Task Packing: Efficient task scheduling in unbalanced parallel programs to maximize CPU utilization

Load imbalance in parallel systems can be generated by external factors to the currently running applications like operating system noise or the underlying hardware like a heterogeneous cluster. HPC applications working on irregular data structures can also have difficulties to balance their computations across the parallel tasks. In this article we extend, improve and evaluate more deeply the Task Packing mechanism proposed in a previous work. The main idea of the mechanism is to concentrate the idle cycles of unbalanced applications in such a way that one or more CPUs are freed from execution. To achieve this, CPUs are stressed with just useful work of the parallel application tasks, provided performance is not degraded. The packing is solved by an algorithm based on the Knapsack problem, in a minimum number of CPUs and using oversubscription. We design and implement a more efficient version of such mechanism. To that end, we perform the Task Packing “in place”, taking advantage of idle cycles generated at synchronization points of unbalanced applications. Evaluations are carried out on a heterogeneous platform using FT and miniFE benchmarks. Results showed that our proposal generates low overhead. In addition the amount of freed CPUs are related to a load imbalance metric which can be used as a prediction for it.Peer ReviewedPostprint (author's final draft

Optimized dynamic load balancing in distributed exascale computing systems

Dottorato di Ricerca in Ricerca Operativa, Ciclo XXVII,a.a. 2015-2016The dynamic nature of new generation scientific problems needs undergoing review in the traditional and static management of computing resources in Exascale computing systems. Doing so will support dynamic and unpredictable requests of the scientific programs for different type of resources. To achieve this facility, it is necessary to present a dynamic load balancing model to manage the load of the system efficiently based on the requests of the programs. Currently, the distributed Exascale systems with heterogeneous resources are the best branch of distributed computing systems that should be able to support the scientific programs with dynamic and interactive requests to resources. In this thesis, distributed Exascale systems are regarded as the operational and real distributed systems, and the dynamic load balancing model for the distributed controlling of load in the nodes in distributed Exascale computing systems are presented. The dominant paradigm in this model is derived from Operation Research sciences, and the request aware approach is replaced with the command-based approach in managing the load of the system. The results of evaluation show us the significant improvement regarding the performance by using the proposed load balancing mechanism in compare with the common distributed load balancing mechanismsUniversità della Calabri