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Very large scale computing systems, ranging from large clusters to worldwide Global Computing systems, require a high level of fault tolerance in order to efficiently run parallel applications.
For successful and timely results, large scale simulations require the use of large scale computing systems, which often are widely distributed and highly heterogeneous.
Moreover, large scale computing systems are usually shared among multiple users, which causes the quality and quantity of the available resources to be highly unpredictable.
Our focus is on building large scale computing systems to do machine learning and doing advanced machine learning research.
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It is applicable to small-, medium-, and large-scale computing systems and organizations.
But Compaq has not been known as a leader in research -- most of its work has been focused narrowly on large-scale computing systems, the Internet and the proliferation of personal electronic devices beyond the PC.
High-performance scalable interconnects allow us to build large-scale computing systems consisting of tens if not hundreds of computing nodes.
Policy-driven system management or policy-based management (PBM) is a research domain that aims at automatizing the management of large-scale computing systems.
Policy-driven system management or policy-based management (PBM) is a research domain that aims to automate the management of large-scale computing systems.
We have people like myself who have a lot of experience in building large-scale computing systems, and then we have people who are world-class machine learning researchers.
Future research will therefore develop sub-optimal solutions for dynamically recalculating the assignment of processors to RAs and so adapt to varying traffic loads in very large-scale computing systems.
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