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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CERN1
TL;DR: In this paper, it was shown that by measuring the delay between the arrival of the signals following a spark at two opposite ends of the electrodes, the initial position of the spark can be determined.
23 citations
TL;DR: The feasibility of incorporating Field‐Programmable Gate Array (FPGA) acceleration into Spark is demonstrated and the performance benefits and bottlenecks of the FPGA‐accelerated Spark environment are presented to show that acceleration is possible even when using a hardware platform that is not well optimized for performance.
Abstract: Summary
Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform-independent, high-abstraction programming paradigm for large-scale data processing by leveraging the Java framework. Though it provides software portability across various machines, Java also limits the performance of distributed environments, such as Spark. While it may be unrealistic to rewrite platforms like Spark in a faster language, a more viable approach to mitigate its poor performance is to accelerate the computations while still working within the Java-based framework. This paper demonstrates the feasibility of incorporating Field-Programmable Gate Array (FPGA) acceleration into Spark and presents the performance benefits and bottlenecks of our FPGA-accelerated Spark environment using a MapReduce implementation of the k-means clustering algorithm, to show that acceleration is possible even when using a hardware platform that is not well optimized for performance. An important feature of our approach is that the use of FPGAs is completely transparent to the user through the use of library functions, which is a common way by which users access functions provided by Spark. Power users can further develop other computations using high-level synthesis.
23 citations
TL;DR: An adaptive tuning framework, mrMoulder, to recommend a near-optimal configuration for the new job in a short time, and the experiment results have demonstrated that, for a new big data application, the recommend time of mmMoulder is only 20% to 30% of that for the existing auto-tuning methods, while the recommendation quality remains almost unchanged.
23 citations
TL;DR: Experimental results based on the standard AURORA2 dataset demonstrate that the SPARK based speech recognizer delivers consistent improvements in word-accuracy when compared with a baseline speech Recognizer trained using the standard ETSI STQ WI008 DSR features.
Abstract: In this paper, we present a novel speech feature extraction algorithm based on a hierarchical combination of auditory similarity and pooling functions. The computationally efficient features known as “Sparse Auditory Reproducing Kernel” (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal is embedded in a reproducing kernel Hilbert space (RKHS) spanned by overcomplete, nonlinear, and time-shifted gammatone basis functions. The feature extraction algorithm first involves computing kernel based similarity between the speech signal and the time-shifted gammatone functions, followed by feature pruning using a simple pooling technique (“MAX” operation). In this paper, we describe the effect of different hyper-parameters and kernel functions on the performance of a SPARK based speech recognizer. Experimental results based on the standard AURORA2 dataset demonstrate that the SPARK based speech recognizer delivers consistent improvements in word-accuracy when compared with a baseline speech recognizer trained using the standard ETSI STQ WI008 DSR features.
22 citations
Patent•
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08 Jun 1999
TL;DR: A stove burner safety system has an electromagnetic valve and a manual gas cock controlling the gas flow to the burner and a spark plug adjacent the burner which receives a spark pulse from the electric circuitry which also controls an EMV as discussed by the authors.
Abstract: A stove burner safety system has an electromagnetic valve and a manual gas cock controlling the gas flow to the burner and a spark plug adjacent the burner which receives a spark pulse from the electric circuitry which also controls an electromagnetic valve. When the stem of the manual valve is actuated a switch turns on the electric module and when a flame failure is detected a train of a certain number of spark pulses is supplied to the spark plug and if reignition does not occur within a certain number of pulse or a certain time, the electromagnetic valve is turned off.
22 citations