6,071 research outputs found
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Architectural Implications of GNN Aggregation Programming Abstractions
Graph neural networks (GNNs) have gained significant popularity due to the
powerful capability to extract useful representations from graph data. As the
need for efficient GNN computation intensifies, a variety of programming
abstractions designed for optimizing GNN Aggregation have emerged to facilitate
acceleration. However, there is no comprehensive evaluation and analysis upon
existing abstractions, thus no clear consensus on which approach is better. In
this letter, we classify existing programming abstractions for GNN Aggregation
by the dimension of data organization and propagation method. By constructing
these abstractions on a state-of-the-art GNN library, we perform a thorough and
detailed characterization study to compare their performance and efficiency,
and provide several insights on future GNN acceleration based on our analysis.Comment: 4 pages, to be published in IEEE Computer Architecture Letters (CAL
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A decision support environment for behavioral synthesis
We present a specification of a general environment for behavioral synthesis centered around the user/designer as the primary motivator for decisions in design development. At each stage of the design process, the user can perform transformations on the design description through graphical user interfaces. Quality measures, physical estimates, and design hints are given to the user at each stage
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