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A Cross-Platform OpenVX Library for FPGA Accelerators

A Cross-Platform OpenVX Library for FPGA Accelerators

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Maria Angélica Dávila-Guzmán Rubén Gran Tejero María Villarroya-Gaudó Darío Suárez Gracia Lester Kalms Diana Göhringer DIIS-I3A Universidad de Zaragoza – HiPEAC Network of Excellence Technische Universität Dresden – HiPEAC Network of Excellence

In Computer Vision, open programming standards such as OpenVX have emerged to bring together portability and acceleration across devices. Unfortunately, achieving both goals on FPGAs remains a challenge because FPGAs still require to adapt the code with proprietary extensions. Exclusively for Xilinx devices, the HiFlipVX open source library partially solves this problem by offering a clean C++ OpenVX API that offers the performance of proprietary extensions without exposing its complexity to programmer. While HiFlipVX enables portability within Xilinx devices, portability between FPGA manufacturers remains an open challenge. This work extends the HiFlipVX's capabilities with a twofold goal: i) to support Intel FPGA devices with different memory configurations, and ii) to enable execution on FPGAs as discrete accelerators. To accomplish these goals, the proposed implementation combines two HLS programming models: C++, using Intel's system of tasks that enables to coalesce nodes and reduce control overhead, and OpenCL, which provides efficient compute kernel nodes. On Intel FPGAs, compared with pure OpenCL implementations, the proposed implementation reduces kernel dispatch resources, saving up to 24% of ALUT resources for each kernel in a graph, and improves performance. Gains are 2.6× on average for representative applications, such as Canny edge detector, or Census transform, compared with state-of-the-art frameworks.

关键词： Performance evaluation C++ languages Transforms Programming Libraries Acceleration Kernel

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Multi-objective Optimization of the Job Shop Scheduling Problem on Unrelated parallel Machines with Sequence-Dependent Setup Times 23rd

Multi-objective Optimization of the Job Shop Scheduling Pr...

引用

23rd international conference on Computational Science and Its applications, ICCSA 2023

作者： dos Santos, Francisco Costa, Lino Varela, Leonilde ALGORITMI Research Centre/LASI University of Minho Braga Portugal Polytechnic Institute University Kimpa Vita Uíge Angola Department of Production and Systems University of Minho Braga Portugal

ISBN: (纸本)9783031371073

Several optimization criteria are involved in the job shop scheduling problems encountered in the engineering area. Multi-objective optimization algorithms are often applied to solve these problems, which become even more complex with the advent of Industry 4.0, mostly due to the increase of data from industrial systems. In this work, several instances of the multi-objective job shop scheduling problem on unrelated parallel machines with sequence-dependent setup times are solved using evolutionary approaches. In this problem, the goal is to assign a set of N jobs on M unrelated machines considering sequence-dependent setup times. Several objectives such as makespan, average completion time, cost and energy consumption can be optimized. In this work, single and multi-objective optimization problems are solved considering the minimization of makespan and the average completion time. Preliminary results for the comparison of algorithms on different instances of the problems are presented and statistically analysed. Future work will include problems with more objectives, and to extend this approach to the distributed job shop problem. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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parallel Computation for Sparse Network Component Analysis 4th

引用

4th international conference on Advanced Machine Learning Technologies and applications, AMLTA 2019

作者： Elsayad, Dina Hamad, Safwat Shedeed, Howida A. Tolba, M.F. Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783030141172

The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network goal is determining the topological order of genes interactions. Moreover, the regulatory network is a vital for understanding genes influence on each other. However, the main challenge confronting gene regulatory network algorithms is the massive data size. Where, the algorithm runtime is relative to the data size. This paper presents a parallel computation for Sparse Network Component Analysis (PSparseNCA) with application on gene regulatory network. PSparseNCA is a parallel version of SparseNCA. PSparseNCA enhanced the computation of SparseNCA using a distributed computing model. Where, the workload is distributed among P processing nodes, PSparseNCA is more efficient than SparseNCA. It achieved a better performance and its speedup reached 12.33. In addition, PsparseNCA complexity is O(NM2/P) instead of O(NM2) for SparseNCA. © 2020, Springer Nature Switzerland AG.

