Now showing items 1-4 of 4

    • Auto-parallelizing stateful distributed streaming applications 

      Schneider, S.; Hirzel, M.; Gedik, Buğra; Wu, K. -L. (2012)
      Streaming applications transform possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. The streaming ...
    • Fast and efficient model parallelism for deep convolutional neural networks 

      Eserol, Burak (Bilkent University, 2019-08)
      Convolutional Neural Networks (CNNs) have become very popular and successful in recent years. Increasing the depth and number of parameters of CNNs has crucial importance on this success. However, it is hard to t deep ...
    • Safe data parallelism for general streaming 

      Schneider S.; Hirzel M.; Gedik, B.; Wu, Kun-Lung (Institute of Electrical and Electronics Engineers, 2015)
      Streaming applications process possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. General streaming ...
    • Tutorial: Stream processing optimizations 

      Schneider, S.; Hirzel, M.; Gedik, Buğra (ACM, 2013)
      This tutorial starts with a survey of optimizations for streaming applications. The survey is organized as a catalog that introduces uniform terminology and a common categorization of optimizations across disciplines, such ...