Malleable task-graph scheduling with a practical speed-up model

Scientific workloads are often described by Directed Acyclic task Graphs.Indeed, DAGs represent both a model frequently studied in theoretical literature and the structure employed by dynamic runtime schedulers to handle HPC applications. A natural problem is then to compute a makespan-minimizing schedule of a given graph. In this paper, we are motivated by task graphs arising from multifrontal factorizations of sparsematrices and therefore work under the following practical model. We focus on malleable tasks (i.e., a single task can be allotted a time-varying number of processors) and specifically on a simple yet realistic speedup model: each task can be perfectly parallelized, but only up to a limited number of processors. We first prove that the associated decision problem of minimizing the makespan is NP-Complete. Then, we study a widely used algorithm, PropScheduling, under this practical model and propose a new strategy GreedyFilling. Even though both strategies are 2-approximations, experiments on real and synthetic data sets show that GreedyFilling achieves significantly lower makespans

Bridging a Gap Between Research and Production: Contributions to Scheduling and Simulation

Large scale distributed computing infrastructures (e.g., data centers, grids, or clouds) are used by scientists from various domains to produce outstanding research results, such as the discovery of the Higgs Boson in High Energy Physics. These infrastructures are also studied by Computer Scientists to produce their own set of scientific results. Ideally, a virtuous circle should exist between Domain and Computer Scientists: the former raising challenges that could be addressed by the latter. Unfortunately, in many occasions, a gap exists that prevents such an ideal and fostering collaboration. This habilitation covers research works conducted in the fields of scheduling and simulation that contribute to the filling of this gap. It discusses the necessary conditions to achieve this goal and details concrete initiatives in this endeavor

Ordonnancement parallèle de DAGs sous contraintes mémoire

Scientific workflows are frequently modeled as Directed Acyclic Graphs (DAG) oftasks, which represent computational modules and their dependencies, in the form of dataproduced by a task and used by another one. This formulation allows the use of runtime sys-tems which dynamically allocate tasks onto the resources of increasingly complex and hetero-geneous computing platforms. However, for some workflows, such a dynamic schedule mayrun out of memory by exposing too much parallelism. This paper focuses on the problem oftransforming such a DAG to prevent memory shortage, and concentrates on shared memoryplatforms. We first propose a simple model of DAG which is expressive enough to emulate com-plex memory behaviors. We then exhibit a polynomial-time algorithm that computes the max-imum peak memory of a DAG, that is, the maximum memory needed by any parallel schedule.We consider the problem of reducing this maximum peak memory to make it smaller than agiven bound by adding new fictitious edges, while trying to minimize the critical path of thegraph. After proving this problem NP-complete, we provide an ILP solution as well as severalheuristic strategies that are thoroughly compared by simulation on synthetic DAGs modelingactual computational workflows. We show that on most instances, we are able to decrease themaximum peak memory at the cost of a small increase in the critical path, thus with little im-pact on quality of the final parallel schedule.Les applications de calcul scientifique sont souvent modélisées pardes graphes de tâches orientés acycliques (DAG), qui représentent les tâchesde calcul et leurs dépendances, sous la forme de données produites par unetâche et utilisées par une autre. Cette formulation permet l’utilisation d’APIqui allouent dynamiquement les tâches sur les ressources de plateformes decalcul hétérogènes de plus en plus complexes. Cependant, pour certaines ap-plications, un tel ordonnancement dynamique peut manquer de mémoire enexploitant trop de parallélisme. Cet article porte sur le problème consistant àtransformer un tel DAG pour empêcher toute pénurie de mémoire, en se con-centrant sur les plateformes à mémoire partagée. On propose tout d’abord unmodèle simple de graphe qui est assez expressif pour émuler des comporte-ments mémoires complexes. On expose ensuite un algorithme polynomial quicalcule le pic mémoire maximum d’un DAG, qui représente la mémoire maxi-male requise par tout ordonnancement parallèle. On considère ensuite le prob-lème consistant à réduire ce pic mémoire maximal pour qu’il devienne plus pe-tit qu’une borne donnée en rajoutant des arêtes fictives, tout en essayant deminimiser le chemin critique du graphe. Après avoir prouvé ce problème NP-complet, on fournit un programme linéaire en nombres entiers le résolvant,ainsi que plusieurs stratégies heuristiques qui sont minitieusement comparées-sur des graphes synthétiques modélisant des applications de calcul réelles. Onmontre que sur la plupart des instances, on arrive à diminuer le pic mémoiremaximal, au prix d’une légère augmentation du chemin critique, et donc avecpeu d’impact sur la qualité de l’ordonnancement parallèle final

Real-Time Wireless Sensor-Actuator Networks for Cyber-Physical Systems

A cyber-physical system (CPS) employs tight integration of, and coordination between computational, networking, and physical elements. Wireless sensor-actuator networks provide a new communication technology for a broad range of CPS applications such as process control, smart manufacturing, and data center management. Sensing and control in these systems need to meet stringent real-time performance requirements on communication latency in challenging environments. There have been limited results on real-time scheduling theory for wireless sensor-actuator networks. Real-time transmission scheduling and analysis for wireless sensor-actuator networks requires new methodologies to deal with unique characteristics of wireless communication. Furthermore, the performance of a wireless control involves intricate interactions between real-time communication and control. This thesis research tackles these challenges and make a series of contributions to the theory and system for wireless CPS. (1) We establish a new real-time scheduling theory for wireless sensor-actuator networks. (2) We develop a scheduling-control co-design approach for holistic optimization of control performance in a wireless control system. (3) We design and implement a wireless sensor-actuator network for CPS in data center power management. (4) We expand our research to develop scheduling algorithms and analyses for real-time parallel computing to support computation-intensive CPS