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DataX Allocator: Dynamic resource management for stream analytics at the Edge

DataX Allocator: Dynamic resource management for stream anal...

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international conference on Internet of things: Systems, Management and Security

作者： Priscilla Benedetti Giuseppe Coviello Kunal Rao Srimat Chakradhar Vrije Universiteit Brussel - Brussels Belgium and University of Perugia Perugia Italy NEC Laboratories America Inc Princeton NJ USA

Serverless edge computing aims to deploy and manage applications so that developers are unaware of challenges associated with dynamic management, sharing, and maintenance of the edge infrastructure. However, this is a non-trivial task because the resource usage by various edge applications varies based on the content in their input sensor data streams. We present a novel reinforcement-learning (RL) technique to maximize the processing rates of applications by dynamically allocating resources (like CPU cores or memory) to microservices in these applications. We model applications as analytics pipelines consisting of several microservices, and a pipeline’s processing rate directly impacts the accuracy of insights from the application. In our unique problem formulation, the state space or the number of actions of RL is independent of the type of workload in the microservices, the number of microservices in a pipeline, or the number of pipelines. this enables us to learn the RL model only once and use it many times to improve the accuracy of insights for a diverse set of AI/ML engines like action recognition or face recognition and applications with varying *** experiments with real-world applications, i.e., face recognition and action recognition, show that our approach outperforms other widely-used alternative approaches and achieves up to 2.5X improvement in the overall application processing rate. Furthermore, when we apply our RL model trained on a face recognition pipeline to a different and more complex action recognition pipeline, we obtain a 2X improvement in processing rate, thus showing the versatility and robustness of our RL model to pipeline changes.

关键词： Face recognition Visual analytics Pipelines Microservice architectures Maintenance engineering Robustness Resource management

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parallel processing algorithms for the Vehicle Routing Problem and Its Variants: A Literature Review with a Look into the Future 20th

Parallel Processing Algorithms for the Vehicle Routing Probl...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Rabbouch, Bochra Rabbouch, Hana Saadaoui, Foued Zone Industrielle Manzel Hayet LEONI Wiring Syst Monastir 5033 Tunisia Univ Monastir Fac Sci Lab LR18ES15 Algebre Theorie Nombres & Anal Nonli Monastir 5019 Tunisia King Abdulaziz Univ Fac Sci Dept Stat POB 80203 Jeddah 21589 Saudi Arabia

ISBN: (纸本)9783030602451;9783030602444

Vehicle Routing Problems (VRPs) are well-know combinatorial optimization problems used to design an optimal route for a fleet of vehicles to service a set of customers under a number of constraints. Due to their NP-hard complexity, a number of purely computational techniques have been proposed in recent years in order to solve them. Among these techniques, nature-inspired algorithms have proven their effectiveness in terms of accuracy and convergence speed. Some of these methods are also designed in such a way to decompose the basic problem into a number of sub-problems which are subsequently solved in parallel computing environments. It is therefore the purpose of this paper to review the fresh corpus of the literature dealing with the main approaches proposed over the past few years to solve combinatorial optimization problems in general and, in particular, the VRP and its different variants. Bibliometric and review studies are conducted with a special attention paid to metaheuristic strategies involving procedures with parallel architectures. the obtained results show an expansion of the use of parallel algorithms for solving various VRPs. Nevertheless, the regression in the number of citations in this framework proves that the interest of the research community has declined somewhat in recent years. this decline may be explained by the lack of rigorous mathematical results and practical interfaces under famous calculation softwares.

关键词： Combinatorial optimization Vehicle Routing Problem Metaheuristics Nature-inspired algorithms parallel computing

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Scalable Sequence Clustering for Large-Scale Immune Repertoire Analysis

Scalable Sequence Clustering for Large-Scale Immune Repertoi...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Bhusal, Prem Alam, A. K. M. Mubashwir Chen, Keke Jiang, Ning Xiao, Jun Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA Marquette Univ Dept Comp Sci Milwaukee WI 53233 USA Univ Penn Dept Bioengn Philadelphia PA USA ImmuDX LLC Austin TX USA

ISBN: (纸本)9781665439022

the development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecular level that allows researchers to understand the healthiness of a patient's immune system. Recent studies have shown that the single-linkage clustering algorithm can give the best results for B cell clonality analysis - a critical type of immune repertoire sequencing (IR-Seq) analysis. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how a specific person's immune system evolves over different stages of disease development. However, the classical single-linkage clustering algorithm does not scale well to such large sequence datasets. Surprisingly, no study has been done to address this scalability issue for immunology research and development. We study three different strategies to scale up the single-linkage algorithm for sequence data. they include (1) the approximate single-linkage algorithm enhanced with the non-Euclidean indexing methods, (2) the Spark-based single-linkage algorithm (SparkMST) that was originally designed for vector data and now modified for sequence data, and (3) a new tree-based sequence summarization approach - SCT that aims to reduce the data for single-linkage clustering with well-preserved clustering quality. We have implemented these approaches and experimented with real sequence datasets for B cell clonality analysis. (1) the index-enhanced hierarchical clustering algorithm (e.g., VPT-HC using the Vantage-Point tree for indexing) preserves the clustering quality very well while significantly reducing the time complexity. (2) the SCT approach serving as a preprocessing step can effectively reduce data size for clustering. the overall clustering, SCT followed by VPT-HC, is the fastest among the evaluated single-machine algorithms. However, this approach also slightly affects the clustering quality. (3) the SparkMST paral

