Dear colleague,

The 6th International Workshop for Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning) will be held with IEEE IPDPS<> on May 29, 2017 in Orlando, Florida, USA. As in previous years, we invite novel works that advance the fields of Machine Learning, Data Mining, and Artificial Intelligence through development of scalable parallel algorithms or distributed computing frameworks. Ideal submissions would be characterized as scaling up X on Y, where potential choices for X and Y are provided below.
Scaling up

  *   recommender systems
  *   gradient descent algorithms
  *   deep learning
  *   sampling/sketching techniques
  *   clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)
  *   classification (SVM and other classifiers)
  *   SVD
  *   probabilistic inference (Bayesian networks)
  *   logical reasoning
  *   graph algorithms and graph mining

  *   Multi-core architectures/frameworks (OpenMP)
  *   Many-core (GPU) architectures/frameworks (OpenCL, OpenACC, CUDA, Intel TBB)
  *   Distributed systems/frameworks (GraphLab, MPI, Hadoop, Spark, Storm, Mahout, etc.)

All papers will be included in the IEEE IPDPS workshops proceedings (and included in the IEEE Xplore Digital Library after the conference). Selected papers from the workshop will be published in a Special Issue of Future Generation Computer Systems, Elsevier's International Journal of eScience.

The Call for Papers and submission details are at the workshop webpage:


Anand Panangadan, PhD
Assistant Professor
Department of Computer Science
California State University, Fullerton
800 N. State College Blvd
Fullerton, CA 92834, USA<>