ParLearning-2017: The 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (with IEEE IPDPS) Buena Vista Palace Hotel Orlando, FL, United States, May 29-June 2, 2017 |
Conference website | http://parlearning.ecs.fullerton.edu/ |
Submission link | https://easychair.org/conferences/?conf=parlearning2017 |
Abstract registration deadline | January 20, 2017 |
Submission deadline | January 20, 2017 |
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 would be characterized as scaling up X on Y, where potential choices for X and Y are provided below.
Scaling up
- recommender systems
- gradient descent algorithms
- deep learning
- sampling/sketching techniques
- clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)
- classification (SVM and other classifiers)
- SVD
- probabilistic inference (Bayesian networks)
- logical reasoning
- graph algorithms and graph mining
On
- Multi-core architectures/frameworks (OpenMP)
- Many-core (GPU) architectures/frameworks (OpenCL, OpenACC, CUDA, Intel TBB)
- Distributed systems/frameworks (GraphLab, MPI, Hadoop, Spark, Storm, Mahout, etc.)
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.
Travel awards: Students with accepted papers have a chance to apply for a travel award. Please find details on the IEEE IPDPS web page.
Submission 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 https://easychair.org/conferences/?conf=parlearning2017
Important Dates
- Paper submission:
January 13January 20, 2017 AoE - Notification: February 10, 2017
- Camera Ready: March 10, 2017
Organizing committee
- General chair: Anand Panangadan (California State University, Fullerton, USA)
- Technical Program co-chairs: Henri Bal (Vrije Universiteit, The Netherlands) and Arindam Pal (TCS Innovation Labs, India)
- Publicity chair: Charalampos Chelmis (University at Albany, State University of New York, USA)
- Steering Committee chair: Yinglong Xia (Huawei Research, USA)
Contact
Please direct questions to Anand Panangadan <apanangadan@fullerton.edu>