ParLearning 2018 - 7th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics

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)
IMPORTANT DATES
• Paper submission: January 13, 2018 AoE
• Notification: February 10, 2018
• Camera Ready: February 24, 2018
PAPER GUIDELINES
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
TRAVEL AWARDS
Students with accepted papers can apply for a travel award. Please find details at www.ipdps.org