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18th ACM/IFIP/USENIX International Middleware Conference (Middleware)

作者： Mayer, Ruben Slo, Ahmad Tariq, Muhammad Adnan Rothermel, Kurt Graeber, Manuel Ramachandran, Umakishore Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany FAST Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad Pakistan Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

ISBN: (纸本)9781450347204

Distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of complex situations in the surrounding world from basic events like sensor readings. In doing so, DCEP operators detect event patterns on their incoming event streams. To yield high operator throughput, data parallelization frameworks divide the incoming event streams of an operator into overlapping windows that are processed in parallel by a number of operator instances. In doing so, the basic assumption is that the different windows can be processed independently from each other. However, consumption policies enforce that events can only be part of one pattern instance;then, they are consumed, i.e., removed from further pattern detection. That implies that the constituent events of a pattern instance detected in one window are excluded from all other windows as well, which breaks the data parallelism between different windows. In this paper, we tackle this problem by means of speculation: Based on the likelihood of an event's consumption in a window, subsequent windows may speculatively suppress that event. We propose the SPECTRE framework for speculative processing of multiple dependent windows in parallel. Our evaluations show an up to linear scalability of SPECTRE with the number of CPU cores.

关键词： Complex Event Processing data parallelization Event Consumption Consumption Policy Speculation

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WORKLOAD-AWARE AUTOMATIC parallelization FOR MULTI-GPU DNN TRAINING 44

WORKLOAD-AWARE AUTOMATIC PARALLELIZATION FOR MULTI-GPU DNN T...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Shin, Sungho Jo, Youngmin Choi, Jungwook Venkataramani, Swagath Srinivasan, Vijayalakshmi Sung, Wonyong Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea IBM Res AI 1101 Kitchawan Rd Yorktown Hts NY 10598 USA

ISBN: (纸本)9781479981311

Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intelligence applications, but their very large and deep models impose high computational requirements during training. Multi-GPU parallelization is a popular option to accelerate demanding computations in DNN training, but most state-of-the-art multi-GPU deep learning frameworks not only require users to have an in-depth understanding of the implementation of the frameworks themselves, but also apply parallelization in a straight-forward way without optimizing GPU utilization. In this work, we propose a workload-aware auto-parallelization framework (WAP) for DNN training, where the work is automatically distributed to multiple GPUs based on the workload characteristics. We evaluate WAP using TensorFlow with popular DNN benchmarks (AlexNet and VGG-16), and show competitive training throughput compared with the state-of-the-art frameworks, and also demonstrate that WAP automatically optimizes GPU assignment based on the workload's compute requirements, thereby improving energy efficiency.

关键词： Multi-GPU training data parallelization auto parallelization neural network training deep learning framework

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Parallel Training of Neural Networks for Speech Recognition

Parallel Training of Neural Networks for Speech Recognition

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13th International Conference on Text, Speech and Dialogue

作者： Vesely, Karel Burget, Lukas Grezl, Frantisek Brno Univ Technol Speech FIT Brno 61266 Czech Republic

The feed-forward multi-layer neural networks have significant importance in speech recognition. A new parallel-training tool TNet was designed and optimized for multiprocessor computers. The training acceleration rate... 详细信息

ISBN: (纸本)9783642157592

关键词： neural network phoneme classification posterior features backpropagation training data parallelization

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Meeting Predictable Buffer Limits in the Parallel Execution of Event Processing Operators 2

Meeting Predictable Buffer Limits in the Parallel Execution ...

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IEEE International Conference on Big data

作者： Mayer, Ruben Koldehofe, Boris Rothermel, Kurt Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781479956661

Complex Event Processing (CEP) systems enable applications to react to live-situations by detecting event patterns (complex events) in data streams. With the increasing number of data sources and the increasing volume at which data is produced, parallelization of event detection is becoming of tremendous importance to limit the time events need to be buffered before they actually can be processed by an event detector-named event processing operator. In this paper, we propose a pattern-sensitive partitioning model for data streams that is capable of achieving a high degree of parallelism for event patterns which formerly could only be consistently detected in a sequential manner or at a low parallelization degree. Moreover, we propose methods to dynamically adapt the parallelization degree to limit the buffering imposed on event detection in the presence of dynamic changes to the workload. Extensive evaluations of the system behavior show that the proposed partitioning model allows for a high degree of parallelism and that the proposed adaptation methods are able to meet the buffering level for event detection under high and dynamic workloads.

