Parallel Programming for Graph Analysis. In 17th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP), New Orleans, LA, February, 2012.
Parallel Programming for Graph Analysis [link]Paper  abstract   bibtex   
An increasingly fast-paced, digital world has produced an ever-growing volume of petabyte-sized datasets. At the same time, terabytes of new, unstructured data arrive daily. As the desire to ask more detailed questions about these massive streams has grown, parallel software and hardware have only recently begun to enable complex analytics in this non-scientific space. In this tutorial, we will discuss the open problems facing us with analyzing this "data deluge". We will present algorithms and data structures capable of analyzing spatio-temporal data at massive scale on parallel systems. We will try to understand the difficulties and bottlenecks in parallel graph algorithm design on current systems and will show how multithreaded and hybrid systems can overcome these challenges. We will demonstrate how parallel graph algorithms can be implemented on a variety of architectures using different programming models. The goal of this tutorial is to provide a comprehensive introduction to the field of parallel graph analysis to an audience with computing background, interested in participating in research and/or commercial applications of this field. Moreover, we will cover leading-edge technical and algorithmic developments in the field and discuss open problems and potential solutions.
@inproceedings{ppopp12-graph,
  author = {David Ediger and Jason Riedy and Rob McColl and
                  David A. Bader},
  ejr-withauthor = {David Ediger and Rob McColl and David A. Bader},
  title = {Parallel Programming for Graph Analysis},
  booktitle = {17th ACM SIGPLAN Annual Symposium on Principles and
                  Practice of Parallel Programming (PPoPP)},
  role = {tutorial},
  tags = {parallel; graph},
  year = 2012,
  month = feb,
  dom = 26,
  address = {New Orleans, LA},
  url = {http://www.cc.gatech.edu/~bader/papers/GraphAnalysisTutorial-PPoPP2012.html},
  abstract = {An increasingly fast-paced, digital world has
                  produced an ever-growing volume of petabyte-sized
                  datasets. At the same time, terabytes of new,
                  unstructured data arrive daily. As the desire to ask
                  more detailed questions about these massive streams
                  has grown, parallel software and hardware have only
                  recently begun to enable complex analytics in this
                  non-scientific space.  In this tutorial, we will
                  discuss the open problems facing us with analyzing
                  this "data deluge". We will present algorithms and
                  data structures capable of analyzing spatio-temporal
                  data at massive scale on parallel systems. We will
                  try to understand the difficulties and bottlenecks
                  in parallel graph algorithm design on current
                  systems and will show how multithreaded and hybrid
                  systems can overcome these challenges. We will
                  demonstrate how parallel graph algorithms can be
                  implemented on a variety of architectures using
                  different programming models.  The goal of this
                  tutorial is to provide a comprehensive introduction
                  to the field of parallel graph analysis to an
                  audience with computing background, interested in
                  participating in research and/or commercial
                  applications of this field. Moreover, we will cover
                  leading-edge technical and algorithmic developments
                  in the field and discuss open problems and potential
                  solutions.},
  officialproject = {nsf-s2i2-conc},
  projtag = {cassmt}
}
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