Spark (mathematics)

About: Spark (mathematics) is a research topic. Over the lifetime, 7304 publications have been published within this topic receiving 63322 citations.

Mass video semantic annotation method based on Spark

Abstract: The invention provides a mass video semantic annotation method based on Spark. The method is mainly based on elastic distributed storage of mass video under a Hadoop big data cluster environment and adopts a Spark computation mode to conduct video annotation. The method mainly comprises the following contents: a video segmentation method based on a fractal theory and realization thereof on Spark; a video feature extraction method based on Spark and a visual word forming method based on a meta-learning strategy; a video annotation generation method based on Spark. Compared with the traditional single machine computation, parallel computation or distributed computation, the mass video semantic annotation method based on Spark can improve the computation speed by more than a hundred times and has the advantages of complete annotation content information, low error rate and the like.

Snailtrail: generalizing critical paths for online analysis of distributed dataflows

Abstract: We rigorously generalize critical path analysis (CPA) to long-running and streaming computations and present SnailTrail, a system built on Timely Dataflow, which applies our analysis to a range of popular distributed dataflow engines. Our technique uses the novel metric of critical participation, computed on time-based snapshots of execution traces, that provides immediate insights into specific parts of the computation. This allows SnailTrail to work online in real-time, rather than requiring complete offline traces as with traditional CPA. It is thus applicable to scenarios like model training in machine learning, and sensor stream processing. SnailTrail assumes only a highly general model of dataflow computation (which we define) and we show it can be applied to systems as diverse as Spark, Flink, TensorFlow, and Timely Dataflow itself. We further show with examples from all four of these systems that SnailTrail is fast and scalable, and that critical participation can deliver performance analysis and insights not available using prior techniques.

Operation of sliding-spark arrays for laser pre-ionization

Abstract: The electrical conditions necessary for reliable operation of sliding-spark arrays have been determined The emission from an array can be considerably enhanced by critically damping the spark discharge circuit When used to pre-ionize a CO2 laser there must be a delay between the array and main discharge pulses, but this delay may be as small as 100 ns and less than the array discharge pulse width

PBSE: a robust path-based speculative execution for degraded-network tail tolerance in data-parallel frameworks

Abstract: We reveal loopholes of Speculative Execution (SE) implementations under a unique fault model: node-level network throughput degradation. This problem appears in many data-parallel frameworks such as Hadoop MapReduce and Spark. To address this, we present PBSE, a robust, path-based speculative execution that employs three key ingredients: path progress, path diversity, and path-straggler detection and speculation. We show how PBSE is superior to other approaches such as cloning and aggressive speculation under the aforementioned fault model. PBSE is a general solution, applicable to many data-parallel frameworks such as Hadoop/HDFS+QFS, Spark and Flume.