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Highly Scalable parallel Checksums

Highly Scalable Parallel Checksums

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international conference on parallel and distributed Systems (ICPADS)

作者： Christian Siebert Heinrich Heine University Düsseldorf Centre for Information and Media Technology Düsseldorf Germany

ISBN: (纸本)9781665408790

Checksums are used to detect errors that might occur while storing or communicating data. Checking the integrity of data is well-established, but only for smaller data sets. Contrary, supercomputers have to deal with huge amounts of data, which introduces failures that may remain undetected. Therefore, additional protection becomes a necessity at large scale. However, checking the integrity of larger data sets, especially in case of distributed data, clearly requires parallel approaches. We show how popular checksums, such as CRC-32 or Adler-32, can be parallelized efficiently. This also disproves a widespread belief that parallelizing aforementioned checksums, especially in a scalable way, is not possible. The mathematical properties behind these checksums enable a method to combine partial checksums such that its result corresponds to the checksum of the concatenated partial data. Our parallel checksum algorithm utilizes this combination idea in a scalable hierarchical reduction scheme to combine the partial checksums from an arbitrary number of processing elements. Although this reduction scheme can be implemented manually using most parallel programming interfaces, we use the Message Passing Interface, which supports such a functionality directly via non-commutative user-defined reduction operations. In conjunction with the efficient checksum capabilities of the zlib library, our algorithm can not only be implemented conveniently and in a portable way, but also very efficiently. Additional shared-memory parallelization within compute nodes completes our hybrid parallel checksum solutions, which show a high scalability of up to 524,288 threads. At this scale, computing the checksums of 240 TiB data took only 3.4 seconds for CRC-32 and 2.6 seconds for Adler-32. Finally, we discuss the APES application as a representative of dynamic supercomputer applications. Thanks to our scalable checksum algorithm, even such applications are now able to detect many errors withi

关键词： Runtime parallel programming Heuristic algorithms Scalability Message passing distributed databases Supercomputers

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Machine learning based network intrusion detection for data streaming IoT applications

Machine learning based network intrusion detection for data ...

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Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Winter), ACIS international Winter conference on

作者： Aymen Yahyaoui Haithem Lakhdhar Takoua Abdellatif Rabah Attia SERCOM Lab University of Carthage Military Academy of Fondouk Jedid Tunisia

In recent years, Internet of Things (IoT) technologies have been widely used in many fields such as surveillance, health-care, smart metering and environment monitoring. This extensive usage leads to massive data management and a complexity in data analysis. A huge number of IoT sensors are deployed for monitoring task and send continuously their collected data to gateways. IoT applications are analyzing these data flows and making real time decisions about specific monitored events (fire, flood, terrorist attacks, etc.). Anomalies that may be related to sensor failures or network intrusions are affecting such decisions. Therefore, they should be detected and eliminated as soon as they arrive. This task requires real time data processing detectors for making accurate and fast predictions. In this paper, we design an architecture for a real time network intrusion detection system for IoT streaming data. The system was developed, deployed and tested with the two leading stream processing frameworks (Apache Flink and Apache Spark Streaming). We used two different public datasets and different machine learning algorithms. Results show considerable throughputs and high detection accuracy especially for Apache Flink.

关键词： Terrorism Surveillance Network intrusion detection Throughput Real-time systems Internet of Things Task analysis

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In Situ Visualization of Performance-Related Data in parallel CFD applications 25th

In Situ Visualization of Performance-Related Data in Paralle...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Alves, Rigel F. C. Knuepfer, Andreas Tech Univ Dresden Ctr Informat Serv & High Performance Comp ZIH D-01062 Dresden Germany

ISBN: (纸本)9783030483401;9783030483395

This paper aims at investigating the feasibility of using ParaView as visualization software for the analysis and optimization of parallel CFD codes' performance. The currently available software tools for reading profiling data do not match the generated measurements to the simulation's original mesh and somehow aggregate them (rather than showing them on a time-step basis). A plugin for the open-source performance tool Score-P has been developed, which intercept an arbitrary number of manually selected code regions (mostly functions) and send their respective measurements - amount of executions and cumulative time spent - to ParaView (through its in situ library, Catalyst), as if they were any other flow-related variable. Results show that (i) the impact of mesh partition algorithms on code performance and (ii) the load imbalances (and their eventual relationship to mesh size/simulation physics) become easier to investigate.