关键词： clustering sequence data scalability parallel processing summarization indexing

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Exploring Unsupervised Pretraining Objectives for Machine Translation

Exploring Unsupervised Pretraining Objectives for Machine Tr...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on Natural Language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Baziotis, Christos Titov, Ivan Birch, Alexandra Haddow, Barry Univ Edinburgh Inst Language Cognit & Computat Sch Informat 10 Crichton St Edinburgh EH8 9AB Midlothian Scotland

ISBN: (纸本)9781954085541

Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT), by drastically reducing the need for large parallel data. Most approaches adapt masked-language modeling (MLM) to sequence-to-sequence architectures, by masking parts of the input and reconstructing them in the decoder. In this work, we systematically compare masking with alternative objectives that produce inputs resembling real (full) sentences, by reordering and replacing words based on their context. We pretrain models with different methods on English $German, English $Nepali and English $Sinhala monolingual data, and evaluate them on NMT. In (semi-) supervised NMT, varying the pretraining objective leads to surprisingly small differences in the finetuned performance, whereas unsupervised NMT is much more sensitive to it. To understand these results, we thoroughly study the pretrained models and verify that they encode and use information in different ways. We conclude that finetuning on parallel data is mostly sensitive to few properties that are shared by most models, such as a strong decoder, in contrast to unsupervised NMT that also requires models with strong cross-lingual abilities.

关键词： Neural machine translation

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Accelerated Edge Detection Algorithm for High-Speed Applications

Accelerated Edge Detection Algorithm for High-Speed Applicat...

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Japan-Egypt international conference on Electronics, Communications and Computers (JEC-ECC)

作者： Aya Saad Khloud Rafat Ahmed Soltan M. Saeed Darweesh School of Engineering and Applied Sciences Nile University Giza Egypt Nanoelectronics Integrated Systems Center (NISC) Nile University Giza Egypt Wireless Intelligent Networks Center (WINC) Nile University Giza Egypt

ISBN: (纸本)9781665482936

Digital Image processing (DIP) is a growing field for various applications, such as autonomous vehicles and video surveillance. To improve the performance of DIP systems, image processing algorithms are implemented in hardware rather than software. the idea here is primarily to get a faster system than software imaging or other alternative hardware. Field-programmable gate arrays (FPGAs) have the advantages of parallel processing, low cost, and low power consumption. these semiconductor devices contain many logic blocks that can be programmed to perform everything from basic digital gate-level technology to complex image processing algorithms. this paper provides an enhancement pipeline system architecture using AXI interface to implement image processing algorithms such as Sobel edge detection and mean filters on the Zybo z7 Zynq 7010 board using Verilog HDL language. the system is implemented in a 512×512 image that takes 0.009ms in the processing system.

关键词： Performance evaluation Image processing Image edge detection Software algorithms Logic gates Filtering algorithms Software

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GPU parallelization and Optimization of a Combustion Simulation Application

GPU Parallelization and Optimization of a Combustion Simulat...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Zhixiang Liao Yongzhou Liu Yonggang Che State Key Laboratory of High Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

Graphics processing units (GPUs) are widely used in the area of scientific computing. While GPUs provide much higher peak performance, efficient implementation of real applications on the GPU architectures is still a non-trivial task. It is crucial to realize efficient solution algorithms that can better utilize GPU architectures. this paper presents our efforts in parallelizing and optimizing LESAP, a CFD application for scramjet combustion simulation, on NVIDIA GPUs. the GPU parallelization is realized based on the CUDA programming model, with a data-parallel implicit time-marching method that is efficient on the GPU architecture. Furthermore, shared memory and redundant calculation are proposed to reduce memory access overhead during GPU computation, and data transfer between CPU and GPU is optimized by packing the data to be transferred. the experimental results show that the GPU version, when runs on four V100 GPUs, achieves a speedup of 11.26 times compared to the CPU version that runs on two 24-core Intel Skylake Gold 6240R CPUs. Excellent parallel scalability across multiple GPUs is also observed.

关键词： Gold Smart cities Computational modeling Graphics processing units Computer architecture Combustion Central processing Unit

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parallel Software Testing Sequence Generation Method Target at Full Covering Tested Behaviors 19th

Parallel Software Testing Sequence Generation Method Target ...