关键词： buffer storage data handling parallel processing CEP systems buffering complex event processing systems data sources data streams data volume dynamic workloads event detection parallelization event patterns detection event processing operators parallel execution parallelization degree pattern-sensitive partitioning model predictable buffer limits system behavior time events Adaptation models Corporate acquisitions Correlation Delays Event detection Middleware Monitoring Complex Event Processing data parallelization Quality of Service Self-Adaptation Stream Processing

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Implementing Block Cipher on Embedded Multiprocessors Platform

Implementing Block Cipher on Embedded Multiprocessors Platfo...

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International Conference on Multimedia Computing and Systems

作者： Khaddour, M. Wang, Z. Hammami, O. ENSTA Lab Elect & Informat Paris France

ISBN: (纸本)9781424437566

Multiprocessor platforms are gaining markets as a solution to boost general performance of processor beyond technological limitations that are present in single processors chips, Multi-processor in embedded systems also have a future in particular with applications like SDR(Software Defined Radio) where both high performance and high adaptability are required. Cryptographic algorithms implementation on embedded systems is also a hot topic for the rapidly developing wireless communication networks. In this paper we examine the implementation of the computation-intensive block ciphers AES and TDES algorithms on a 16 processors platform implemented on FPGA, we implemented a CBC operation mode which suites mass encryption on the platform and we obtained a linear speedup in computation time

关键词： AES CBC mode data parallelization Embedded Multiprocessors FPGA TDES Virtex4

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Parallel Algorithm For Constructing a Cubic Spline on Multi-Core Processors in a Cluster 14

Parallel Algorithm For Constructing a Cubic Spline on Multi-...

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14th IEEE International Conference on Application of Information and Communication Technologies (AICT)

作者： Zaynidinov, Hakimjon Mallayev, Oybek Nurmurodov, Javohir TUIT Comp Engn Tashkent Uzbekistan

ISBN: (纸本)9781728173863

The article explores the possibility of computing parallel data compression using cubic spline. For example, ways to parallel the process of digital processing of seismic signals have been considered. The main performance indicators of parallel algorithms have been compared with consecutive algorithms. Spline methods are a versatile signal processing tool. It is more accurate than other mathematical methods, information equality is faster, and maintenance costs are much lower. On the other hand, the equipment used in such systems must also meet high performance requirements. To achieve high speeds, parallel algorithms were developed using OpenMP and MPI technologies and implemented in the architecture of multi-core processors. A mathematical method for the parallel calculation of the coefficients of a cubic spline has been developed and a parallel signal processing algorithm has been developed on its basis. As an example, parallelization is a computation during seismic signal processing. The main indicators of efficiency and acceleration of the parallel algorithm were compared with the sequential algorithm. Explained the relevance of the use of parallel numerical systems, described the main approaches to the distribution of processes and methods of data processing, described the principles of parallel programming technology, studied the basic parameters of parallel algorithms for the initial calculation of the numerical value of cubic spline. The parallel algorithm considered for constructing the cubic spline of defect 1 as p - > n leads to the construction of a local cubic spline on each grid interval omega.

关键词： Parallel computing UMA NUMA SMP data parallelization task parallelization data processing MPI

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Paralelní trénování neuronových sítí pro rozpoznávání řeči

Paralelní trénování neuronových sítí pro rozpoznává...

