May 21, 2018
Vancouver, British Columbia CANADA
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.
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.PDF Flyer
Travel awards: Students with accepted papers have a chance to apply for a travel award. Please find details on the IEEE IPDPS web page.
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 https://easychair.org/conferences/?conf=parlearning2018