Section: New Results

Management of Parallel Architectures

• In [12] , we present a topology-aware load balancing algorithm for parallel multi-core machines and its proof of asymptotic convergence to an optimal solution. The algorithm, named HwTopoLB, takes into account the properties of current parallel systems composed of multi-core compute nodes, namely their network interconnection, and their complex and hierarchical core topology. We have implemented HwTopoLB using the Charm++ Parallel Runtime System and evaluated its performance with two different benchmarks and one application. Our experimental results confirms that HwTopoLB outperform existing load balancing strategies on different multi-core systems.

• Large scale distributed systems typically comprise hundreds to millions of entities that have only a partial view of resources. How to fairly and efficiently share such resources between entities in a distributed way has thus become a critical question. In [31] , we develop a possible answer based on Lagrangian optimization and distributed gradient descent. Under certain conditions, the resource sharing problem can be formulated as a global optimization problem, which can be solved by a distributed self-stabilizing demand and response algorithm.

• The management of resources on testbeds, including their description, reservation and verification, is a challenging issue, especially on of large scale testbeds such as those used for research on High Performance Computing or Clouds. In [23] , we present the solution designed for the Grid'5000 testbed in order to: (1) provide users with an in-depth and machine-parsable description of the testbed's resources; (2) enable multi-criteria selection and reservation of resources using a HPC resource manager; (3) ensure that the description of the resources remains accurate. In [24] , we present Kascade, a solution for the broadcast of data to a large set of compute nodes. We evaluate Kascade using a set of large scale experiments in a variety of experimental settings, and show that Kascade: (1) achieves very high scalability by organizing nodes in a pipeline; (2) can almost saturate a 1 Gbit/s network, even at large scale; (3) handles failures of nodes during the transfer seamlessly because of its fault-tolerant design.