Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two...
High-throughput real-time systems require non-standard and costly hardware and software solutions. Modern workstation can represent a credible alternative to develop realtime inte...
Many communication-centred systems today rely on asynchronous messaging among distributed peers to make efficient use of parallel execution and resource access. With such asynchron...
—Virtual machines offer unique advantages to the scientific computing community, such as Quality of Service(QoS) guarantee, performance isolation, easy resource management, and ...
Lizhe Wang, Gregor von Laszewski, Marcel Kunze, Ji...
In order to guarantee that real-time systems meet their timing specification, static execution time bounds need to be calculated. Not considering execution time predictability led...
Benedikt Huber, Wolfgang Puffitsch, Martin Schoebe...