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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Badruddoja, Syed Dantu, Ram Widick, Logan Zaccagni, Zachary Upadhyay, Kritagya Univ North Texas Dept Comp Sci & Engn Denton TX 76207 USA

ISBN: (纸本)9781728174457

This paper presents a novel approach to securing IoT devices by leveraging DDoS Open Threat Signaling (DOTS) architecture on a Blockchain framework. Like many areas of the information technology domain, IoT sensors are also prone to attacks but on a larger scale. There are millions of devices being connected to a central domain to provide different types of services. Since these low-powered IoT devices have constrained technical requirements with less computational capabilities, they lack the capacity to judge their behavior as benign or malignant. IoT relies heavily on the higher level of intelligent nodes to decide on their status. An IoT Controller/Edge server handles the registration and the limited management of devices. Since traditional security is unable to protect the IoT environment sufficiently, we present a Blockchain-based DDoS detection approach to secure and mitigate such attacks in the IoT environment. Our test setup includes dataset from four sensors over two months. These values were tested using a threshold calculation against the variation of temperature, humidity, pressure, and wind direction on that day to find out whether an IoT sensor is under a DDoS attack. Our results show how DOTS can help in detection of attack when mapped on IoT edge computing.

关键词： Cybersecurity Blockchain IoT Edge computing distributed Ledger DDOS Protection Smart Contracts

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Automatic Selection of Tuning Plugins in PTF Using Machine Learning 34

Automatic Selection of Tuning Plugins in PTF Using Machine L...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Mijakovic, Robert Gerndt, Michael Bavarian Acad Sci & Humanities Leibniz Supercomp Ctr LRZ Garching Germany Tech Univ Munich Inst Informat Garching Germany

ISBN: (纸本)9781728174457

Performance tuning of scientific codes often requires tuning many different aspects like vectorization, OpenMP synchronization, MPI communication, and load balancing. The Periscope Tuning Framework (PTF), an online automatic tuning framework, relies on a flexible plugin mechanism providing tuning plugins for different tuning aspects. Individual plugins can be combined for convenience into meta-plugins. Since each plugin can take considerable execution time for testing various combination of the tuning parameters, it is desirable to automatically predict the tuning potential of plugins for programs before their application. We developed a generic automatic prediction mechanism based on machine learning techniques for this purpose. This paper demonstrates this technique in the context of the Compiler Flags Selection plugin, that tunes the parameters of a user specified compiler for a given application.

关键词： parallel computing performance tuning auto-tuning PTF program characterization machine learning

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CycLedger: A Scalable and Secure parallel Protocol for distributed Ledger via Sharding 34

CycLedger: A Scalable and Secure Parallel Protocol for Distr...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Zhang, Mengqian Li, Jichen Chen, Zhaohua Chen, Hongyin Deng, Xiaotie Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9781728168760

Traditional public distributed ledgers have not been able to scale-out well and work efficiently. Sharding is deemed as a promising way to solve this problem. By partitioning all nodes into small committees and letting them work in parallel, we can significantly lower the amount of communication and computation, reduce the overhead on each node's storage, as well as enhance the throughput of the distributed ledger. Existing sharding-based protocols still suffer from several serious drawbacks. The first thing is that all non-faulty nodes must connect well with each other, which demands a huge number of communication channels in the network. Moreover, previous protocols have faced great loss in efficiency in the case where the honesty of each committee's leader is in question. At the same time, no explicit incentive is provided for nodes to actively participate in the protocol. We present CycLedger, a scalable and secure parallel protocol for distributed ledger via sharding. Our protocol selects a leader and a partial set for each committee, who are in charge of maintaining intra-shard consensus and communicating with other committees, to reduce the amortized complexity of communication, computation, and storage on all nodes. We introduce a novel semi-commitment scheme between committees and a recovery procedure to prevent the system from crashing even when leaders of committees are malicious. To add incentive for the network, we use the concept of reputation, which measures each node's trusty computing power. As nodes with a higher reputation receive more rewards, there is an encouragement for nodes with strong computing ability to work honestly to gain reputation. In this way, we strike out a new path to establish scalability, security, and incentive for the sharding-based distributed ledger.

关键词： distributed Ledger Sharding Scalability Security Reputation

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An Efficient Data Structure for Sparse Bit-Vectors with Applications in Neuromorphic computing

An Efficient Data Structure for Sparse Bit-Vectors with Appl...

