution of the tasks, considering their inter-dependence and both the filtering out rate and the computational weight, and (iii) manages the parallel execution of the computational tasks in a multicore system, applied to the same or to different dataset elements. A real case study data analysis application from High Energy Physics (HEP) was used to validate the efficiency of this scheduler. Preliminary results show an impressive performance improvement of the pipeline tuning when compared to the original sequential HEP code (up to a 35x speedup in a dual 12-core system), and also show significant performance speedups over conventional parallelization approaches of this case study application (up to 10x faster in the same system).