关键词： parallel computing Performance analysis In situ processing

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parallel analysis of TACLeBench kernel benchmark’s loop and procedure level speculation

Parallel analysis of TACLeBench kernel benchmark’s loop and...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Huiling Meng Yaobin Wang Ling Li Manasah Musariri Xinyi Wang Southwest University of Science and Technology Mianyang China

Thread level speculation (TLS) technology has gained substantial international recognition due to its unique parallel program execution. It uses the potential parallel execution of programs to improve the utilization of multi-core resources. However, the kernel benchmark of TACLeBench has not effectively been analyzed using TLS parallelization. In response to this problem, we select 7 programs in the TACLeBench kernel benchmark to analyze its loop and procedure level speculation executions to measure their maximum potential parallelism. Furthermore, we discuss its runtime characteristics (thread size, speculative parallelism coverage, dependency feature) and the influence of program source code on speedup. Our experimental results illustrate that: 1) Most applications achieve impressive results. Bsort reaches 20.79x speedup in loop level speculation, and lms has 9.51x speedup in procedure level speculation; 2) By using TLS to accelerate the TACLeBench kernel benchmark, most applications effectively utilize computing resources from 4 to 16 cores; 3) The kernel benchmark is more suitable for developing parallelism in loop level speculation.

关键词： Runtime Codes Instruction sets Benchmark testing parallel processing Kernel

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Exploring the role of vegetation indices in plant diseases identification 6

Exploring the role of vegetation indices in plant diseases i...

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6th international conference on parallel, distributed and Grid Computing, PDGC 2020

作者： Meena, Sangeeta Vaibhav Dhaka, Vijaypal Singh Sinwar, Deepak Manipal University Jaipur Department of Computer Applications Rajasthan Jaipur India Manipal University Jaipur Department of Computer and Communication Engineering Rajasthan Jaipur India

ISBN: (纸本)9781728171326

The economy of the agriculture industry is badly affected by plant diseases. Effective management practices involve regular monitoring of the plant's health with early detection of pathogens for reducing the spread of diseases. Traditionally, several invasive plant disease diagnostic techniques are used that involve the devastation of leaf samples with chemical treatment. Apart from that, non-invasive disease detection techniques are more feasible and practical ways of monitoring plant diseases in real time applications without affecting the growth of plants. Imaging and spectroscopic are non-invasive disease identification methods used for discovering harmful organisms that affect the health of plants. For identifying diseases, biophysical parameters of plants are extracted through vegetation indices. A vegetation index is a spectral computation that can be done using two or more spectral bands that are sensitive to plant vigor and biomass. Vegetation indices are used to estimate water contents of soils, monitor drought, classify vegetation, examine climate trends, crop management, identify changes in biodiversity, etc. The paper aims to discuss various methods used for detecting plant diseases. Some commonly used vegetation indices are also discussed along with the role of vegetation indices in identifying plant diseases. © 2020 IEEE.

关键词： Spectroscopy

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Making Memristive processing-in-Memory Reliable

Making Memristive Processing-in-Memory Reliable

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IEEE international conference on Electronics, Circuits and Systems (ICECS)

作者： Orian Leitersdorf Ronny Ronen Shahar Kvatinsky Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel

processing-in-memory (PIM) solutions vastly accelerate systems by reducing data transfer between computation and memory. Memristors possess a unique property that enables storage and logic within the same device, which is exploited in the memristive Memory processing Unit (mMPU). The mMPU expands fundamental stateful logic techniques, such as IM-PLY, MAGIC and FELIX, to high-throughput parallel logic and arithmetic operations within the memory. Unfortunately, memristive processing-in-memory is highly vulnerable to soft errors and this massive parallelism is not compatible with traditional reliability techniques, such as error-correcting-code (ECC). In this paper, we discuss reliability techniques that efficiently support the mMPU by utilizing the same principles as the mMPU computation. We detail ECC techniques that utilize the unique properties of the mMPU for efficiently utilizing the massive parallelism. Furthermore, we present novel solutions for efficiently implementing triple modular redundancy (TMR). The short-term and long-term reliability of large-scale applications, such as neural-network acceleration, are evaluated. The analysis clearly demonstrates the importance of high-throughput reliability mechanisms for memristive processing-in-memory.

关键词： conferences Redundancy Memristors parallel processing Data transfer Error correction codes Computational efficiency

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Survey and Performance Test of Python-based Libraries for parallel processing 2020

Survey and Performance Test of Python-based Libraries for Pa...

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The 9th international conference on Smart Media and applications

作者： TaeHong Kim YoonSeok Cha ByeonChun Shin ByungRae Cha Gwangju Metro-city S. Korea Chonnam National Uniersity S. Korea

ISBN: (纸本)9781450389259

By the Fourth Industrial Revolution and the 10 strategic technology of the Gartner Group, Artificial Intelligence(AI) technology was important and affected many areas. One of the ways to accelerate AI services is the Python-based parallel processing library. High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. This migration towards orchestration rather than implementation, coupled with the growing need for parallel computing (e.g., due to big data and the end of Moore's law), necessitates rethinking how parallelism is expressed in programs.[1] In this paper, take a look at a Python-based distributed parallel processing library, one of the ways to accelerate AI services, and use it to compare serial and parallel processing times.