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international symposium on Advanced Research in Asynchronous Circuits and Systems

作者： Prafull Purohit Johannes Leugering Rajit Manohar Electrical Engineering Yale University New Haven CT United States Broadband and Broadcast Dept. Fraunhofer Institute for Integrated Circuits (IIS) Erlangen Germany

Iterating over all the elements of a set is a very common problem in highly parallel systems. In hardware, this is typically realized by either storing the set-membership of each element in a fixed-length bit-vector or by storing just the indices of the members in a dynamically sized queue. However, the former solution is only efficient in terms of memory and runtime if each element is a member of the set with approximately 50% probability, whereas the latter is only efficient if the set is extremely sparse. We propose an alternative asynchronous, concurrent, distributed data structure based on a binary tree topology that is more efficient for sets with sparsity in between these two extremes. The proposed structure allows us to construct a set by adding individual elements one at a time in arbitrary order, and to iterate over all these elements exactly once, clearing the set in the process. We analyzed this data structure, simulated its behavior in CHP, synthesized it into an asynchronous digital circuit, optimized the circuits, and performed SPICE simulation to evaluate our design. The results confirm that our proposed structure offers a low-latency, low-power solution for moderately sparse data, and may thus prove useful for asynchronous and neuromorphic systems.

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Optimistic scheduling with service guarantees

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JOURNAL OF parallel AND distributed computing 2020年 135卷 246-258页

作者： Kambatla, Karthik Yarlagadda, Vamsee Goiri, Inigo Grama, Ananth Facebook Inc 1 Hacker Way Menlo Pk CA 94025 USA Cloudera Inc 395 Page Mill Rd Palo Alto CA USA Microsoft Res 14865 NE 36th St Redmond WA USA Purdue Univ Dept CS 305 N Univ Ave W Lafayette IN 47907 USA

Data centers form the core of current information and commercial enterprise. At current scale, any improvement in data center resource utilization leads to substantial savings. We focus on the problem of scheduling jobs in distributed execution environments to improve resource utilization. Cluster schedulers like YARN and Mesos base their scheduling decisions on resource requirements provided by end users. It is hard for end-users to predict the exact amount of resources required for a task/job, especially since resource utilization can vary significantly over time and across tasks. In practice, users make highly conservative estimates of peak utilization across all tasks of a job to ensure job completion, leading to resource fragmentation and severe under utilization in production clusters. We present UBIS, a utilization-aware approach to cluster scheduling, to address resource fragmentation, and to improve cluster utilization and job throughput. UBIS considers actual usage of running tasks and schedules opportunistic work on under-utilized nodes. UBIS monitors resource usage on these nodes and preempts opportunistic containers in the event this over-subscription becomes untenable. In doing so, UBIS effectively utilizes wasted resources, while minimizing adverse effects on regularly scheduled tasks. Our implementation of UBIS on YARN demonstrates improvements of up to 30% in makespan for a representative workload and 25% in individual job durations. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Cluster scheduling distributed computing

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Data parallel Large Sparse Deep Neural Network on GPU 34

Data Parallel Large Sparse Deep Neural Network on GPU

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Sattar, Naw Safrin Arifuzzaman, Shaikh Univ New Orleans Dept Comp Sci New Orleans LA 70148 USA

ISBN: (纸本)9781728174457

Sparse Deep Neural Network (DNN) is an emerging research area since deploying deep neural networks with limited resources is very challenging. In this work, we provide a scalable solution to the Sparse DNN Challenge-a challenge posed by MIT/IEEE/Amazon ***-by designing data parallelism on GPUs. We provide a solution based on Python TensorFlow as it is a widely used tool in different scientific applications for deep learning. We use the datasets provided by GraphChallenge, derived from the MNIST handwritten letters. We use the Synthetic DNNs from RadiX-Net with varying number of neurons and layers. We implement a data parallel implementation of Sparse DNN using TensorFlow on GPU. Our solution shows up to 4.7x speedup over the basehne serial MATLAB implementation given in GraphChallenge. In addition to that, our TensorFlow GPU implementation demonstrates a 3-fold speedup over our TensorFloW CPU implementation.

