Middleware 2019 Workshops
The DIDL workshop is co-located with ACM/IFIP Middleware 2019, which takes place from December 9-13 at UC Davis, CA.
Deep learning is a rapidly growing field of machine learning, and has proven successful in many domains, including computer vision, language translation, and speech recognition. The training of deep neural networks is resource intensive, requiring compute accelerators such as GPUs, as well as large amounts of storage and memory, and network bandwidth. Additionally, getting the training data ready requires a lot of tooling for data cleansing, data merging, ambiguity resolution, etc. Sophisticated middleware abstractions are needed to schedule resources, manage the distributed training job as well as visualize how well the training is progressing. Likewise, serving the large neural network models with low latency constraints can require middleware to manage model caching, selection, and refinement.
All the major cloud providers, including Amazon, Google, IBM, and Microsoft have started to offer cloud services in the last year or so with services to train and/or serve deep neural network models. In addition, there is a lot of activity in open source middleware for deep learning, including Tensorflow, Theano, Caffe2, PyTorch, and MXNet. There are also efforts to extend existing platforms such as Spark for deep learning workloads.
This workshop focuses on the tools, frameworks, and algorithms to support executing deep learning algorithms in a distributed environment. As new hardware and accelerators become available, the middleware and systems need to be able exploit their capabilities and ensure they are utilized efficiently.
The workshop is scheduled to be in the morning on Dec 10, 2019.
Record questions and ideas in this Google Doc: https://goo.gl/YRUKsz
Introduction and tutorial on deep learning (9:00 - 9:30) Bishwaranjan Bhattacharjee (IBM Research)
Keynote : NVIDIA Metropolis Software Platform (9:30 - 10:30) Sujit Biswas (NVIDIA)
Sujit Biswas is a Principal Software Engineer at Nvidia. He works for Company’s metropolis platform team building Intelligent Video Analytics. Previously Sujit has held software architect role at Cisco and Sun Microsystems. He has received M.S. computer science from Indian Statistical Institute in 2002.
Break (10:30 - 11:00)
Paper presentations (11:00 - 12:00)
A sim2real framework enabling decentralized agents to execute MADDPG tasks Young-Ho Suh, Sung-Pil Woo, Hyunhak Kim and Dong-Hwan Park (Electronics and Telecommunications Research Institute)
Challenges and Opportunities of DNN Model Execution Caching Guin R. Gilman, Samuel S. Ogden, Robert J. Walls, Tian Guo (Worcester Polytechnic Institute)
Panel and discussion (12:00 - 12:30) Moderator: Vinod Muthusamy (IBM Research)
Bishwaranjan Bhattacharjee, IBM Research
Vatche Ishakian, Bentley University
Vinod Muthusamy, IBM Research
Parag Chandakkar, Walmart Labs
Ian Foster, Argonne National Laboratory and the University of Chicago
Gauri Joshi, Carnegie Mellon University
Jayaram K. R., IBM Research
Ruben Mayer, Technical University of Munich
Pietro Michiardi, Eurecom
Phuong Nguyen, eBay
Peter Pietzuch, Imperial College
Chuan Wu, University of Hong Kong