November 30th, 2021 
Daniela Lopez de Luise
ScaDL 2022:
Scalable Deep Learning over Parallel And Distributed Infrastructure – An IPDPS 2022 Workshop
Scope of the Workshop
Recently, Deep Learning (DL) has received tremendous attention in the research community because of the impressive results obtained for a large number of machine learning problems. The success of state-of-the-art deep learning systems relies on training deep neural networks over a massive amount of training data, which typically requires a large-scale distributed computing infrastructure to run. In order to run these jobs in a scalable and efficient manner, on cloud infrastructure or dedicated HPC systems, several interesting research topics have emerged which are specific to DL. The sheer size and complexity of deep learning models when trained over a large amount of data makes them harder to converge in a reasonable amount of time. It demands advancement along multiple research directions such as, model/data parallelism, model/data compression, distributed optimization algorithms for DL convergence, synchronization strategies, efficient communication and specific hardware acceleration.
SCADL seeks to advance the following research directions:
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Asynchronous and Communication-Efficient SGD: Stochastic gradient descent is at the core of large-scale machine learning. Parallelizing SGD gradient computation across multiple nodes increases the data processed per iteration, but exposes the SGD to communication and synchronization delays and unpredictable node failures in the system. Thus, there is a critical need to design robust and scalable distributed SGD methods to achieve fast error-convergence in spite of such system variabilities.
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High performance computing aspects: Deep learning is highly compute intensive. Algorithms for kernel computations on commonly used accelerators (e.g. GPUs), efficient techniques for communicating gradients and loading data from storage are critical for training performance.
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Model and Gradient Compression Techniques: Techniques such as reducing weights and the size of weight tensors help in reducing the compute complexity. Using lower-bit representations such as quantization and sparsification allow for more optimal use of memory and communication bandwidth.
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Distributed Trustworthy AI: New techniques are needed to meet the goal of global trustworthiness (e.g., fairness and adversarial robustness) efficiently in a distributed DL setting.
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Emerging AI hardware Accelerators: with the proliferation of new hardware accelerators for AI such in memory computing (Analog AI) and neuromorphic computing, novel methods and algorithms need to be introduced to adapt to the underlying properties of the new hardware (example: the non-idealities of the phase-change memory (PCM) and the cycle-to-cycle statistical variations).
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The intersection of Distributed DL and Neural Architecture Search (NAS): NAS is increasingly being used to automate the synthesis of neural networks. However, given the huge computational demands of NAS, distributed DL is critical to make NAS computationally tractable (e.g., differentiable distributed NAS).
This intersection of distributed/parallel computing and deep learning is becoming critical and demands specific attention to address the above topics which some of the broader forums may not be able to provide. The aim of this workshop is to foster collaboration among researchers from distributed/parallel computing and deep learning communities to share the relevant topics as well as results of the current approaches lying at the intersection of these areas.
Areas of Interest
In this workshop, we solicit research papers focused on distributed deep learning aiming to achieve efficiency and scalability for deep learning jobs over distributed and parallel systems. Papers focusing both on algorithms as well as systems are welcome. We invite authors to submit papers on topics including but not limited to:
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Deep learning on cloud platforms, HPC systems, and edge devices
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Model-parallel and data-parallel techniques
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Asynchronous SGD for Training DNNs
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Communication-Efficient Training of DNNs
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Scalable and distributed graph neural networks, Sampling techniques for graph neural networks
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Federated deep learning, both horizontal and vertical, and its challenges
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Model/data/gradient compression
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Learning in Resource constrained environments
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Coding Techniques for Straggler Mitigation
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Elasticity for deep learning jobs/spot market enablement
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Hyper-parameter tuning for deep learning jobs
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Hardware Acceleration for Deep Learning including digital and analog accelerators
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Scalability of deep learning jobs on large clusters
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Deep learning on heterogeneous infrastructure
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Efficient and Scalable Inference
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Data storage/access in shared networks for deep learning
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Communication-efficient distributed fair and adversarially robust learning
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Distributed learning techniques applied to speed up neural architecture search
Workshop Format
Due to the continuing impact of COVID-19, ScaDL 2022 will also adopt relevant IPDPS 2022 policies on virtual participation and presentation. Consequently, the organizers are currently planning a hybrid (in-person and virtual) event.
