Topic
Spark (mathematics)
About: Spark (mathematics) is a research topic. Over the lifetime, 7304 publications have been published within this topic receiving 63322 citations.
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TL;DR: An alternative systematic methodology for parameter tuning is proposed, which can be easily applied onto any computing infrastructure and is shown to yield comparable if not better results than the initial one when applied to MN3; observed speedups in the validating test case studies start from 20%.
56 citations
TL;DR: In this article, major developments in the understanding of the physics and chemistry of the atmospheric pressure spark discharge are presented and commented upon, including work in the areas of equipment, initial gap breakdown, spark channel formation, electrode sampling phenomena, sample propagation phenomena, excited state production, related plasma physics, and counter electrode phenomena.
Abstract: Major developments in the understanding of the physics and chemistry of the atmospheric pressure spark discharge are presented and commented upon. These include work in the areas of equipment, initial gap breakdown, spark channel formation, electrode sampling phenomena, sample propagation phenomena, excited state production, related plasma physics, and counter electrode phenomena.
56 citations
25 Oct 2015
TL;DR: This paper presents a complete architecture which fully fits into the Big Data paradigm and so enables interactive visualization of heatmaps at ultra-scale and an adaptive GPU based method for kernel density estimation is proposed.
Abstract: Point set visualization is required in lots of visualization techniques. Scatter plots as well as geographic heat-maps are straightforward examples. Data analysts are now well trained to use such visualization techniques. The availability of larger and larger datasets raises the need to make these techniques scale as fast as the data grows. The Big Data Infrastructure offers the possibility to scale horizontally. Designing point set visualization methods that fit into that new paradigm is thus a crucial challenge. In this paper, we present a complete architecture which fully fits into the Big Data paradigm and so enables interactive visualization of heatmaps at ultra-scale. A new distributed algorithm for multi-scale aggregation of point set is given and an adaptive GPU based method for kernel density estimation is proposed. A complete prototype working with Hadoop, HBase, Spark and WebGL has been implemented. We give a benchmark of our solution on a dataset having more than 2 billion points.
56 citations
TL;DR: This paper discusses a possible approach to the control of this type of drive-train structures for a specific operating condition ("high-power regime") using feedback linearization and a "kick-down"-controller.
Abstract: Replacing conventional gear-boxes with continuously variable transmissions (CVT's) can reduce the fuel-consumption of spark ignition engines significantly. A possible approach to the control of this type of drive-train structures for a specific operating condition ("high-power regime") is discussed in this paper. In the first part the plant dynamics are exactly linearized over the complete operating range using feedback linearization. Much attention is paid to the existence conditions for this nonlinear part. In the second part, as an application of the exact linearization approach, a "kick-down"-controller is designed. Simulations show that combining the two controllers yields good transient behavior and robustness of the closed-loop system. >
55 citations
11 Dec 2017
TL;DR: An online stratified reservoir sampling algorithm to produce approximate output with rigorous error bounds is designed and can be applied to two prominent types of stream processing systems: (1) batched stream processing such as Apache Spark Streaming, and (2) pipelined stream processingsuch as Apache Flink.
Abstract: Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing --- based on the chosen sample size --- can make a systematic trade-off between the output accuracy and computation efficiency. Unfortunately, the state-of-the-art systems for approximate computing primarily target batch analytics, where the input data remains unchanged during the course of computation. Thus, they are not well-suited for stream analytics. This motivated the design of StreamApprox--- a stream analytics system for approximate computing. To realize this idea, we designed an online stratified reservoir sampling algorithm to produce approximate output with rigorous error bounds. Importantly, our proposed algorithm is generic and can be applied to two prominent types of stream processing systems: (1) batched stream processing such as Apache Spark Streaming, and (2) pipelined stream processing such as Apache Flink. To showcase the effectiveness of our algorithm, we implemented StreamApprox as a fully functional prototype based on Apache Spark Streaming and Apache Flink. We evaluated StreamApprox using a set of microbenchmarks and real-world case studies. Our results show that Spark- and Flink-based StreamApprox systems achieve a speedup of 1.15×---3× compared to the respective native Spark Streaming and Flink executions, with varying sampling fraction of 80% to 10%. Furthermore, we have also implemented an improved baseline in addition to the native execution baseline --- a Spark-based approximate computing system leveraging the existing sampling modules in Apache Spark. Compared to the improved baseline, our results show that StreamApprox achieves a speedup of 1.1×---2.4× while maintaining the same accuracy level.
55 citations