Variable intra-task threading for power-constrained performance and energy optimization in DAG scheduling

• Published in: The Journal of supercomputing 2019-03, Vol.75 (3), p.1717-1731
• Main Authors: Rey, Antón, Igual, Francisco D., Prieto-Matías, Manuel
• Format: Article
• Language: English
• Subjects: Cholesky factorization; Compilers; Computer Science; Computer simulation; Concurrency; Energy conservation; Hardware; Interpreters; Multiprocessing; Optimization; Parallel programming; Processor Architectures; Programming Languages; Task complexity; Task scheduling
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-019-02760-6

Task-parallel programming models have alleviated the gap between software and hardware complexity in high-performance computing. However, the developer is still in charge of complex decisions that have a significant impact in the overall efficiency and affect the application development. Specifically, in a context in which a set of heterogeneous and interdependent tasks share resources, there is a complex interplay between different factors such as task granularity, task criticality, problem size, application inter-task and intra-task parallelism and available hardware concurrency. In this paper, we explore the effects of this mix from a static scheduling perspective, by exposing a mixed-integer linear program in which the amount of inter- and intra-task parallelism can be adapted as the execution evolves. We solve a set of instances simulating a dense Cholesky factorization on a 20-core Xeon multiprocessor in a power-constrained scenario targeting makespan and energy minimization. The model reveals performance gains up to 17.9% in terms of performance and 4.1% in terms of energy by discovering a set of high-quality scheduling solutions.