关键词： deep neural network sparse data parallel computing GPU TensorFlow

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Exploiting Reduced Precision for GPU-based Time Series Mining

Exploiting Reduced Precision for GPU-based Time Series Minin...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yi Ju Amir Raoofy Dai Yang Erwin Laure Martin Schulz Max Plank Computing and Data Facility Technical University of Munich NVIDIA GmbH

The mining of multi-dimensional time series is a crucial step in gaining insights into data obtained from physical systems and from monitoring infrastructures. A widely accepted approach for this challenge is the matrix profile, which, however, is computationally very expensive. It relies on calculating large correlation matrices coupled with sort operations across all dimensions of the data, as well as on performing inclusive scans. All of these steps are inherently data parallel and can, therefore, benefit from execution on GPUs, and even more so from horizontal scaling on multiple GPUs. In addition, the nature of the matrix profile calculation allows the exploitation of reduced precision on GPUs. This offers further improvements to enable the analysis of ever growing data sets in real-world scenarios. Based on these motivations, we introduce the first parallel algorithm for multi-dimensional matrix profile on multiple GPUs exploiting reduced precision modes and provide a highly opti-mized implementation using novel optimization techniques. On one NVIDIA A100 GPU, our implementation achieves a 54x performance improvement in comparison to an optimized single-node execution on a state-of-the-art CPU-based implementation relying on double-precision computation and an additional factor of 1.4x when switching to reduced precision while maintaining sufficient accuracy. We study the accuracy and performance trade-offs for our proposed algorithm in detail and present synthetic and real-world case studies to demonstrate how the reduced precision improves the performance, while accomplishing sufficiently accurate results.

关键词： Time series analysis Layout Graphics processing units Switches Data mining Synchronization parallel algorithms

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A parallel compression framework for fractal images using DCT block classification

A parallel compression framework for fractal images using DC...

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IEEE international symposium on parallel and distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Jiahao Pang Jie He Hui Guo Minglang Chen Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

Fractal image compression has higher space domain compression performance compared to the mainstream image compression algorithms using discrete cosine transform, but the search time for coding fractal image compression is too long and less practical. In this paper, we propose a fractal image block classification scheme based on discrete cosine transform, and use NVIDIA CUDA architecture to achieve parallel coding, which shows that this scheme can achieve 107 times faster speedup than the basic fractal scheme, and the coding time is significantly reduced, and the PSNR decreases only about 0.4, so this scheme has greater practicality.

关键词： Image coding Graphics processing units Computer architecture Ubiquitous computing Fractals Classification algorithms Discrete cosine transforms

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parallel Query Service for Object-centric Data Management Systems 34

Parallel Query Service for Object-centric Data Management Sy...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Tang, Houjun Byna, Suren Dong, Bin Koziol, Quincey Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

ISBN: (纸本)9781728174457

While large-scale scientific experiments and simulations produce massive amounts of data, a small fraction of data contains useful information. Efficient querying on such volume of data to extract that information increases the productivity of the scientific discovery process. Although querying has been explored extensively in relational databases, research and adoption of querying tools for scientific data that is stored in parallel file systems on high performance computing (HPC) systems are still in infancy. In this paper, we introduce a parallel query service, called PDC-Query, for an object data management systems (ODMS) on HPC systems. It operates on partitioned objects in parallel, and provides several optimization strategies for fast query evaluation. The ODMS paradigm for HPC systems is promising in reducing the burden on users in data management and in moving data transparently across the deep memory hierarchy in modern HPC systems. We propose a 'global histogram'-based approach to accelerate query evaluation, through selectivity estimation and reducing the amount of data that needs to be loaded from storage and processed. We compare querying performance and demonstrate the efficiency and scalability of different approaches PDC-Query supports, including using global histograms, bitmap indexes, sorting, and full scan, in performing various queries on top of a plasma physics dataset with 125 billion particles and an astronomy dataset with 25 million objects.

关键词： Information management

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Coupling streaming AI and HPC ensembles to achieve 100–1000× faster biomolecular simulations

Coupling streaming AI and HPC ensembles to achieve 100–1000...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Alexander Brace Igor Yakushin Heng Ma Anda Trifan Todd Munson Ian Foster Arvind Ramanathan Hyungro Lee Matteo Turilli Shantenu Jha Data Science and Learning Division Argonne National Laboratory Lemont USA Department of Computer Science University of Chicago Chicago USA Data Science and Learning Division Argonne National LaboratoryLemontUSA Center For Biophysics University of Illinois Urbana-Champaign Champaign USA Mathematics and Computer Science Division Argonne National LaboratoryLemontUSA RADICAL-Lab Rutgers University New Brunswick USA Computational Science Initiative Brookhaven National Laboratory Upton USA

Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-of-magnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100-1000× acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadership-class platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.

关键词： Proteins Couplings Computers Accelerometers Solid modeling distributed processing Computational modeling

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