Modern computational platforms are characterized by the heterogeneity of their processing elements. Additionally, there are many algorithms which can be structured as a set of procedures or tasks with different computational cost. Balancing the computational load among the available processing elements is one of the main keys for the optimal exploitation of such heterogeneous platforms. When the processing time of any procedure executed on any of the available processing elements is known, this workload-balancing problem can be modeled as the well-known scheduling on unrelated parallel machines problem. Solving this type of problems is a big challenge due to the high heterogeneity on both, the tasks and the machines. In this paper, the balancing problem has been formally defined as a global optimization problem which minimizes the makespan (parallel runtime) and a heuristic based on a Genetic Algorithm, called Genetic Scheduler (GenS), has been developed to solve it. In order to analyze the behavior of GenS for several heterogeneous clusters, an example taken from the field of statistical mechanics has been considered as a case study: an active microrheology model. Given this type of problem and a heterogeneous cluster, we seek to minimize the total runtime to extend and analyze in depth the case of study. In such context, a task consists of the simulation of a tracer particle pulled into a cubic box with smaller bath particles. The computational load depends on the total number of the bath particles. Moreover, GenS has been compared to other dynamic and static scheduling approaches. The experimental results of such a comparison show that GenS outperforms the rest of the tested alternatives achieving a better distribution of the computational workload on a heterogeneous cluster. So, the scheduling strategy developed in this paper is of potential interest for any application which requires the execution of many tasks of different duration (a priori known) on a heterogeneous cluster.

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解决无关并行机调度问题：以主动微流变学为例

现代计算平台的特点是其处理元素的异质性。此外，有许多算法可以构建为一组具有不同计算成本的过程或任务。平衡可用处理元素之间的计算负载是优化开发此类异构平台的主要关键之一。当在任何可用处理元素上执行的任何过程的处理时间已知时，此工作负载平衡问题可以建模为众所周知的无关并行机上的调度问题。由于任务和机器的高度异质性，解决这类问题是一个巨大的挑战。在本文中，平衡问题已被正式定义为一个全局优化问题，它最小化 makespan（并行运行时间），并开发了一种基于遗传算法的启发式算法，称为遗传调度程序 (GenS)，以解决该问题。为了分析几个异构集群的 GenS 行为，我们将统计力学领域的一个例子作为案例研究：一个活跃的微流变模型。鉴于此类问题和异构集群，我们寻求最小化总运行时间以扩展和深入分析研究案例。在这种情况下，任务包括模拟示踪粒子被拉入具有较小浴粒子的立方体。计算负荷取决于浴液颗粒的总数。而且，GenS 已与其他动态和静态调度方法进行了比较。这种比较的实验结果表明，GenS 优于其他测试替代方案，在异构集群上实现了计算工作负载的更好分布。因此，本文开发的调度策略对于需要在异构集群上执行许多不同持续时间（先验已知）的任务的任何应用程序都具有潜在意义。