The 8th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics

May 20/24, 2019
Rio de Janeiro, BRAZIL

In Conjunction with 33rd IEEE International Parallel & Distributed Processing Symposium
May 20-24, 2019
Hilton Rio de Janeiro Copacabana
Rio de Janeiro, BRAZIL
IPDPS 2019 logo

Call for Papers

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

Using

On

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.

Awards

Best Paper Award: The program committee will nominate a paper for the Best Paper award. In past years, the Best Paper award included a cash prize. Stay tuned for this year!

Travel awards: Students with accepted papers have a chance to apply for a travel award. Please find details on the IEEE IPDPS web page.

Important Dates

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 (link TBA).

Organization

Past workshops