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The cloud brings new possibilities to run traditional HPC applications, giving its flexibility and reduced cost. However, running MPI applications in the cloud can reduce appreciably its performance, because the cloud hides its internal network topology information, and existing topology-aware techniques to optimize MPI communications cannot be directly applied to virtualized infrastructures. In this paper it is presented the MPI-Performance-Aware-Reallocation method (MPAR), a general approach to improve MPI communications. This new approach: (i) is not linked to any specific software or hardware infrastructure, (ii) is applicable to cloud, (iii) abstracts the network topology performing experimental tests, and (iv) is able to improve the performance of the MPI users application via the reallocation of the involved MPI processes. The MPAR has been demonstrated for cloud infrastructures, via the implementation of the Latency-Aware-MPI-Cloud-Scheduler (LAMPICS) layer. LAMPICS is able to improve the latency of MPI communications in clouds, without the need of creating ad-hoc MPI implementations or modifying the source code of user’s MPI applications. We have tested LAMPICS with the Sendrecv micro benchmark provided by the Intel MPI Benchmarks, with performance improvements of up to 70%, and with two real-world applications from the Unified European Applications Benchmark Suite, obtaining performance improvements of up to 26.5%. [ABSTRACT FROM AUTHOR]

SUMMARY Nowadays, parallel architectures are changing so fast that there is a need for scalable and efficient tools to analyze and predict the performance of parallel applications. Analytical models are proved to be a useful approximation for characterizing parallel algorithms, but developing accurate analytical models is a hard issue, and, in general, they provide coarse performance predictions due to their intrinsic lack of accuracy. In this paper, we describe in detail the Tools for Instrumentation and Analysis (TIA) framework, an easy-to-use tool that automatically obtains accurate performance models by means of analytical expressions. This framework automatizes most of its internal tasks, reducing opportunities for human error, and it only requires the user to focus on the metrics and execution parameters that might influence the performance, those that should be considered in the modeling process. Its main advantage over other tools is that TIA uses model selection techniques that allow the automation of the modeling process. As a case of study, the use of TIA to obtain analytical models of different implementations of the broadcast collective communication in a cluster of multicores is shown. The results obtained by TIA are evaluated and compared with theoretical approaches based on the LogGP model. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]