990 research outputs found
What does fault tolerant Deep Learning need from MPI?
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML)
algorithm for large scale data analysis. DL algorithms are computationally
expensive - even distributed DL implementations which use MPI require days of
training (model learning) time on commonly studied datasets. Long running DL
applications become susceptible to faults - requiring development of a fault
tolerant system infrastructure, in addition to fault tolerant DL algorithms.
This raises an important question: What is needed from MPI for de- signing
fault tolerant DL implementations? In this paper, we address this problem for
permanent faults. We motivate the need for a fault tolerant MPI specification
by an in-depth consideration of recent innovations in DL algorithms and their
properties, which drive the need for specific fault tolerance features. We
present an in-depth discussion on the suitability of different parallelism
types (model, data and hybrid); a need (or lack thereof) for check-pointing of
any critical data structures; and most importantly, consideration for several
fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI
and their applicability to fault tolerant DL implementations. We leverage a
distributed memory implementation of Caffe, currently available under the
Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches
by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation
using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies
demonstrates the effectiveness of the proposed fault tolerant DL implementation
using OpenMPI based ULFM
The Case for Graph-Based Recommendations
Recommender systems have been intensively used to create personalised profiles, which enhance the user experience. In certain areas, such as e-learning, this approach is short-sighted, since each student masters each concept through different means. The progress from one concept to the next, or from one lesson to another, does not necessarily follow a fixed pattern. Given these settings, we can no longer use simple structures (vectors, strings, etc.) to represent each user's interactions with the system, because the sequence of events and their mapping to user's intentions, build up into more complex synergies. As a consequence, we propose a graph-based interpretation of the problem and identify the challenges behind (a) using graphs to model the users' journeys and hence as the input to the recommender system, and (b) producing recommendations in the form of graphs of actions to be taken
HIL: designing an exokernel for the data center
We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.Partial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation awards 1347525 and 1149232 as well as the several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or
Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment
Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future
Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
The Dynamic Distributed Dimensional Data Model (D4M) library implements
associative arrays in a variety of languages (Python, Julia, and Matlab/Octave)
and provides a lightweight in-memory database implementation of hypersparse
arrays that are ideal for analyzing many types of network data. D4M relies on
associative arrays which combine properties of spreadsheets, databases,
matrices, graphs, and networks, while providing rigorous mathematical
guarantees, such as linearity. Streaming updates of D4M associative arrays put
enormous pressure on the memory hierarchy. This work describes the design and
performance optimization of an implementation of hierarchical associative
arrays that reduces memory pressure and dramatically increases the update rate
into an associative array. The parameters of hierarchical associative arrays
rely on controlling the number of entries in each level in the hierarchy before
an update is cascaded. The parameters are easily tunable to achieve optimal
performance for a variety of applications. Hierarchical arrays achieve over
40,000 updates per second in a single instance. Scaling to 34,000 instances of
hierarchical D4M associative arrays on 1,100 server nodes on the MIT SuperCloud
achieved a sustained update rate of 1,900,000,000 updates per second. This
capability allows the MIT SuperCloud to analyze extremely large streaming
network data sets.Comment: 6 pages; 6 figures; accepted to IEEE High Performance Extreme
Computing (HPEC) Conference 2019. arXiv admin note: text overlap with
arXiv:1807.05308, arXiv:1902.0084
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
The IoT (Internet of Things) technology has been widely adopted in recent
years and has profoundly changed the people's daily lives. However, in the
meantime, such a fast-growing technology has also introduced new privacy
issues, which need to be better understood and measured. In this work, we look
into how private information can be leaked from network traffic generated in
the smart home network. Although researchers have proposed techniques to infer
IoT device types or user behaviors under clean experiment setup, the
effectiveness of such approaches become questionable in the complex but
realistic network environment, where common techniques like Network Address and
Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic
analysis using traditional methods (e.g., through classical machine-learning
models) is much less effective under those settings, as the features picked
manually are not distinctive any more. In this work, we propose a traffic
analysis framework based on sequence-learning techniques like LSTM and
leveraged the temporal relations between packets for the attack of device
identification. We evaluated it under different environment settings (e.g.,
pure-IoT and noisy environment with multiple non-IoT devices). The results
showed our framework was able to differentiate device types with a high
accuracy. This result suggests IoT network communications pose prominent
challenges to users' privacy, even when they are protected by encryption and
morphed by the network gateway. As such, new privacy protection methods on IoT
traffic need to be developed towards mitigating this new issue
Method to Generate Disaster-Damage Map using 3D photometry and Crowd Sourcing
Thanks to the rapid progress of the Internet and mobile devices, information related to disaster areas can be collected through the Internet. To grasp the degree of damage in a disaster situation, the use of crowdsourcing for coordinating the individual efforts (micro tasks) of an enormous number of users (workers) on the Internet has been drawing attention as a means of quickly solving problems. However, the information gathered from the Internet is huge and diverse, so it is difficult to formulate as a crowdsourcing task. This paper proposes a conversion platform for the images of a disaster site photographed by various users as information about the site, integrating the images into a single map using 3D image processing, and providing the map to crowdsourcing as a micro task.Published in: 2017 IEEE International Conference on Big Data (Big Data) Date of Conference: 11-14 Dec. 2017 Conference Location: Boston, MA, US
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