Optimizing latency and reliability of pipeline workflow applications

(ENG) Mapping applications onto heterogeneous platforms is a difficult challenge, even for simple application patterns such as pipeline graphs. Theproblem is even more complex when processors are subject to failureduring the execution of the application. In this paper, we study thecomplexity of a bi-criteria mapping which aims at optimizing the la-tency (i.e., the response time) and the reliability (i.e., the probabilitythat the computation will be successful) of the application. Latency isminimized by using faster processors, while reliability is increased byreplicating computations on a set of processors. However, replicationincreases latency (additional communications, slower processors). Theapplication fails to be executed only if all the processors fail duringexecution. While simple polynomial algorithms can be found for fullyhomogeneous platforms, the problem becomes NP-hard when tacklingheterogeneous platforms. This is yet another illustration of the additional complexity added by heterogeneity.L’ordonnancement et l’allocation des applications sur plates-formes hétérogènes sont des problèmes cruciaux, même pour des applications simples comme des graphes en pipeline. Le problème devient même encore plus complexe quand les processeurs peuvent tomber en panne pendant l’exécution de l’application. Dans cet article, nous étudions la complexité d’une allocation bi-critère qui vise à optimiser la latence (i.e., le temps de réponse) et la fiabilité (i.e., la probabilité que le calcul réussisse)de l’application. La latence est minimisée en utilisant des processeurs rapides, tandis que la fiabilité est augmentée en répliquant les calculs sur un ensemble de processeurs. Toutefois, la réplication augmente la latence (communications additionnelles et processeurs moins rapides). L’application échoue à être exécutée seulement si tout les processeurs échouent pendant l’exécution. Des algorithmes simples en temps polynomial peuvent être trouvés pour plates-formes complètement homogènes,tandis que le problème devient NP-dur quand on s’attaque aux plates-formes hétérogènes. C’est encore une autre illustration de la complexité additionnelle due à l’hétérogénéité

Complexity results for throughput and latency optimization of replicated and data-parallel workflows

International audienceMapping applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline or fork graphs. Several antagonist criteria should be optimized for workflow applications, such as throughput and latency (or a combination). In this paper, we consider a simplified model with no communication cost, and we provide an exhaustive list of complexity results for different problem instances. Pipeline or fork stages can be replicated in order to increase the throughput by sending consecutive data sets onto different processors. In some cases, stages can also be data-parallelized, i.e. the computation of one single data set is shared between several processors. This leads to a decrease of the latency and an increase of the throughput. Some instances of this simple model are shown to be NP-hard, thereby exposing the inherent complexity of the mapping problem. We provide polynomial algorithms for other problem instances. Altogether, we provide solid theoretical foundations for the study of mono-criterion or bi-criteria mapping optimization problems

Complexity results for throughput and latency optimization of replicated and data-parallel workflows

Mapping applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline or fork graphs. Several antagonist criteria should be optimized for workflow applications, such as throughput and latency (or a combination). In this paper, we consider a simplified model with no communication cost, and we provide an exhaustive list of complexity results for different problem instances. Pipeline or fork stages can be replicated in order to increase the throughput of the workflow, by sending consecutive data sets onto different processors. In some cases, stages can also be data-parallelized, i.e. the computation of one single data set is shared between several processors. This leads to a decrease of the latency and an increase of the throughput. Some instances of this simple model are shown to be NP-hard, thereby exposing the inherent complexity of the mapping problem. We provide polynomial algorithms for other problem instances. Altogether, we provide solid theoretical foundations for the study of mono-criterion or bi-criteria mapping optimization problems

Complexity results for throughput and latency optimization of replicated and data-parallel workflows.

(eng) Mapping applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline or fork graphs. Several antagonist criteria should be optimized for workflow applications, such as throughput and latency (or a combination). In this paper, we consider a simplified model with no communication cost, and we provide an exhaustive list of complexity results for different problem instances. Pipeline or fork stages can be replicated in order to increase the throughput of the workflow, by sending consecutive data sets onto different processors. In some cases, stages can also be data-parallelized, i.e. the computation of one single data set is shared between several processors. This leads to a decrease of the latency and an increase of the throughput. Some instances of this simple model are shown to be NP-hard, thereby exposing the inherent complexity of the mapping problem. We provide polynomial algorithms for other problem instances. Altogether, we provide solid theoretical foundations for the study of mono-criterion or bi-criteria mapping optimization problems