Grant: Alliance Grants

The communication and computing infrastructure is getting ever more sophisticated at an extremely rapid pace. Recent applications include 5G connected mobile devices, autonomous cars, smart robots and intelligent digital assistants powered by Machine Learning. These advances are made possible by a number of technological developments at the hardware and software levels, such as computer central processing units with tens of cores, coprocessors for graphics and intensive computations (GPGPU) with thousands of cores and over 18 billion logic elements, 5G low latency high speed networking, and Cloud based infrastructures that execute their requests in parallel.As a result, even a simple operation such as initiating a phone call, making a Web search, routing a packet or displaying a video frame, can involve many parallel cores on more than one processing unit, possibly on several servers. Moreover, the same operation, a few seconds later, may be served in a different way by different cores and physical servers in the Cloud. Therefore, understanding the performance of these operations has become extremely difficult and the tools for that purpose are severely lacking. In this project, the tracing, profiling, debugging and monitoring tools for High Performance Distributed Systems will be extended to efficiently extract information from all units in all layers, from the hardware to the applications, and cope with the large number of cores and computers. The project has a specific focus on Cloud applications connecting to mobile and Internet of Things devices through Edge servers and 5G networks, High Performance Computing exploiting the new generation of shared memory GPGPUs, Machine Learning applications, and a new modular architecture for more integrated software development tools. As a result, the designers and operators of High Performance Distributed Systems will have the tools in hand to quickly analyse their system performance, automatically or manually find problems, and optimise operations.