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Sparse Deep Neural Network graph challenge

September 24, 2019
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Conference Paper
Author:
Jeremy Kepner
…
Published in:
IEEE High Performance Extreme Computing Conf., HPEC, 24-26 September 2019.
Topic:
graph processing
R&D area:
R&D group:

Summary

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The proposed Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems. The Sparse DNN Challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment. Sparse DNN inference is amenable to both vertex-centric implementations and array-based implementations (e.g., using the GraphBLAS.org standard). The computations are simple enough that performance predictions can be made based on simple computing hardware models. The input data sets are derived from the MNIST handwritten letters. The surrounding I/O and verification provide the context for each sparse DNN inference that allows rigorous definition of both the input and the output. Furthermore, since the proposed sparse DNN challenge is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Reference implementations have been implemented and their serial and parallel performance have been measured. Specifications, data, and software are publicly available at GraphChallenge.org.
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Summary

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The proposed Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is...

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Sparse Deep Neural Network graph challenge

Optimizing the visualization pipeline of a 3-D monitoring and management system

September 24, 2019
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Conference Paper
Author:
Rebecca L. Wild
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Published in:
2019 IEEE High Performance Computing Conf., HPEC, 24-26 September 2019.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Monitoring and managing High Performance Computing (HPC) systems and environments generate an ever growing amount of data. Making sense of this data and generating a platform where the data can be visualized for system administrators and management to proactively identify system failures or understand the state of the system requires the platform to be as efficient and scalable as the underlying database tools used to store and analyze the data. In this paper we will show how we leverage Accumulo, d4m, and Unity to generate a 3-D visualization platform to monitor and manage the Lincoln Laboratory Supercomputer systems and how we have had to retool our approach to scale with our systems.
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Summary

Monitoring and managing High Performance Computing (HPC) systems and environments generate an ever growing amount of data. Making sense of this data and generating a platform where the data can be visualized for system administrators and management to proactively identify system failures or understand the state of the system requires...

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Optimizing the visualization pipeline of a 3-D monitoring and management system

Survey and benchmarking of machine learning accelerators

September 24, 2019
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Conference Paper
Author:
Albert I. Reuther
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Published in:
IEEE High Performance Extreme Computing Conf., HPEC, 24-26 September 2019.
Topic:
machine learning
R&D area:
R&D group:

Summary

Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore's Law, have prompted an explosion of processors and accelerators that promise even greater computational and machine learning capabilities. These processors and accelerators are coming in many forms, from CPUs and GPUs to ASICs, FPGAs, and dataflow accelerators. This paper surveys the current state of these processors and accelerators that have been publicly announced with performance and power consumption numbers. The performance and power values are plotted on a scatter graph and a number of dimensions and observations from the trends on this plot are discussed and analyzed. For instance, there are interesting trends in the plot regarding power consumption, numerical precision, and inference versus training. We then select and benchmark two commercially-available low size, weight, and power (SWaP) accelerators as these processors are the most interesting for embedded and mobile machine learning inference applications that are most applicable to the DoD and other SWaP constrained users. We determine how they actually perform with real-world images and neural network models, compare those results to the reported performance and power consumption values and evaluate them against an Intel CPU that is used in some embedded applications.
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Summary

Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore's Law, have prompted an explosion of processors and accelerators that promise even greater computational and...

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Survey and benchmarking of machine learning accelerators

Streaming 1.9 billion hyperspace network updates per second with D4M

September 24, 2019
  |
Conference Paper
Author:
Jeremy Kepner
…
Published in:
2019 IEEE High Performance Computing Conf., HPEC, 24-26 September 2019.
Topic:
high performance computing
R&D area:
R&D group:

Summary

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.
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Summary

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...

