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Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right(More)
SpiNNaker is a massively parallel architecture designed to model large-scale spiking neural networks in (biological) real-time. Its design is based around <i>ad-hoc</i> multi-core System-on-Chips which are interconnected using a two-dimensional toroidal triangular mesh. Neurons are modeled in software and their spikes generate packets that propagate through(More)
We discuss a collection of techniques, included in our INSEE simulation environment but applicable to other contexts, designed to do simulation-based performance studies of parallel computing systems using traces. We explain the mechanisms required to capture traces from MPI-based parallel applications, point out some important limitations in the way events(More)
This paper studies the influence that task placement may have on the performance of applications, mainly due to the relationship between communication locality and overhead. This impact is studied for torus and fat-tree topologies. A simulation-based performance study is carried out, using traces of applications and application kernels, to measure the time(More)
Interconnection networks arranged as k-ary n-trees or spines are widely used to build high-performance computing clusters. Current blade-based technology allows the integration of the first level of the network together with the compute elements. The remaining network stages require dedicated rack space. In most systems one or several racks house the upper(More)