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

Using

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

On

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

Proceedings of the Parlearning workshop will be distributed at the conference and will be submitted for inclusion in the IEEE Xplore Digital Library after the conference.

• Paper submission: TBD
• Notification: TBD
• Camera Ready: TBD

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 (link TBA).

• General co-chairs: Arindam Pal (TCS Innovation Labs, India) and Anand Panangadan (California State University, Fullerton, USA)
• Program chair: Thomas Parnell (IBM Research – Zurich, Switzerland)
• Publicity chair: Yanik Ngoko (Université Paris XIII, France)
• Steering Committee: Sutanay Choudhury (Pacific Northwest National Laboratory, USA), and Yinglong Xia (Huawei Research America, USA)

• 2018
• 2017
• 2016
• 2015
• 2014
• 2013
• 2012