Hélène Renard

Mapping and load-balancing iterative computations

We consider the mapping of iterative algorithms onto heterogeneous clusters. The application data is partitioned over the processors, which are arranged along a virtual ring. At each iteration, independent calculations are carried out in parallel, and some communications take place between consecutive processors in the ring. The aim is to determine how to slice the application data into chunks, and to assign these chunks to the processors, so that the total execution time is minimized. One major difficulty is to embed a processor ring into a network that typically is not fully connected, so that some communication links have to be shared by several processor pairs. We establish a complexity result that assesses the difficulty of this problem, and we design a practical heuristic that provides efficient mapping, routing, link- sharing, and data distribution schemes.

Static Load-Balancing Techniques for Iterative Computations on Heterogeneous Clusters

This paper is devoted to static load balancing techniques for mapping iterative algorithms onto heterogeneous clusters. The application data is partitioned over the processors. At each iteration, independent calculations are carried out in parallel, and some communications take place. The question is to determine how to slice the application data into chunks, and to assign these chunks to the processors, so that the total execution time is minimized. We establish a complexity result that assesses the difficulty of this problem, and we design practical heuristics that provide efficient distribution schemes.

Data Redistribution Algorithms for Heterogeneous Processor Rings

We consider the problem of redistributing data on homogeneous and heterogeneous rings of processors. The problem arises in several applications, after each invocation of a load-balancing mechanism (but we do not discuss the load-balancing mechanism itself). We provide algorithms that aim at optimizing the data redistribution, both for unidirectional and bidirectional rings. One major contribution of the paper is that we are able to prove the optimality of the proposed algorithms in all cases except that of a bidirectional heterogeneous ring, for which the problem remains open.

Load-Balancing Iterative Computations on Heterogeneous Clusters with Shared Communication Links

We focus on mapping iterative algorithms onto heteroge- neous clusters. The application data is partitioned over the processors, which are arranged along a virtual ring. At each iteration, independent calculations are carried out in parallel, and some communications take place between consecutive processors in the ring. The question is to de- termine how to slice the application data into chunks, and assign these chunks to the processors, so that the total execution time is minimized. A major difficulty is to embed a processor ring into a network that typ- ically is not fully connected, so that some communication links have to be shared by several processor pairs. We establish a complexity result as- sessing the difficulty of this problem, and we design a practical heuristic that provides efficient mapping, routing, and data distribution schemes.

A first step to the evaluation of SimGrid in the context of a real application

Simulation is a ¿simple¿ method to experimentally evaluate the behavior of algorithms designed for parallel and distributed platforms. Moreover, the reliability of the evaluation strongly depends on the models used inside the simulator. This paper is devoted to the study and the evaluation of the behavior of the SimGrid simulator through a comparison between a simulated and a real execution of the same target application (i.e. heat propagation). Our target platforms are heterogeneous and their characteristics may dynamically vary during the execution. The obtained results (on a set of various platforms) show that the behavior observed when using the simulated platform is very close to the one obtained on the real one.

Data redistribution algorithms for homogeneous and heterogeneous processor rings

We consider the problem of redistributing data on homogeneous and heterogeneous processor rings The problem arises in several applications, each time after a load-balancing mechanism is invoked (but we do not discuss the load-balancing mechanism itself) We provide algorithms that aim at optimizing the data redistribution, both for uni-directional and bi-directional rings One major contribution of the paper is that we are able to prove the optimality of the proposed algorithms in all cases except that of a bi-directional heterogeneous ring, for which the problem remains open.

Mapping and Load-Balancing Iterative Computations on Heterogeneous Clusters

This paper is devoted to mapping iterative algorithms onto heterogeneous clusters. The application data is partitioned over the processors, which are arranged along a virtual ring. At each iteration, independent calculations are carried out in parallel, and some communications take place between consecutive processors in the ring. The question is to determine how to slice the application data into chunks, and to assign these chunks to the processors, so that the total execution time is minimized. One major difficulty is to embed a processor ring into a network that typically is not fully connected, so that some communication links have to be shared by several processor pairs. We establish a complexity result that assesses the difficulty of this problem, and we design a practical heuristic that provides efficient mapping, routing, and data distribution schemes.