引用

19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Sun, Tao Wan, Xiaoyun Zhong, Wenjie Guo, Xin Zhang, Ting Inner Mongolia Univ Coll Comp Sci Hohhot 100021 Inner Mongolia Peoples R China

ISBN: (纸本)9783030389611;9783030389604

parallel software system testing is very difficult because the number of states expands sharply caused by parallel behaviors. In this paper, a testing sequence generation method is proposed, target at full covering tested behaviors and related behaviors over all execution paths. Because of state spaces of parallel software systems are often large, this paper focuses on the state space subgraph, contained all firing of tested and related behaviors, of system model. Firstly, the software system is modeled with Colored Petri Net (CPN), called system model (SM), and every tested behavior or related behavior is modeled also with CPN, called Behavior Model Unit (BMU). then, the method proposes mapping operation, intersection operation, and so on to finally realize the generation of test sequence. Practices show that this method is efficient, which could achieve full coverage.

关键词： parallel software Testing sequence generation Full covering Tested behaviors Colored Petri Net (CPN)

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FANG: Fast and Efficient Successor-State Generation for Heuristic Optimization on GPUs 19th

FANG: Fast and Efficient Successor-State Generation for Heur...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Koester, Marcel Gross, Julian Krueger, Antonio Saarland Informat Campus Campus D3-2 D-66123 Saarbrucken Germany

ISBN: (纸本)9783030389918;9783030389901

Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the existence of a neighborhood or successor-state function N(x), where x is a candidate state. the implementation of such a function can become arbitrarily complex in the field of combinatorial optimization. Many N(x) functions for a huge variety of different domainspecific problems have been developed in the past to solve this general problem. However, it has always been a great challenge to port or realize these functions on a massively-parallel architecture like a Graphics processing Unit (GPU). We present a GPU-based method called FANG that implements a generic and reusable N(x) for arbitrary domains in the field of combinatorial optimization. It can be customized to satisfy domainspecific requirements and leverages the underlying hardware in a fast and efficient way by construction. Moreover, our method has a high scalability with respect to the number of input states and the complexity of a single state. Measurements show significant performance improvements compared to traditional exploration approaches leveraging the CPU on our evaluation scenarios.

关键词： Heuristic search Combinatorial optimization Successor-state generation Neighborhood exploration Massively-parallel processing Graphics processing units GPUs

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A parallel Approach to Advantage Actor Critic in Deep Reinforcement Learning 19th

A Parallel Approach to Advantage Actor Critic in Deep Reinfo...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zhu, Xing Du, Yunfei Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Deep Reinforcement learning (DRL) algorithms recently still take a long time to train models in many applications. parallelization has the potential to improve the efficiency of DRL algorithms. In this paper, we propose an parallel approach (ParaA2C) for the popular Actor-Critic (AC) algorithms in DRL, to accelerate the training process. Our work considers the parallelization of the basic advantage actor critic (Serial-A2C) in AC algorithms. Specifically, we use multiple actor-learners to mitigate the strong correlation of data and the instability of updating, and finally reduce the training time. Note that we assign each actor-learner MPI process to a CPU core, in order to prevent resource contention between MPI processes, and make our ParaA2C approach more scalable. We demonstrate the effectiveness of ParaA2C by performing on Arcade Learning Environment (ALE) platform. Notably, our ParaA2C approach takes less than 10 min to train in some commonly used Atari games when using 512 CPU cores.

关键词： Deep reinforcement learning Advantage actor critic parallelization MPI Scalable

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Automatic Optimization of Python Skeletal parallel Programs 19th

Automatic Optimization of Python Skeletal Parallel Programs

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Loulergue, Frederic Philippe, Jolan No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA IMT Atlantique LS2N INRIA UBL Nantes France

ISBN: (纸本)9783030389918;9783030389901

Skeletal parallelism is a model of parallelism where parallel constructs are provided to the programmer as usual patterns of parallel algorithms. High-level skeleton libraries often offer a global view of programs instead of the common Single Program Multiple Data view in parallel programming. A program is written as a sequential program but operates on parallel data structures. Most of the time, skeletons on a parallel data structure have counterparts on a sequential data structure. For example, the map function that applies a given function to all the elements of a sequential collection (e.g., a list) has a map skeleton counterpart that applies a sequential function to all the elements of a distributed collection. Two of the challenges a programmer faces when using a skeleton library that provides a wide variety of skeletons are: which are the skeletons to use, and how to compose them? these design decisions may have a large impact on the performance of the parallel programs. However, skeletons, especially when they do not mutate the data structure they operate on, but are rather implemented as pure functions, possess algebraic properties that allow to transform compositions of skeletons into more efficient compositions of skeletons. In this paper, we present such an automatic transformation framework for the Python skeleton library PySke and evaluate it on several example applications.

关键词： Algorithmic skeletons Program transformation Python

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