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The continuous increase in performance requirements, for both scientific computation and industry, motivates the need of a powerful computing infrastructure. The Grid appeared as a solution for inexpensive execution of heavy applications in a parallel and distributed manner. It allows combining resources independently of their physical location and architecture to form a global resource pool available to all grid users. However, grid environments are highly unstable and unpredictable. Adaptability is a crucial issue in this context, in order to guarantee an appropriate quality of service to users. Migration is a technique frequently used for achieving adaptation. The objective of this report is to survey the problem of strong migration in heterogeneous environments like the grids, the related implementation issues and the current solutions.
We present and explore a model of stateless and self-stabilizing distributed computation, inspired by real-world applications such as routing on todays Internet. Processors in our model do not have an internal state, but rather interact by repeatedly
A critical challenge for modern system design is meeting the overwhelming performance, storage, and communication bandwidth demand of emerging applications within a tightly bound power budget. As both the time and power, hence the energy, spent in da
Heterogeneous systems have become one of the most common architectures today, thanks to their excellent performance and energy consumption. However, due to their heterogeneity they are very complex to program and even more to achieve performance port
This work presents a heterogeneous communication library for clusters of processors and FPGAs. This library, Shoal, supports the Partitioned Global Address Space (PGAS) memory model for applications. PGAS is a shared memory model for clusters that cr
Modern deep learning systems like PyTorch and Tensorflow are able to train enormous models with billions (or trillions) of parameters on a distributed infrastructure. These systems require that the internal nodes have the same memory capacity and com