[hpc-announce] IEEE 7th Parlearning workshop with IPDPS 2018

ngoko ngoko at lipn.univ-paris13.fr
Fri Nov 24 09:27:57 CST 2017

Please, accept our apologies in case of multiple copies of this CFP.

The 7th International Workshop on Parallel and Distributed Computing for 
Large Scale Machine Learning and Big Data Analytics

May 21, 2018
Vancouver, British Columbia CANADA
In Conjunction with 32nd IEEE International Parallel & Distributed 
Processing Symposium.

Scaling up machine-learning (ML), data mining (DM) and reasoning 
algorithms from Artificial Intelligence (AI) for massive datasets is a 
major technical challenge in the time of "Big Data". The past ten years 
have seen the rise of multi-core and GPU based computing. In parallel 
and distributed computing, several frameworks such as OpenMP, OpenCL, 
and Spark continue to facilitate scaling up ML/DM/AI algorithms using 
higher levels of abstraction. We invite novel works that advance the 
trio-fields of ML/DM/AI through development of scalable algorithms or 
computing frameworks. Ideal submissions should describe methods for 
scaling up X using Y on Z, where potential choices for X, Y and Z are 
provided below.

Scaling up
•    Recommender systems
•    Optimization algorithms (gradient descent, Newton methods)
•    Deep learning
•    Sampling/sketching techniques
•    Clustering (agglomerative techniques, graph clustering, clustering 
heterogeneous data)
•    Classification (SVM and other classifiers)
•    SVD and other matrix computations
•    Probabilistic inference (Bayesian networks)
•    Logical reasoning
•    Graph algorithms/graph mining and knowledge graphs
•    Semi-supervised learning
•    Online/streaming learning
•    Generative adversarial networks

•    Parallel architectures/frameworks (OpenMP, OpenCL, OpenACC, Intel 
•    Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark)
•    Machine learning frameworks (TensorFlow, PyTorch, Theano, Caffe)

•    Clusters of conventional CPUs
•    Many-core CPU (e.g. Xeon Phi)
•    FPGA
•    Specialized ML accelerators (e.g. GPU and TPU)

•    Paper submission: January 13, 2018 AoE
•    Notification: February 10, 2018
•    Camera Ready: February 24, 2018


Submitted manuscripts should be upto 10 single-spaced double-column 
pages using 10-point size font on 8.5x11 inch pages (IEEE conference 
style), including figures, tables, and references. Format requirements 
are posted on the IEEE IPDPS web page.
All submissions must be uploaded electronically at TBA


Students with accepted papers can apply for a travel award. Please find 
details at www.ipdps.org

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