Grégory Mounié

A

A malleable task is a computational unit that may be executed on any arbitrary number of processors, whose execution time depends on the amount of resources allotted to it. This paper presents a new approach for scheduling a set of independent malleable tasks which leads to a worst case guarantee of

\frac32

\frac{3}{2}+\varepsilon

-Approximation Algorithm for Scheduling Independent Monotonic Malleable Tasks

for the minimization of the parallel execution time for any fixed

Bi-criteria algorithm for scheduling jobs on cluster platforms

\varepsilon > 0

Analysis of Scheduling Algorithms with Reservations

. The main idea of this approach is to focus on the determination of a good allotment and then to solve the resulting problem with a fixed number of processors by a simple scheduling algorithm. The first phase is based on a dual approximation technique where the allotment problem is expressed as a knapsack problem for partitioning the set of tasks into two shelves of respective heights

Scheduling independent tasks on multi-cores with GPU accelerators

1

An approximation algorithm for scheduling trees of malleable tasks

and

Approximation Algorithms for Scheduling Independent Malleable Tasks

\frac{1}{2}

Scheduling heuristics for efficient broadcast operations on grid environments

.

Dynamic Load Balancing for Ocean Circulation Model with Adaptive Meshing

We describe in this paper a new method for building an efficient algorithm for scheduling jobs in a cluster. Jobs are considered as parallel tasks (PT) which can be scheduled on any number of processors. The main feature is to consider two criteria that are optimized together. These criteria are the makespan and the weighted minimal average completion time (minsum). They are chosen for their complementarity, to be able to represent both user-oriented objectives and system administrator objectives.We propose an algorithm based on a batch policy with increasing batch sizes, with a smart selection of jobs in each batch. This algorithm is assessed by intensive simulation results, compared to a new lower bound (obtained by a relaxation of ILP) of the optimal schedules for both criteria separately. It is currently implemented in an actual real-size cluster platform.