关键词： Gene expression

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DisCO: Efficient distributed window aggregation 23

DisCO: Efficient distributed window aggregation

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23rd international conference on Extending Database Technology, EDBT 2020

作者： Benson, Lawrence Grulich, Philipp M. Zeuch, Steffen Markl, Volker Rabl, Tilmann Hasso Plattner Institute University of Potsdam Germany Technische Universität Berlin Germany DFKI GmbH Germany

ISBN: (纸本)9783893180837

Many business applications benefit from fast analysis of online data streams. Modern stream processing engines (SPEs) provide complex window types and user-defined aggregation functions to analyze streams. While SPEs run in central data centers, wireless sensors networks (WSNs) perform distributed aggregations close to the data sources, which is beneficial especially in modern IoT setups. However, WSNs support only basic aggregations and windows. To bridge the gap between complex central aggregations and simple distributed analysis, we propose Disco, a distributed complex window aggregation approach. Disco processes complex window types on multiple independent nodes while efficiently aggregating incoming data streams. Our evaluation shows that Disco’s throughput scales linearly with the number of nodes and that Disco already outperforms a centralized solution in a two-node setup. Furthermore, Disco reduces the network cost significantly compared to the centralized approach. Disco’s treelike topology handles thousands of nodes per level and scales to support future data-intensive streaming applications. © 2020 Copyright held by the owner/author(s). Published in Proceedings of the 23rd international conference on Extending Database Technology (EDBT), March 30-April 2, 2020, ISBN 978-3-89318-083-7 on ***. Distribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.

关键词： Complex networks

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PANDA: Population Automatic Neural distributed Algorithm for Deep Leaning

PANDA: Population Automatic Neural Distributed Algorithm for...

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

作者： Jia Wei Xingjun Zhang Zeyu Ji Jingbo Li Zheng Wei High Performance Computing Lab Xi’an Jiaotong University Xi’an China

Deep neural network models perform very brightly in the field of artificial intelligence, but their success is affected by hyperparameters, and the learning rate schedule is one of the most important hyperparameters, and the search for the learning rate schedule is often time-consuming and computationally resource-intensive. In this paper, we propose a Population Automatic Neural distributed Algorithm (PANDA) based on population joint optimization, which uses distributed data parallel deep neural network training to implement a dynamic learning rate schedule optimization strategy based on the population idea, with almost no loss of test accuracy, PANDA is able to dynamically refine the learning rate schedule during model training instead of following the usual suboptimal strategy. We conducted experiments on typical AlexNet, VGG16, and ResNet18 using the Tianhe-3 supercomputing prototype, and the results show that using PANDA to dynamically update the learning rate greatly reduces the learning rate schedule search time while ensuring close performance with the latest population hyperparameter algorithm, and in our experiments, PANDA lead to at max 123.85x speedup, and the experimental results prove the effectiveness and robustness of PANDA.

关键词： Training Deep learning Schedules Heuristic algorithms Sociology Learning (artificial intelligence) Statistics

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Optimum parallelism in Spark Framework on Hadoop YARN for Maximum Cluster Resource Utilization 1st

Optimum Parallelism in Spark Framework on Hadoop YARN for Ma...

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1st international conference on Sustainable Technologies for Computational Intelligence (ICTSCI)

作者： Janardhanan, P. S. Samuel, Philip SunTec Business Solut Pvt Ltd TechnoPk Trivandrum Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi Kerala India

ISBN: (纸本)9789811500299;9789811500282

Spark iswidely used as a distributed computing framework for in-memory parallel processing. It implements distributed computing by splitting the jobs into tasks and deploying them on executors on the nodes of a cluster. Executors are JVMs with dedicated allocation of CPU cores and memory. The number of tasks depends on the partitions of input data. Depending on the number of CPU cores allocated to executors, one or more cores get allocated to one task. Tasks run as independent threads on executors hosted on JVMs dedicated exclusively to the executor. One or more executors are deployed on the nodes of the cluster depending on the resource availability. The performance advantage provided by distributed computing on Spark framework depends on the level of parallelism configured at 3 levels, namely node level, executor level, and task level. The parallelism at each of these levels should be configured to fully utilize the available computing resources. This paper recommends optimum parallelism configuration for Apache Spark framework deployed on Hadoop YARN cluster. The recommendations are based on the results of the experiments conducted to evaluate the dependency of parallelism at each of these levels on the performance of Spark applications. For the purpose of the evaluation, a CPU-intensive job and an I/O-intensive job are used. The performance is measured by varying the parallelism at each of the 3 levels. The results presented in this paper help Spark users in selecting optimum parallelism at each of these levels for achieving maximum performance for Spark jobs by maximum resource utilization.