Submission Link
Submissions will be managed through linklings. Submission link available at: https://2022.scadl.org/call-for-papers
Key Dates
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Paper Submission: January 24, 2022
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Acceptance Notification: March 1, 2022
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Camera ready papers due: March 15, 2022 (hard deadline)
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Workshop Date: TBA (May 30th or June 3rd, 2022)
Author Instructions
ScaDL 2022 accepts submissions in two categories:
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Regular papers: 8-10 pages
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Short papers/Work in progress: 4 pages
The aforementioned lengths include all technical content, references and appendices.
We encourage submissions that are original research work, work in progress, case studies, vision papers, and industrial experience papers.
Papers should be formatted using IEEE conference style, including figures, tables, and references. The IEEE conference style templates for MS Word and LaTeX provided by IEEE eXpress Conference Publishing are available for download. See the latest versions at https://www.ieee.org/conferences/publishing/templates.html
General Chairs
Asynchronous and Communication-Efficient SGD: Stochastic gradient descent is at the core of large-scale machine learning. Parallelizing SGD gradient computation across multiple nodes increases the data processed per iteration, but exposes the SGD to communication and synchronization delays and unpredictable node failures in the system. Thus, there is a critical need to design robust and scalable distributed SGD methods to achieve fast error-convergence in spite of such system variabilities.
High performance computing aspects: Deep learning is highly compute intensive. Algorithms for kernel computations on commonly used accelerators (e.g. GPUs), efficient techniques for communicating gradients and loading data from storage are critical for training performance.
Model and Gradient Compression Techniques: Techniques such as reducing weights and the size of weight tensors help in reducing the compute complexity. Using lower-bit representations such as quantization and sparsification allow for more optimal use of memory and communication bandwidth.
Distributed Trustworthy AI: New techniques are needed to meet the goal of global trustworthiness (e.g., fairness and adversarial robustness) efficiently in a distributed DL setting.
Emerging AI hardware Accelerators: with the proliferation of new hardware accelerators for AI such in memory computing (Analog AI) and neuromorphic computing, novel methods and algorithms need to be introduced to adapt to the underlying properties of the new hardware (example: the non-idealities of the phase-change memory (PCM) and the cycle-to-cycle statistical variations).
The intersection of Distributed DL and Neural Architecture Search (NAS): NAS is increasingly being used to automate the synthesis of neural networks. However, given the huge computational demands of NAS, distributed DL is critical to make NAS computationally tractable (e.g., differentiable distributed NAS).
Deep learning on cloud platforms, HPC systems, and edge devices
Model-parallel and data-parallel techniques
Asynchronous SGD for Training DNNs
Communication-Efficient Training of DNNs
Scalable and distributed graph neural networks, Sampling techniques for graph neural networks
Federated deep learning, both horizontal and vertical, and its challenges
Model/data/gradient compression
Learning in Resource constrained environments
Coding Techniques for Straggler Mitigation
Elasticity for deep learning jobs/spot market enablement
Hyper-parameter tuning for deep learning jobs
Hardware Acceleration for Deep Learning including digital and analog accelerators
Scalability of deep learning jobs on large clusters
Deep learning on heterogeneous infrastructure
Efficient and Scalable Inference
Data storage/access in shared networks for deep learning
Communication-efficient distributed fair and adversarially robust learning
Distributed learning techniques applied to speed up neural architecture search
Paper Submission: January 24, 2022
Acceptance Notification: March 1, 2022
Camera ready papers due: March 15, 2022 (hard deadline)
Workshop Date: TBA (May 30th or June 3rd, 2022)
Regular papers: 8-10 pages
Short papers/Work in progress: 4 pages