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Streaming 1.9 billion hyperspace network updates per second with D4M

Corpora design and score calibration for text dependent pronunciation proficiency recognition

September 20, 2019
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Conference Paper
Author:
Frederick S. Richardson
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Published in:
8th ISCA Workshop on Speech and Language Technology in Education, SLaTe 2019, 20-21 September 2019.
Topic:
speech recognition
R&D area:
R&D group:

Summary

This work investigates methods for improving a pronunciation proficiency recognition system, both in terms of phonetic level posterior probability calibration, and in ordinal utterance level classification, for Modern Standard Arabic (MSA), Spanish and Russian. To support this work, utterance level labels were obtained by crowd-sourcing the annotation of language learners' recordings. Phonetic posterior probability estimates extracted using automatic speech recognition systems trained in each language were estimated using a beta calibration approach [1] and language proficiency level was estimated using an ordinal regression [2]. Fusion with language recognition (LR) scores from an i-vector system [3] trained on 23 languages is also explored. Initial results were promising for all three languages and it was demonstrated that the calibrated posteriors were effective for predicting pronunciation proficiency. Significant relative gains of 16% mean absolute error for the ordinal regression and 17% normalized cross entropy for the binary beta regression were achieved on MSA through fusion with LR scores.
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Summary

This work investigates methods for improving a pronunciation proficiency recognition system, both in terms of phonetic level posterior probability calibration, and in ordinal utterance level classification, for Modern Standard Arabic (MSA), Spanish and Russian. To support this work, utterance level labels were obtained by crowd-sourcing the annotation of language learners'...

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Corpora design and score calibration for text dependent pronunciation proficiency recognition

Using K-means in SVR-based text difficulty estimation

September 20, 2019
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Conference Paper
Author:
Raymond H. Budd
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Published in:
8th ISCA Workshop on Speech and Language Technology in Education, SLaTE, 20-21 September 2019.
Topic:
human language technology
R&D area:
R&D group:

Summary

A challenge for second language learners, educators, and test creators is the identification of authentic materials at the right level of difficulty. In this work, we present an approach to automatically measure text difficulty, integrated into Auto-ILR, a web-based system that helps find text material at the right level for learners in 18 languages. The Auto-ILR subscription service scans web feeds, extracts article content, evaluates the difficulty, and notifies users of documents that match their skill level. Difficulty is measured on the standard ILR scale with language-specific support vector machine regression (SVR) models built from vectors incorporating length features, term frequencies, relative entropy, and K-means clustering.
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Summary

A challenge for second language learners, educators, and test creators is the identification of authentic materials at the right level of difficulty. In this work, we present an approach to automatically measure text difficulty, integrated into Auto-ILR, a web-based system that helps find text material at the right level for...

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Using K-means in SVR-based text difficulty estimation

The leakage-resilience dilemma

September 15, 2019
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Conference Paper
Author:
Bryan Ward
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Published in:
Proc. European Symp. on Research in Computer Security, ESORICS 2019, pp. 87-106.
Topic:
cyber security
R&D area:
R&D group:

Summary

Many control-flow-hijacking attacks rely on information leakage to disclose the location of gadgets. To address this, several leakage-resilient defenses, have been proposed that fundamentally limit the power of information leakage. Examples of such defenses include address-space re-randomization, destructive code reads, and execute-only code memory. Underlying all of these defenses is some form of code randomization. In this paper, we illustrate that randomization at the granularity of a page or coarser is not secure, and can be exploited by generalizing the idea of partial pointer overwrites, which we call the Relative ROP (RelROP) attack. We then analyzed more that 1,300 common binaries and found that 94% of them contained sufficient gadgets for an attacker to spawn a shell. To demonstrate this concretely, we built a proof-of-concept exploit against PHP 7.0.0. Furthermore, randomization at a granularity finer than a memory page faces practicality challenges when applied to shared libraries. Our findings highlight the dilemma that faces randomization techniques: course-grained techniques are efficient but insecure and fine-grained techniques are secure but impractical.
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Summary

Many control-flow-hijacking attacks rely on information leakage to disclose the location of gadgets. To address this, several leakage-resilient defenses, have been proposed that fundamentally limit the power of information leakage. Examples of such defenses include address-space re-randomization, destructive code reads, and execute-only code memory. Underlying all of these defenses is...

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The leakage-resilience dilemma

State-of-the-art speaker recognition for telephone and video speech: the JHU-MIT submission for NIST SRE18

September 15, 2019
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Conference Paper
Author:
Jesus Villalba
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Published in:
Interspeech, 15-19 September 2019.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

