2019 International Conference on High Performance Computing & Simulation (HPCS)最新文献_第4页

A robust approach to the cell switch-off problem in 5G ultradense networks 一种解决5G超密集网络中蜂窝关闭问题的稳健方法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188085

F. Luna, Pablo H. Zapata-Cano, J. Valenzuela-Valdés, P. Padilla

{"title":"A robust approach to the cell switch-off problem in 5G ultradense networks","authors":"F. Luna, Pablo H. Zapata-Cano, J. Valenzuela-Valdés, P. Padilla","doi":"10.1109/HPCS48598.2019.9188085","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188085","url":null,"abstract":"Ultra-dense networks (UDNs) are recognized as one of the key enabling technologies of the fifth generation (5G) networks, as they allow for an efficient spatial reuse of the spectrum, which is required to meet the traffic demands foreseen for the next coming years. However, the power consumption of UDNs, with potentially hundreds of small base stations (SBSs) within each macrocell, is a major concern for the cellular operators, and has to be properly addressed prior to the actual deployment of these 5G networks. Among the different existing approaches to address this issue, a widely accepted strategy lies in the selective deactivation of SBSs, but without compromising the QoS provided to the User Equipments (UEs). This is known as the Cell Switch-Off (CSO) problem. The typical formulation of this problem is based on estimations of the traffic demand of the User Equipments (UEs) within the network. But these estimations could not be met. This work approaches these uncertain scenarios by extending the CSO problem with additional objectives that account for the robustness of the solutions to disturbances in these traffic estimates. To do so, a computationally demanding Monte-Carlo sampling is used to evaluate each solution. To manage such an increasingly large computing cost, a parallel version of the NSGA-II algorithm that is able to run on a computing platform composed of more than 500 cores has been used. It is able to compute in roughly 2 hours, an accumulated execution time of more than 42 days, which is within the expected timeframe of operators to make changes in the network configuration.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"100 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":"124734626","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

Applying ADM and OpenFlow to Build High Availability Networks 应用ADM和OpenFlow构建高可用性网络

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188189

James T. Yu

引用次数: 0

Exploration of Clustering Algorithms effects on Mesh of Clusters based FPGA Architecture Performance 基于FPGA结构性能的聚类算法对聚类网格的影响研究

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188138

Khouloud Bouaziz, S. Chtourou, Z. Marrakchi, M. Abid, A. Obeid

引用次数: 1

Towards a Resource-aware Thing Composition Approach 面向资源感知的事物组合方法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188186

Z. Maamar, S. Cheikhrouhou, M. Asim, A. Qamar, T. Baker, E. Ugljanin

引用次数: 4

A FPGA-Pipelined, High-Throughput Approach to Coarse-Grained Simulation of HPC Systems 高性能计算系统粗粒度仿真的fpga流水线高吞吐量方法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188129

C. Pascoe, R. Blanchard, H. Lam, G. Stitt

{"title":"A FPGA-Pipelined, High-Throughput Approach to Coarse-Grained Simulation of HPC Systems","authors":"C. Pascoe, R. Blanchard, H. Lam, G. Stitt","doi":"10.1109/HPCS48598.2019.9188129","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188129","url":null,"abstract":"Although previous studies have accelerated discreteevent simulation with various parallelization strategies, total simulation time remains prohibitive for certain use cases that require many independent simulations (e.g., design-space exploration, Monte Carlo simulation). In this paper, rather than focus solely on improved execution time for an individual simulation, we introduce an FPGA-accelerated approach that potentially sacrifices simulation latency to greatly increase throughput by many orders of magnitude. In this approach, the simulation design space is converted to an intermediate dataflow graph representation and ultimately mapped to a simulation pipeline by a custom-built compiler. We describe the design and implementation of our approach. Additionally, we present a resourcesharing strategy that greatly increases design scalability at the cost of slightly reduced simulation throughput. Although not applicable in all scenarios, we demonstrate that this approach can accelerate total simulation time for design-space exploration of HPC algorithmic/architectural co-design by up to 6 orders of magnitude when compared to the same exploration performed with a parallel software simulator.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"69 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":"125448918","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

Learning Low-Wastage Memory Allocations for Scientific Workflows at IceCube 在冰立方学习低损耗内存分配的科学工作流程

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188126

Carl Witt, J. Santen, U. Leser

{"title":"Learning Low-Wastage Memory Allocations for Scientific Workflows at IceCube","authors":"Carl Witt, J. Santen, U. Leser","doi":"10.1109/HPCS48598.2019.9188126","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188126","url":null,"abstract":"In scientific computing, scheduling tasks with heterogeneous resource requirements still requires users to estimate the resource usage of tasks. These estimates tend to be inaccurate in spite of laborious manual processes used to derive them. We show that machine learning outperforms user estimates, and models trained at runtime improve the resource allocation for workflows. We focus on allocating main memory in batch systems, which enforce resource limits by terminating jobs.The key idea is to train prediction models that minimize the costs resulting from prediction errors rather than minimizing prediction errors. In addition, we detect and exploit opportunities to predict resource usage of individual tasks based on their input size.We evaluated our approach on a 10 month production log from the IceCube South Pole Neutrino Observatory experiment. We compare our method to the performance of the current production system and a state-of-the-art method. We show that memory allocation quality can be increased from about 50% to 70%, while at the same time allowing users to provide only rough estimates of resource usage.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"158 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":"121685768","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: 7

Investigating Performance and Potential of the Parallel STL Using NAS Parallel Benchmark Kernels 使用NAS并行基准核研究并行STL的性能和潜力

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188147

Nicco Mietzsch, Karl Fuerlinger

引用次数: 2

Methodology for Decoupled Simulation of SystemVerilog HDL Designs SystemVerilog HDL设计的解耦仿真方法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188056

Juan-José Crespo, G. Mathey, J. L. Sánchez, F. J. Alfaro, J. Escudero-Sahuquillo, P. García, F. Quiles

引用次数: 0

Towards Automatically Optimizing PySke Programs 朝着自动优化PySke程序

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188160

Jolan Philippe, F. Loulergue

引用次数: 0

Evaluating GPU Performance for Deep Learning Workloads in Virtualized Environment 虚拟环境下深度学习GPU性能评估

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188098

R. Radhakrishnan, Y. Varma, Uday Kurkure

引用次数: 0