Fifth Workshop on Distributed Infrastructures for Deep Learning (DIDL) 2021

Middleware 2021 Workshops

The DIDL workshop is co-located with ACM/IFIP Middleware 2021, which takes place from December 6-10 in Qu├ębec, Canada.

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 recently 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 but not limited to Tensorflow, Theano, Caffe2, PyTorch, MXNet, Hugging Face, and fairseq. There are also efforts to extend existing platforms such as Spark and Ray for various aspects of deep learning.

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.

Workshop call for papers

Call For Papers (CFP)

Workshop Co-chairs

Bishwaranjan Bhattacharjee, IBM Research
Vatche Ishakian, IBM Research
Vinod Muthusamy, IBM Research

Program Committee (Tentative)

Parag Chandakkar, Walmart Labs
Ian Foster, Argonne National Laboratory and the University of Chicago
Matthew Hill, Dataminr
Mayoore Jaiswal, Nvidia
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