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We characterize a general class of algorithms common in machine learning, scientific computing, and signal processing, whose computational dependencies are both sparse, and dynamically defined throughout execution. Existing parallel computing runtimes, like MapReduce and GraphLab, are a poor fit for this class because they assume statically defined(More)
We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures caused by temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary(More)
—Wireless data transfer under high mobility, as found in unmanned aerial vehicle (UAV) applications, is a challenge due to varying channel quality and extended link outages. We present FlowCode, an easily deployable link-layer solution utilizing multiple transmitters and receivers for the purpose of supporting existing transport protocols such as TCP in(More)
—We apply hierarchical sparse coding, a form of deep learning, to model user-driven workloads based on on-chip hardware performance counters. We then predict periods of low instruction throughput, during which frequency and voltage can be scaled to reclaim power. Using a multi-layer coding structure, our method progressively codes counter values in terms of(More)
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