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Graphs are widely used to model relationships between entities. Mining for information on large scale social network graphs poses a major challenge in terms of computation and performance due to the sheer size, scale and growth of the graphs involved. Therefore it is important to be able to work with graphs in a distributed setting. Recently, Natural language processing (NLP)-based graph representation...
With the development of information Technology, we can see an exponential growth in the amount of data that is being generated and used over the last decade. The need for storing the data and processing them in order to extract value from skew data has paved way for parallel, distributed processing applications like Hadoop. The current Hadoop implementation assumes that the compute capacity of all...
The advent of ubiquitous mobile communication has posed a lot of challenges, one of the prominent being suppression of background noise, especially in non-stationary noisy environments. In literature, several speech enhancement techniques have been proposed to tackle this problem of noise reduction. Codebook-based speech enhancement (CBSE) employing trained speech and noise codebooks, is one of the...
The Intel MIC architecture, implemented in the Xeon Phi coprocessor, is targeted at highly parallel applications. In order to exploit it, one needs to make full use of simultaneous multi-threading, which permits four simultaneous threads per core. Our results also show that distributed tag directories can be a greater bottleneck than the ring for small messages when multiple threads access the same...
Compute-intensive tasks in high-end high performance computing (HPC) systems often generate large amounts of data, especially floating-point data, that need to be transmitted over the network. Although computation speeds are very high, the overall performance of these applications is affected by the data transfer overhead. Moreover, as data sets are growing in size rapidly, bandwidth limitations pose...
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