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2019 International Conference on High Performance Computing & Simulation (HPCS)最新文献

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A Novel FPGA-Based High Throughput Accelerator For Binary Search Trees 一种基于fpga的二叉搜索树高吞吐量加速器
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188158
Oyku Melikoglu, O. Ergin, Behzad Salami, Julian Pavon, O. Unsal, A. Cristal
{"title":"A Novel FPGA-Based High Throughput Accelerator For Binary Search Trees","authors":"Oyku Melikoglu, O. Ergin, Behzad Salami, Julian Pavon, O. Unsal, A. Cristal","doi":"10.1109/HPCS48598.2019.9188158","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188158","url":null,"abstract":"This paper presents a deeply pipelined and massively parallel Binary Search Tree (BST) accelerator for Field Programmable Gate Arrays (FPGAs). Our design relies on the extremely parallel on-chip memory, or Block RAMs (BRAMs) architecture of FPGAs. To achieve significant throughput for the search operation on BST, we present several novel mechanisms including tree duplication as well as horizontal, duplicated, and hybrid (horizontal-vertical) tree partitioning. Also, we present efficient techniques to decrease the stalling rates that can occur during the parallel tree search. By combining these techniques and implementations on Xilinx Virtex-7 VC709 platform, we achieve up to 8X throughput improvement gain in comparison to the baseline implementation, i.e., a fully-pipelined FPGA-based accelerator.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134460976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
End-to-End Learning of Graph Similarity 图相似度的端到端学习
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188094
Zhixin Chen, Mengxiang Lin, Deqing Wang
{"title":"End-to-End Learning of Graph Similarity","authors":"Zhixin Chen, Mengxiang Lin, Deqing Wang","doi":"10.1109/HPCS48598.2019.9188094","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188094","url":null,"abstract":"Constructing and calculating the metrics of graphs comparison precisely can be expensive due to the prohibitively high time complexity, exponential in some cases. Thus building a learning model to approximate the metrics is expected. In this paper, we convert the computation of graphs similarity/distance into a learning problem and propose an end-to-end GCN(Graph Convolutional Network) based model to calculate the GFD(Graphlet Frequency Distribution) distance of graphs. In this way, the trained model predicts the GFD distance of graphs directly rather than constructs a GFD vector by counting graphlets as in traditional methods. A experimental evaluation is conducted to validate the effectiveness of our model in real-world networks scaled from tens of nodes to thousands of nodes. Our trained model takes $ 480times$ less time on average compared with the count-based method in the dataset. The 3-top nearest accuracy reaches 74.6% while the 5-top nearest accuracy reaches 85.2% in the test data.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134178507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Management of collaborations in digital marketplaces 数字市场协作管理
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188185
Lu Zhang
{"title":"Management of collaborations in digital marketplaces","authors":"Lu Zhang","doi":"10.1109/HPCS48598.2019.9188185","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188185","url":null,"abstract":"With everyone generating value out of data, our work focuses to distributed data trading platforms, Digital Market Places (DMPs), that can handle the intricacies of data sharing, e.g. how, where, and what can be done with the traded data. Here we represent collaborations among involving parities in DMPs in the form of archetypes and model them with numeric representations for easier manipulation with standard mathematical tools. We also develop a methodology which aims to select a best-fit infrastructure archetype with any customer-defined application request. In addition, we propose multiple metrics which allows evaluate and compare competing DMPs systemically from more dimensions: coverage, extensibility, precision and flexibility.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132474360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Open-Source Shared Memory implementation of the HPCG benchmark: analysis, improvements and evaluation on Cavium ThunderX2 开源共享内存实现的HPCG基准:在Cavium ThunderX2上的分析、改进和评估
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188103
Daniel Ruiz, F. Spiga, Marc Casas, M. Garcia-Gasulla, F. Mantovani
{"title":"Open-Source Shared Memory implementation of the HPCG benchmark: analysis, improvements and evaluation on Cavium ThunderX2","authors":"Daniel Ruiz, F. Spiga, Marc Casas, M. Garcia-Gasulla, F. Mantovani","doi":"10.1109/HPCS48598.2019.9188103","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188103","url":null,"abstract":"The High Performance Conjugate Gradient (HPCG) benchmark complements the LINPACK benchmark in the performance evaluation coverage of large High Performance Computing (HPC) systems. Due to its lower arithmetic intensity and higher memory pressure, HPCG is recognized as a more representative benchmark for data-center and irregular memory access pattern workloads, therefore its popularity has been steadily raising within the HPC community. As only a small fraction of the reference version of the HPCG benchmark is parallelized with shared memory techniques (OpenMP), in this paper we introduce and evaluate in-depth two OpenMP parallelization strategies for the Gauss-Seidel preconditioner. Due to the increasing attractiveness of Arm architecture and Arm ecosystem in HPC, we evaluate our modified HPCG version on a state-of-the-art HPC system based on Cavium ThunderX2 SoC. We consider our work as a broader contribution not exclusively to the Arm: along with this paper, the source code of the modified HPCG has been made publicly available on GitLab to enable further optimizations at benefit of all HPC community.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133151526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Shared Resource Allocation for Mobile Users in Multi-Tier Heterogeneous Wireless Network 多层异构无线网络中移动用户共享资源分配
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188067
Chetna Singhal, Anshul Varma
{"title":"Shared Resource Allocation for Mobile Users in Multi-Tier Heterogeneous Wireless Network","authors":"Chetna Singhal, Anshul Varma","doi":"10.1109/HPCS48598.2019.9188067","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188067","url":null,"abstract":"Heterogeneous cell architecture in 5G networks has emerged as a leading solution in the field of mobile and wireless communication to meet bandwidth and coverage area requirements. The off-loading of mobile stations (MS) from Macro cell to Small Cells (Micro, Pico, Femto) increases the overall system throughput. However, heterogeneity in cell architecture comes with drawbacks like increased interference, frequent handovers, and QoS degradation for mobile users. In this paper, we deal with the issue of handover and limited resources in heterogeneous cell architecture. MS opt for Complete, Split or No hand-off based on its velocity. In case of Small cells, we exploit the overlap of coverage area of macro cell and small cells and propose ‘Shared Resource Allocation’, which gets initiated during the handover phase when small cell is unable to support the existing throughput and QoS requirement of MS. The MS then undergoes ‘Split hand-off’ rather than a conventional hand-off and continues to share the resources of both macro cell and small cell throughout its stay in the smaller cell’s coverage area.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124417945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Introducing Streaming into Linear Algebra-based Sparse Graph Algorithms 在基于线性代数的稀疏图算法中引入流
