Distributed deep learning systems effectively respond to the increasing demand for large-scale data processing in recent years. However, significant investment building distributed with powerful computing nodes places a huge financial burden on developers and researchers. It will be good predict precise benefit, i.e., how many times of speedup it can get compared training single machine (or few...

In recent years, coded distributed computing (CDC) has attracted significant attention, because it can efficiently facilitate many delay-sensitive computation tasks against unexpected latencies in computing systems. Despite such a salient feature, design challenges and opportunities remain. thi...

in the last two decades, the trend in healthcare information technology has been towards digital and multimedia concepts. the next generation of healthcare information systems will consist of a vast network of heterogeneous, autonomous and distributed imaging scanners, hospital information systems (his), medical information from clinical departments such as radiology information systems (ris), ...

The architectures of high-end embedded system have evolved into heterogeneous distributed integrated architectures. The scheduling of multiple distributed mixed-criticality functions in heterogeneous distributed embedded systems is a considerable challenge because of the different requirements of systems and functions. Overall scheduling length (i.e., makespan) is the main concern in system per...