Advance Program

Time	Title	Authors/Speaker
8:15-8:30am	Opening remarks
8:30-9:30am	Invited Talk 1	Abhinav Vishnu, Principal member of technical staff, AMD, USA
9:30-10:00am	Break
10:00-10:30am	Near-Optimal Straggler Mitigation for Distributed Gradient Methods (ParLearning-01)	Songze Li, Seyed Mohammadreza Mousavi Kalan, A. Salman Avestimehr and Mahdi Soltanolkotabi
10:30-11:00am	Streaming Tiles: Flexible Implementation of Convolution Neural Networks Inference on Manycore Architectures (ParLearning-02)	Nesma Rezk, Madhura Purnaprajna and Zain Ul-Abdin
11:00-12:0pm	Invited Talk 2: Model Parallelism optimization with deep reinforcement learning	Azalia Mirhoseini, Google Brain, USA
12:00-1:30pm	Lunch
1:30-2:30pm	Invited Talk 3: Introduction to Snap Machine Learning	Thomas Parnell, IBM Research – Zurich, Switzerland
2:30-3:00pm	Parallel Huge Matrix Multiplication on a Cluster with GPGPU Accelerators (ParLearning-06)	Seungyo Ryu and Dongseung Kim
3:00-3:30pm	Break
3:30-4:00pm	Invited Talk 4
4:00-4:30pm	A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression (ParLearning-04)	Elizaveta Rebrova, Gustavo Chávez, Yang Liu, Pieter Ghysels and Xiaoye Sherry Li
4:30-5:00pm	Panel Discussion	Azalia Mirhoseini, Thomas Parnell, Abhinav Vishnu

Abhinav Vishnu, Principal member of technical staff, AMD, USA

Azalia Mirhoseini, Google Brain, USA

Model Parallelism optimization with deep reinforcement learning

Thomas Parnell, IBM Research – Zurich, Switzerland

Introduction to Snap Machine Learning

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.

PDF Flyer

• Paper submission: February 16, 2018 AoE
• Notification: March 16 March 9, 2018
• Camera Ready: March 30 March 16, 2018

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

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

• Indrajit Bhattacharya, TCS Research, India
• Vito Giovanni Castellana, Pacific Northwest National Laboratory, USA
• Tanmoy Chakraborty, IIIT Delhi, India
• Daniel Gerardo Chavarria, Pacific Northwest National Laboratory, USA
• Sutanay Choudhury, Pacific Northwest National Laboratory, USA
• Zhihui Du, Tsinghua University, China
• Anand Eldawy, University of Minnesota, USA
• Erich Elsen, Google Brain, USA
• Dinesh Garg, IIT Gandhinagar and IBM Research, India
• Kripabandhu Ghosh, IIT Kanpur, India
• Saptarshi Ghosh, IIT Kharagpur, India
• Kazuaki Ishizaki, IBM Research - Tokyo, Japan
• Farinaz Koushanfar, UCSD, USA
• Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
• Carson Leung, University of Manitoba, Canada
• Animesh Mukherjee, IIT Kharagpur, India
• Debnath Mukherjee, TCS Research, India
• Francesco Parisi, University of Calabria, Italy
• Saurabh Paul, PayPal, USA
• Lijun Qian, Prairie View A&M University, USA
• Lingfei Wu, IBM T. J. Watson Research Center, USA
• Jianting Zhang, City College of New York, USA

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