We present a condensed description of the joint effort of JHUCLSP, JHU-HLTCOE, MIT-LL., MIT CSAIL and LSE-EPITA for NIST SRE18. All the developed systems consisted of xvector/i-vector embeddings with some flavor of PLDA backend. Very deep x-vector architectures–Extended and Factorized TDNN, and ResNets– clearly outperformed shallower xvectors and i-vectors. The systems were tailored to the video (VAST) or to the telephone (CMN2) condition. The VAST data was challenging, yielding 4 times worse performance than other video based datasets like Speakers in the Wild. We were able to calibrate the VAST data with very few development trials by using careful adaptation and score normalization methods. The VAST primary fusion yielded EER=10.18% and Cprimary= 0.431. By improving calibration in post-eval, we reached Cprimary=0.369. In CMN2, we used unsupervised SPLDA adaptation based on agglomerative clustering and score normalization to correct the domain shift between English and Tunisian Arabic models. The CMN2 primary fusion yielded EER=4.5% and Cprimary=0.313. Extended TDNN x-vector was the best single system obtaining EER=11.1% and Cprimary=0.452 in VAST; and 4.95% and 0.354 in CMN2.
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Summary

We present a condensed description of the joint effort of JHUCLSP, JHU-HLTCOE, MIT-LL., MIT CSAIL and LSE-EPITA for NIST SRE18. All the developed systems consisted of xvector/i-vector embeddings with some flavor of PLDA backend. Very deep x-vector architectures–Extended and Factorized TDNN, and ResNets– clearly outperformed shallower xvectors and i-vectors. The...

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State-of-the-art speaker recognition for telephone and video speech: the JHU-MIT submission for NIST SRE18

Guest editorial: special issue on hardware solutions for cyber security

August 22, 2019
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Journal Article
Author:
Mankuan Michael Vai
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Published in:
J. Hardw. Syst. Secur., Vol. 3, No. 199, 2019.
Topic:
computing
R&D area:
R&D group:

Summary

A cyber system could be viewed as an architecture consisting of application software, system software, and system hardware. The hardware layer, being at the foundation of the overall architecture, must be secure itself and also provide effective security features to the software layers. In order to seamlessly integrate security hardware into a system with minimal performance compromises, designers must develop and understand tangible security specifications and metrics to trade between security, performance, and cost for an optimal solution. Hardware security components, libraries, and reference architecture are increasingly important in system design and security. This special issue includes four exciting manuscripts on several aspects of developing hardware-oriented security for systems.
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Summary

A cyber system could be viewed as an architecture consisting of application software, system software, and system hardware. The hardware layer, being at the foundation of the overall architecture, must be secure itself and also provide effective security features to the software layers. In order to seamlessly integrate security hardware...

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Guest editorial: special issue on hardware solutions for cyber security

Improving robustness to attacks against vertex classification

August 5, 2019
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Conference Paper
Author:
Benjamin A. Miller
…
Published in:
15th Intl. Workshop on Mining and Learning with Graphs, 5 August 2019.
Topic:
networking
R&D area:
R&D group:

Summary

Vertex classification—the problem of identifying the class labels of nodes in a graph—has applicability in a wide variety of domains. Examples include classifying subject areas of papers in citation networks or roles of machines in a computer network. Recent work has demonstrated that vertex classification using graph convolutional networks is susceptible to targeted poisoning attacks, in which both graph structure and node attributes can be changed in an attempt to misclassify a target node. This vulnerability decreases users' confidence in the learning method and can prevent adoption in high-stakes contexts. This paper presents work in progress aiming to make vertex classification robust to these types of attacks. We investigate two aspects of this problem: (1) the classification model and (2) the method for selecting training data. Our alternative classifier is a support vector machine (with a radial basis function kernel), which is applied to an augmented node feature-vector obtained by appending the node’s attributes to a Euclidean vector representing the node based on the graph structure. Our alternative methods of selecting training data are (1) to select the highest-degree nodes in each class and (2) to iteratively select the node with the most neighbors minimally connected to the training set. In the datasets on which the original attack was demonstrated, we show that changing the training set can make the network much harder to attack. To maintain a given probability of attack success, the adversary must use far more perturbations; often a factor of 2–4 over the random training baseline. Even in cases where success is relatively easy for the attacker, we show that the classification and training alternatives allow classification performance to degrade much more gradually, with weaker incorrect predictions for the attacked nodes.
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Summary

Vertex classification—the problem of identifying the class labels of nodes in a graph—has applicability in a wide variety of domains. Examples include classifying subject areas of papers in citation networks or roles of machines in a computer network. Recent work has demonstrated that vertex classification using graph convolutional networks is...

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Improving robustness to attacks against vertex classification
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