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作者： Veselý, Karel Brno University of Technology

Tato diplomová práce je zaměřena na paralelizaci trénování neuronových sítí pro rozpoznávání řeči. V rámci této diplomové práce byly implemen... 详细信息

Tato diplomová práce je zaměřena na paralelizaci trénování neuronových sítí pro rozpoznávání řeči. V rámci této diplomové práce byly implementovány a porovnány dvě strategie paralelizace. První strategií je paralelizace dat s využitím rozdělení trénování do několika POSIX vláken. Druhou strategií je paralelizace uzlů s využitím platformy pro obecné výpočty na grafických kartách CUDA. V případě první strategie bylo dosaženo 4x urychlení, v případě využití platformy CUDA bylo dosaženo téměř 10x urychlení. Pro trénování byl použit algoritmus Stochastic Gradient Descent se zpětným šířením chyb. Po krátkém úvodu následuje druhá kapitola práce, která je motivační a zasazuje probém do kontextu rozpoznávání řeči. Třetí kapitola práce je teoretická a diskutuje neuronové sítě a metodu trénování. Následující kapitoly jsou zaměřené na návrh a implementaci a popisují iterativní vývoj tohoto projektu. Poslední obsáhlá kapitola popisuje testovací systém a uvádí výsledky provedených experimentů. V závěru jsou krátce zhodnoceny dosažené výsledky a nastíněna perspektiva dalšího vývoje projektu.

关键词： neuronová síť akustický model rozpoznávání řeči rychlé trénování paralelizace dat POSIX vlákna paralelizace uzlů CUDA GPGPU neural network acoustic model speech recognition fast training data parallelization POSIX threads node parallelizati

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Optimizing Explicit data Transfers for data Parallel Applications on the Cell Architecture

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2012年第4期8卷 37-37页

作者： Saidi, Selma Tendulkar, Pranav Lepley, Thierry Maler, Oded STMicroelect Grenoble Grenoble France CNRS Verimag Lab Grenoble France Univ Grenoble Verimag Lab Grenoble France

In this paper we investigate a general approach to automate some deployment decisions for a certain class of applications on multi-core computers. We consider data-parallelizable programs that use the well-known double buffering technique to bring the data from the off-chip slow memory to the local memory of the cores via a DMA (direct memory access) mechanism. Based on the computation time and size of elementary data items as well as DMA characteristics, we derive optimal and near optimal values for the number of blocks that should be clustered in a single DMA command. We then extend the results to the case where a computation for one data item needs some data in its neighborhood. In this setting we characterize the performance of several alternative mechanisms for data sharing. Our models are validated experimentally using a cycle-accurate simulator of the Cell Broadband Engine architecture.

关键词： Design Algorithms Performance data parallelization double buffering direct memory access (DMA) Cell BE

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OVarFlow: a resource optimized GATK 4 based Open source Variant calling workFlow

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BMC BIOINFORMATICS 2021年第1期22卷 402-402页

作者： Bathke, Jochen Luehken, Gesine Justus Liebig Univ Giessen Inst Anim Breeding & Genet Ludwigstr 21 D-35390 Giessen Germany

Background The advent of next generation sequencing has opened new avenues for basic and applied research. One application is the discovery of sequence variants causative of a phenotypic trait or a disease pathology. The computational task of detecting and annotating sequence differences of a target dataset between a reference genome is known as "variant calling". Typically, this task is computationally involved, often combining a complex chain of linked software tools. A major player in this field is the Genome Analysis Toolkit (GATK). The "GATK Best Practices" is a commonly referred recipe for variant calling. However, current computational recommendations on variant calling predominantly focus on human sequencing data and ignore ever-changing demands of high-throughput sequencing developments. Furthermore, frequent updates to such recommendations are counterintuitive to the goal of offering a standard workflow and hamper reproducibility over time. Results A workflow for automated detection of single nucleotide polymorphisms and insertion-deletions offers a wide range of applications in sequence annotation of model and non-model organisms. The introduced workflow builds on the GATK Best Practices, while enabling reproducibility over time and offering an open, generalized computational architecture. The workflow achieves parallelized data evaluation and maximizes performance of individual computational tasks. Optimized Java garbage collection and heap size settings for the GATK applications SortSam, MarkDuplicates, HaplotypeCaller, and GatherVcfs effectively cut the overall analysis time in half. Conclusions The demand for variant calling, efficient computational processing, and standardized workflows is growing. The Open source Variant calling workFlow (OVarFlow) offers automation and reproducibility for a computationally optimized variant calling task. By reducing usage of computational resources, the workflow removes prior existing entry barriers to the variant

关键词： Variant calling SNP indel GATK Next generation sequencing Reproducibility data parallelization Benchmarking Java

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