关键词： distributed computing Apache Spark Hadoop YARN SparkBench Spark configuration Multi-level parallelism Resource optimization

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Cloud computing with Kubernetes cluster elastic scaling 19

Cloud computing with Kubernetes cluster elastic scaling

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3rd international conference on Future Networks and distributed Systems, ICFNDS 2019

作者： Thurgood, Brandon Lennon, Ruth G. Dept. of Computing Letterkenny Institute of Techology Port Rd. Letterkenny Co. Donegal Ireland

ISBN: (纸本)9781450371636

Cloud computing and artificial intelligence (AI) technologies are becoming increasingly prevalent in the industry, necessitating the requirement for advanced platforms to support their workloads through parallel and distributed architectures. Kubernetes provides an ideal platform for hosting various workloads, including dynamic workloads based on AI applications that support ubiquitous computing devices leveraging parallel and distributed architectures. The rationale is that Kubernetes can be used to support backend services running on parallel and distributed architectures, hosting ubiquitous cloud computing workloads. These applications support smart homes and concerts, providing an environment that automatically scales based on demand. While Kubernetes does offer support for auto scaling of Pods to support these workloads, automated scaling of the cluster itself is not currently offered. In this paper we introduce a Free and Open Source Software (FOSS) solution for autoscaling Kubernetes (K8s) worker nodes within a cluster to support dynamic workloads. We go on to discuss scalability issues and security concerns both on the platform and within the hosted AI applications. © 2019 Association for Computing Machinery.

关键词： Automation

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A model management platform for industry 4.0 – enabling management of machine learning models in manufacturing environments 23rd

A model management platform for industry 4.0 – enabling man...

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23rd international conference on Business Information Systems, BIS 2020

作者： Weber, Christian Hirmer, Pascal Reimann, Peter Graduate School of Advanced Manufacturing Engineering University of Stuttgart Stuttgart Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany

ISBN: (纸本)9783030533366

Industry 4.0 use cases such as predictive maintenance and product quality control make it necessary to create, use and maintain a multitude of different machine learning models. In this setting, model management systems help to organize models. However, concepts for model management systems currently focus on data scientists, but do not support non-expert users such as domain experts and business analysts. Thus, it is difficult for them to reuse existing models for their use cases. In this paper, we address these challenges and present an architecture, a metadata schema and a corresponding model management platform. © Springer Nature Switzerland AG 2020.

关键词： Machine learning

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DEO: A Smart Dynamic Edge Offloading Scheme using processing Resources of Nearby Wireless Devices to Form an Edge Computing Engine 3

DEO: A Smart Dynamic Edge Offloading Scheme using Processing...

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3rd IEEE international conference on Cloud and Fog Computing Technologies and applications (IEEE Cloud Summit)

作者： Lami, Ihsan Alshahib Al-ameri, Ali Sch Comp Appl Comp Buckingham England

ISBN: (纸本)9781728131016

Edge computing reduces connectivity costs and network traffic congestion over cloud computing, by offering local resources (processing and storage) at one hop closer to the end-users. I.e. it reduces the Round-Trip Time (RTT) for offloading part of the processing workload from end-nodes/devices to servers at the edge. However, edge servers are normally pre-setup as part of the overall computing resource infrastructure, which is tough to predict for mobile/IoT deployments. This paper introduces a smart Dynamic Edge Offloading scheme, (we named it DEO), that forms the "edge computing resource" on-the-go, as needed from nearby available devices in a cooperative sharing environment. This is especially necessary for hosting mobile/IoT applications traffic at crowded/urban situations, and, for example, when executing a processing intensive Mobile Cloud Computing Service (MCCS) on a Smartphone (SP). DEO implementation is achieved by using a short-range wireless connectivity between available cooperative end-devices, that will form the edge computing resource. DEO includes an intelligent cloud-based engine, that will facilitate the engagement of the edge network devices. For example, if the end-device is a SP running an MCCS, DEO will partition the processing of the MCCS into sub-tasks, that will be run in parallel on the newly formed "edge resource network" of other nearby devices. Our experiments prove that DEO reduces the RTT and cost overhead by 62.8% and 75.5%, when compared to offloading to a local edge server or a cloud-based server.

关键词： mobile cloud computing services edge computing offloading parallel processing

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Scheduling Bag-of-Task-Chains in distributed Systems 14

Scheduling Bag-of-Task-Chains in Distributed Systems

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14th IEEE international Symposium on Autonomous Decentralized Systems (ISADS) / 3rd international Workshop on Service Assurance in System Wide Information Management (SASWIM)

作者： Stavrinides, Georgios L. Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

ISBN: (纸本)9781728116723

distributed resources required for processing complex parallel applications are becoming larger in scale day-by-day. They need to be used efficiently in order to provide Quality of Service (QoS). Therefore, resource allocation and scheduling are of paramount importance. In this paper we investigate issues involved with the scheduling of parallel jobs that are bag-of-task-chains in distributed systems. Two task-chain scheduling techniques in two different cases of resource allocation are studied. The performance of the task-chain scheduling algorithms is evaluated via simulation, under different cases of system workload. The experimental results show that the performance of the task-chain scheduling strategies depends on the employed resource allocation techniques.

关键词： bag-of-task-chains scheduling simulation distributed systems performance

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