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188143
P. Kogge, Neil A. Butcher, Brian A. Page
{"title":"Introducing Streaming into Linear Algebra-based Sparse Graph Algorithms","authors":"P. Kogge, Neil A. Butcher, Brian A. Page","doi":"10.1109/HPCS48598.2019.9188143","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188143","url":null,"abstract":"GraphBLAS is a new package designed to provide a standard set of building blocks for graph algorithms based formally in the language of linear algebra. This paper suggests some extensions of the underlying math that would enhance GraphBLAS’ ability to stream updates into a computation without a bulk recomputation, and at greatly reduced computational complexity. The process is applied to several examples.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115973773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
TECN: Task Selection and Placement in GPU Enabled Clouds Using Neural Networks 使用神经网络在GPU支持的云中选择和放置任务
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188101
Hari Sivaraman, Uday Kurkure, Lan Vu
{"title":"TECN: Task Selection and Placement in GPU Enabled Clouds Using Neural Networks","authors":"Hari Sivaraman, Uday Kurkure, Lan Vu","doi":"10.1109/HPCS48598.2019.9188101","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188101","url":null,"abstract":"Resource scheduling in cloud computing needs to be addressed effectively and efficiently to enable fair share, high throughput and low latency for large numbers of jobs that share the cloud. Currently, there are no known solutions for vGPU enabled VM placement. Virtualized GPUs present many opportunities and challenges; finding an optimal placement for VMs is an NP-hard problem. Our research focusses on using Machine Learning to address task/VM placement in a vGPU enabled cloud.We built a simulator to test and compare different strategies to select and place VMs. In this paper, we describe the simulator and discuss the results of the comparison of different heuristics. We present details of the dense neural networks (DNN) we built that out-perform all the heuristics. The DNNs learns the \"best\" heuristic at every system configuration and as such are \"superior\" to any individual heuristic. Our approach to using machine learning to solve the problem of selection and placement starts with heuristics, trains DNNs from these heuristics, and then out-performs them. We did a head-to-head comparison of the task selection by the DNNs with that generated by the heuristics. In this comparison, the DNNs show better task selection results for 76% of the test cases than the heuristics. These results obtained by using DNNs look promising and can be further improved by refining the neural networks.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116123920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Stream-Based Representation and Incremental optimization of Technical Market Indicators 基于流的市场技术指标表示与增量优化
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188212
K. Bakanov, I. Spence, H. Vandierendonck
{"title":"Stream-Based Representation and Incremental optimization of Technical Market Indicators","authors":"K. Bakanov, I. Spence, H. Vandierendonck","doi":"10.1109/HPCS48598.2019.9188212","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188212","url":null,"abstract":"Technical market indicators are used to measure the trends of financial markets. In practice they are conventionally expressed using a non-formal notation or a DSL specific to a certain development platform, which poorly correlates between individual trades and the high-level formulas operating on those trades and leaving very little room for optimization. In this paper we propose a formal, mathematically based notation for expressing technical market indicators, which represents trades as streams of data. We argue that this notation is more accurate and open to optimizations. We express three technical indicators from the ground up, demonstrate our optimization approach, and implement the indicators using Click router runtime. Finally, we benchmark various configurations and versions of the implemented indicators, running in kernel space as well as user space, and discuss the findings.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122394398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sensing at the Edge: The Challenges of Ubiquitous Sensing 边缘传感:无处不在传感的挑战
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188075
G. O’hare
{"title":"Sensing at the Edge: The Challenges of Ubiquitous Sensing","authors":"G. O’hare","doi":"10.1109/HPCS48598.2019.9188075","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188075","url":null,"abstract":"Sensing, and the ability to sense, has now permeated every aspect and fabric of our everyday lives. This presents both new horizons in terms of knowledge acquisition and emergent understanding while simultaneously presenting numerous challenges in terms of computational infrastructure, computing power intelligence and social and ethical dilemmas. This paper explores the challenges associated with the ubiquitous sensing phenomena.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122476308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms 天线设计中的机器学习:机器学习概念和算法概述
2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188224
H. M. E. Misilmani, Tarek Naous
{"title":"Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms","authors":"H. M. E. Misilmani, Tarek Naous","doi":"10.1109/HPCS48598.2019.9188224","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188224","url":null,"abstract":"With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently taking a major part of the ongoing research, and expected to be the key player in today’s technologies. This paper introduces and investigates the applications of machine learning in antenna design. It covers the major aspects of machine learning, including its basic concept, differentiation with artificial intelligence and deep learning, learning algorithms, its wide applications in various technologies, with a main focus on its usage in antenna design. The review also includes a comparison of the results using machine learning in antenna design, compared to the conventional design methods.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123569646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 48
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