SiL: An Approach for Adjusting Applications to Heterogeneous Systems Under Perturbations

Scientific applications consist of large and computationally-intensive loops. Dynamic loop scheduling (DLS) techniques are used to load balance the execution of such applications. Load imbalance can be caused by variations in loop iteration execution times due to problem, algorithmic, or systemic characteristics (also, perturbations). The following question motivates this work: "Given an application, a high-performance computing (HPC) system, and both their characteristics and interplay, which DLS technique will achieve improved performance under unpredictable perturbations?" Existing work only considers perturbations caused by variations in the HPC system delivered computational speeds. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. Simulator in the loop (SiL) is introduced, herein, as a new control-theoretic inspired approach to dynamically select DLS techniques that improve the performance of applications on heterogeneous HPC systems under perturbations. The present work examines the performance of six applications on a heterogeneous system under all above system perturbations. The SiL proof of concept is evaluated using simulation. The performance results confirm the initial hypothesis that no single DLS technique can deliver best performance in all scenarios, while the SiL-based DLS selection delivered improved application performance in most experiments

SimAS: A Simulation-Assisted Approach for the Algorithm Selection Problem of Scheduling under Perturbations

Many scientific applications consist of large and computationally-intensive loops. Dynamic loop self-scheduling (DLS) techniques are used to parallelize and to balance the load of such applications during execution. Load imbalance arises from variations in the loop iteration (or tasks) execution times, caused by problem, algorithmic, or systemic characteristics. Variations in systemic characteristics are referred to as perturbations. Our hypothesis is that no single DLS technique can achieve the absolute best performance under various perturbations on heterogeneous HPC systems. Therefore, the selection of the most efficient DLS technique is critical to achieve the best application performance. The goal of this work is to solve the algorithm selection problem for the scheduling of computationally-intensive applications under perturbations. Existing work only considers perturbations caused by variations in the delivered computational speed of the HPC systems. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. A Simulation-assisted scheduling Algorithm Selection (SimAS) approach is introduced herein as a novel control-theoretic-inspired approach to select DLS techniques dynamically that improve the performance of applications executing on heterogeneous HPC systems under perturbations. The present work examines the performance of seven applications on a heterogeneous HPC system under all the above system perturbations. SimAS is evaluated using native and simulative experiments. The performance results confirm the original hypothesis that motivates this work. The experimental evaluation shows that the SimAS-based DLS selection identifies the most efficient technique and improves application performance in most cases