@STRING{jan = "January"}
@STRING{feb = "February"}
@STRING{mar = "March"}
@STRING{apr = "April"}
@STRING{may = "May"}
@STRING{jun = "June"}
@STRING{jul = "July"}
@STRING{aug = "August"}
@STRING{sep = "September"}
@STRING{oct = "October"}
@STRING{nov = "November"}
@STRING{dec = "December"}


@InProceedings{hpec23-tutorial,
  author =       {Jeffrey Young and Patrick Lavin and Jason Riedy and Srinivas Eswar},
  title =        {Exploring Graph Analysis for HPC with Near-Memory Accelerators},
  booktitle =    {IEEE High Performance Extreme Computing ({HPEC})},
  year =         2022,
  month =        sep,
  url = {https://github.com/gt-crnch-rg/lucata-pathfinder-tutorial},
  note = {\url{https://crnch-rg.gitlab.io/pearc-2019/},}}
}

@InProceedings{pearc19-tutorial,
  author =       {E. Jason Riedy and Jeffrey S. Young},
  title =        {Programming Novel Architectures in the Post-{Moore} Era with the {Rogues} {Gallery}},
  booktitle =    {Practice and Experience in Advanced Research Computing ({PEARC})},
  year =         2019,
  month =        jul,
  address =      {Chicago, IL},
  url = {https://crnch-rg.gitlab.io/pearc-2019/},
  note = {\url{https://crnch-rg.gitlab.io/pearc-2019/}}
}

@InProceedings{asplos19-tutorial,
  author =       {E. Jason Riedy and Jeffrey S. Young},
  title =        {Programming Novel Architectures in the Post-{Moore} Era with The {Rogues} {Gallery}},
  booktitle =    {24th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)},
  year =         2019,
  month =        apr,
  address =      {Providence, RI},
  url = {https://crnch-rg.gitlab.io/asplos-2019/},
  note = {\url{https://crnch-rg.gitlab.io/asplos-2019/}},
}

@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},
  OPTtags =         {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},
  keywords =     {graph analysis, high performance data analysis, streaming data},
  ejr-proj =     {hpda, graph-analysis},
  ejr-grant =    {cassmt},
}

@InProceedings{md11-graph,
  author =       {David A. Bader and David Ediger and E. Jason Riedy},
  ejr-withauthor ={David A. Bader and David Ediger},
  title =        {Parallel Programming for Graph Analysis},
  booktitle =    {full day tutorial},
  role =         {tutorial},
  OPTtags =         {parallel; graph},
  year =         2011,
  month =        sep,
  dom =          28,
  address =      {Columbia, MD},
  projtag =      {cassmt},
  keywords =     {graph analysis, high performance data analysis, streaming data},
  ejr-proj =     {hpda, graph-analysis},
  ejr-grant =    {cassmt},
}

@InProceedings{ppopp11-graph,
  author =       {David A. Bader and David Ediger and E. Jason Riedy},
  ejr-withauthor ={David A. Bader and David Ediger},
  title =        {Parallel Programming for Graph Analysis},
  booktitle =    {16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP)},
  role =         {tutorial},
  OPTtags =         {parallel; graph},
  year =         2011,
  month =        feb,
  dom =          12,
  address =      {San Antonio, TX},
  url =          {http://www.cc.gatech.edu/~bader/papers/GraphAnalysisTutorial-PPoPP2011.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.},
  projtag =      {cassmt},
  keywords =     {graph analysis, high performance data analysis, streaming data},
  ejr-proj =     {hpda, graph-analysis},
  ejr-grant =    {cassmt},
}