Laura Cristina De Giusti

Dynamic load balancing on non-homogeneous clusters

This paper discusses the dynamic and static balancing of non-homogenous cluster architectures, simultaneously analyzing the theoretical parallel speedup as well as the speedup experimentally obtained. n nA classical application (Parallel N-Queens) with a parallel solution algorithm, where processing predominates upon communication, has been chosen so as to go deep in the load balancing aspects (dynamic or static) without distortion of results caused by communication overhead. n nFour interconnected clusters have been used in which the machines within each cluster have homogeneous processors although different among clusters. Thus, the set can be seen as a N-processor heterogeneous cluster or as a multi-cluster scheme with 4 subsets of homogeneous processors. n nAt the same time, three forms of load distribution in the processors (Direct Static, Predictive Static and Dynamic by Demand) have been studied, analyzing in each case parallel speedup and load unbalancing regarding problem size and the processors used.

AMTHA: An Algorithm for Automatically Mapping Tasks to Processors in Heterogeneous Multiprocessor Architectures

An automatic task-to-processor mapping algorithm is analyzed in parallel systems that run over loosely coupled distributed architectures.The MPAHA (Model on Parallel Algorithms on Heterogeneous Architectures) model that allows predicting parallel application performance running over heterogeneous architectures is presented. In particular, the heterogeneity of both processors and communications is taken into consideration.From the results obtained with the MPAHA model, the AMTHA (Automatic Mapping Task on Heterogeneous Architectures) algorithm for task-to-processors assignment is presented and its implementation is analyzed.Experimental results compare executionxa0xa0time obtainedxa0xa0with AMTHA mapping scheme with those obtained usingxa0xa0the known mapping algorithm HEFT (Heterogeneous – Earliest Finish – Time), using a simple heterogeneous multicluster architecture.Finally actual lines of research are presented, focusing extensions to multicore processors and Grid environments.

ECMRE: Extended Concurrent Multi Robot Environment

ECMRE is an extension of CMRE (Concurrent Multi Robot Environment) that adds features related to current parallel architectures: processor heterogeneity, energy consumption, processor speed change techniques in relation to temperature and/or energy consumption.

Analysis of RAPL Energy Prediction Accuracy in a Matrix Multiplication Application on Shared Memory

In recent years, energy consumption has emerged as one of the biggest issues in the development of HPC applications. The traditional approach of parallel and distributed computing has changed its perspective from looking for greater computational efficiency to an approach that balances performance with energy consumption. As a consequence, different metrics and measurement mechanisms have been implemented to achieve this balance. The objective of this article focuses on monitoring and analyzing energy consumption for a given application through physical measurements and a software interface based on hardware counters. A comparison of the energy values gathered by Intel RAPL versus physical measurements obtained through the processor power source is presented. These measurements are applied during the execution of a classic matrix multiplication application. Our results show that, for the application being considered, the average power required by the processor has an error of up to 22% versus the values predicted by RAPL.