<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rosa Filgueira</style></author><author><style face="normal" font="default" size="100%">David E. Singh</style></author><author><style face="normal" font="default" size="100%">Alejandro Calderón</style></author><author><style face="normal" font="default" size="100%">Félix García Carballeira</style></author><author><style face="normal" font="default" size="100%">Jesús Carretero</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive CoMPI: Enhancing MPI based applications performance and scalability by using adaptive compression.</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of High Performance Computing and Applications, 2010. Sage</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><abstract><style face="normal" font="default" size="100%">This paper presents an optimization of MPI communication, called Adaptive-CoMPI, based on runtime compression of MPI messages exchanged by applications. The technique developed can be used for any application, because its implementation is transparent for the user, and integrates different compression algorithms for both MPI collective and point-to-point primitives. Furthermore, compression is turned on and off and the most appropriate compression algorithms are selected at runtime, depending on the characteristics of each message, the network behavior, and compression algorithm behavior, following a runtime adaptive strategy. Our system can be optimized for a specific application, through a guided strategy, to reduce the runtime strategy overhead. Adaptive-CoMPI has been validated using several MPI benchmarks and real HPC applications. Results show that, in most cases, by using adaptive compression, communication time is reduced, enhancing application performance and scalability.</style></abstract><issue><style face="normal" font="default" size="100%">25 (3)</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rosa Filgueira</style></author><author><style face="normal" font="default" size="100%">Jesús Carretero</style></author><author><style face="normal" font="default" size="100%">David E. Singh</style></author><author><style face="normal" font="default" size="100%">Alejandro Calderón</style></author><author><style face="normal" font="default" size="100%">Alberto Nunez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic-CoMPI: Dynamic optimization techniques for MPI parallel applications.</style></title><secondary-title><style face="normal" font="default" size="100%">The Journal of Supercomputing.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptive systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Clusters architectures</style></keyword><keyword><style  face="normal" font="default" size="100%">Collective I/O</style></keyword><keyword><style  face="normal" font="default" size="100%">Compression algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Heuristics</style></keyword><keyword><style  face="normal" font="default" size="100%">MPI library - Parallel techniques</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2010</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><abstract><style face="normal" font="default" size="100%">This work presents an optimization of MPI communications, called Dynamic-CoMPI, which uses two techniques in order to reduce the impact of communications and non-contiguous I/O requests in parallel applications. These techniques are independent of the application and complementaries to each other. The first technique is an optimization of the Two-Phase collective I/O technique from ROMIO, called Locality aware strategy for Two-Phase I/O (LA-Two-Phase I/O). In order to increase the locality of the file accesses, LA-Two-Phase I/O employs the Linear Assignment Problem (LAP) for finding an optimal I/O data communication schedule. The main purpose of this technique is the reduction of the number of communications involved in the I/O collective operation. The second technique, called Adaptive-CoMPI, is based on run-time compression of MPI messages exchanged by applications. Both techniques can be applied on every application, because both of them are transparent for the users. Dynamic-CoMPI has been validated by using several MPI benchmarks and real HPC applications. The results show that, for many of the considered scenarios, important reductions in the execution time are achieved by reducing the size and the number of the messages. Additional benefits of our approach are the reduction of the total communication time and the network contention, thus enhancing, not only performance, but also scalability.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rosa Filgueira</style></author><author><style face="normal" font="default" size="100%">David E. Singh</style></author><author><style face="normal" font="default" size="100%">Alejandro Calderón</style></author><author><style face="normal" font="default" size="100%">Jesús Carretero</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CoMPI: Enhancing MPI Based Applications Performance and Scalability Using Run-Time Compression.</style></title><secondary-title><style face="normal" font="default" size="100%">EUROPVM/MPI 2009.Espoo, Finland. September 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/09/2009</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Espoo. Finland</style></pub-location><volume><style face="normal" font="default" size="100%">5759/2009</style></volume><abstract><style face="normal" font="default" size="100%">This paper presents an optimization of MPI communications, called CoMPI, based on run-time compression of MPI messages exchanged by applications. A broad number of compression algorithms have been fully implemented and tested for both MPI collective and point to point primitives. In addition, this paper presents a study of several compression algorithms that can be used for run-time compression, based on the datatype used by applications. This study has been validated by using several MPI benchmarks and real HPC applications. Show that, in most of the cases, using compression reduces the application communication time enhancing application performance and scalability. In this way, CoMPI obtains important improvements in the overall execution time for many of the considered scenarios.</style></abstract></record></records></xml>