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Most list scheduling heuristics rely on a simple platform model where communication contention is not taken into account. In addition, it is generally assumed that processors in the systems are completely safe. To schedule precedence graphs in a more realistic framework, we introduce an efficient fault tolerant scheduling algorithm that is both contentionaware and capable of supporting ε arbitrary fail-silent (fail-stop) processor failures. We focus on a bi-criteria approach, where we aim at minimizing the total execution time, or latency, given a fixed number of failures supported in the system. Our algorithm has a low time complexity, and drastically reduces the number of additional communications induced by the replication mechanism. Experimental results fully demonstrate the usefulness of the proposed algorithm, which leads to efficient execution schemes while guaranteeing a prescribed level of fault tolerance.

In this paper, we deal with the problem of scheduling independent tasks on heterogeneous master-slave platforms. We target both off-line and on-line problems, with several objective functions (makespan, maximum response time, total completion time). On the theoretical side, our results are two-fold: (i) For off-line scheduling, we prove several optimality results for problems with release dates; (ii) For on-line scheduling, we establish lower bounds on the competitive ratio of any deterministic algorithm. On the practical side, we have implemented several heuristics, some classical and some new ones derived in this paper, on a small but fully heterogeneous MPI platform. Our results show the superiority of those heuristics which fully take into account the relative capacity of the communication links.

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Presented are two new approximation algorithms for solving two different NP-hard graph problems. The first is the minimum k-cut problem. The new algorithm runs within the same time complexity as the current best-known algorithm and may have equal or better performance given any input. The second new algorithm uses the k-cut algorithm to approximate a solution to the parallel task scheduling problem. Both of these problems have important applications in the real world. Also discussed are the algorithms that lead up to the new ones, along with their respective applications. 1.

We consider the execution of a complex application on a heterogeneous “grid ” computing platform. The complex application consists of a suite of identical, independent problems to be solved. In turn, each problem consists of a set of tasks. There are dependences (precedence constraints) between these tasks and these dependences are organized as a tree. A typical example is the repeated execution of the same algorithm on several distinct data samples. We use a non-oriented graph to model the grid platform, where resources have different speeds of computation and communication. We show how to determine the optimal steady-state scheduling strategy for each processor (the fraction of time spent computing and the fraction of time spent communicating with each neighbor). This result holds for a quite general framework, allowing for cycles and multiple paths in the platform graph.

Assessing the impact and limits of steady-state scheduling for mixed task and data parallelism on heterogeneous platforms