关键词： Artificial Intelligence

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LightSaber: Efficient Window Aggregation on Multi-core Processors 20

LightSaber: Efficient Window Aggregation on Multi-core Proce...

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ACM SIGMOD international conference on Management of Data (SIGMOD)

作者： Theodorakis, Georgios Koliousis, Alexandros Pietzuch, Peter Pirk, Holger Imperial Coll London London England Graphcore Res Bristol Avon England

ISBN: (纸本)9781450367356

Window aggregation queries are a core part of streaming applications. To support window aggregation efficiently, stream processing engines face a trade-off between exploiting parallelism (at the instruction/multi-core levels) and incremental computation (across overlapping windows and queries). Existing engines implement ad-hoc aggregation and parallelization strategies. As a result, they only achieve high performance for specific queries depending on the window definition and the type of aggregation function. We describe a general model for the design space of window aggregation strategies. Based on this, we introduce LightSaber, a new stream processing engine that balances parallelism and incremental processing when executing window aggregation queries on multi-core CPUs. Its design generalizes existing approaches: (i) for parallel processing, LightSaber constructs a parallel aggregation tree (PAT) that exploits the parallelism of modern processors. The PAT divides window aggregation into intermediate steps that enable the efficient use of both instruction-level (i.e., SIMD) and task-level (i.e., multi-core) parallelism;and (ii) to generate efficient incremental code from the PAT, LightSaber uses a generalized aggregation graph (GAG), which encodes the low-level data dependencies required to produce aggregates over the stream. A GAG thus generalizes state-of-the-art approaches for incremental window aggregation and supports work-sharing between overlapping windows. LightSaber achieves up to an order of magnitude higher throughput compared to existing systems-on a 16-core server, it processes 470 million records/s with 132 mu s average latency.

关键词： stream processing window aggregation incremental computation

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DAFEE: A Scalable distributed Automatic Feature Engineering Algorithm for Relational Datasets 20th

DAFEE: A Scalable Distributed Automatic Feature Engineering ...

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20th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Zhao, Wenqian Li, Xiangxiang Rong, Guoping Lin, Mufeng Lin, Chen Yang, Yifan Transwarp Technol Shanghai Co Ltd Shanghai Peoples R China Nanjing Univ Joint Lab Nanjing Univ & Transwarp Data Technol Nanjing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Automatic feature engineering aims to construct informative features automatically and reduce manual labor for machine learning applications. The majority of existing approaches are designed to handle tasks with only one data source, which are less applicable to real scenarios. In this paper, we present a distributed automatic feature engineering algorithm, DAFEE, to generate features among multiple large-scale relational datasets. Starting from the target table, the algorithm uses a Breadth-First-Search type algorithm to find its related tables and constructs advanced high-order features that are remarkably effective in practical applications. Moreover, DAFEE implements a feature selection method to reduce the computational cost and improve predictive performance. Furthermore, it is highly optimized to process a massive volume of data. Experimental results demonstrate that it can significantly improve the predictive performance by 7% compared to SOTA algorithms.

关键词： AutoML Automatic feature engineering Relational dataset Big data Feature selection Machine learning

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SAPipe: staleness-aware pipeline for data-parallel DNN training 22

SAPipe: staleness-aware pipeline for data-parallel DNN train...

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Proceedings of the 36th international conference on Neural Information processing Systems

作者： Yangrui Chen Cong Xie Meng Ma Juncheng Gu Yanghua Peng Haibin Lin Chuan Wu Yibo Zhu The University of Hong Kong ByteDance

ISBN: (纸本)9781713871088

Data parallelism across multiple machines is widely adopted for accelerating distributed deep learning, but it is hard to achieve linear speedup due to the heavy communication. In this paper, we propose SAPipe, a performant system that pushes the training speed of data parallelism to its fullest extent. By introducing partial staleness, the communication overlaps the computation with minimal staleness in SAPipe. To mitigate additional problems incurred by staleness, SAPipe adopts staleness compensation techniques including weight prediction and delay compensation with provably lower error bounds. Additionally, SAPipe presents an algorithm-system co-design with runtime optimization to minimize system overhead for the staleness training pipeline and staleness compensation. We have implemented SAPipe in the BytePS framework, compatible to both TensorFlow and PyTorch. Our experiments show that SAPipe achieves up to 157% speedups over BytePS (non-stale), and outperforms PipeSGD in accuracy by up to 13.7%.

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