Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user experience.Edge computing is well known as a pros...Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user experience.Edge computing is well known as a prospective method for the development of the Internet of Things(IoT).However,with the development of smart terminals,much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream dataflow.In this paper,we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network architecture.We propose a Time-Slicing Self-Adaptive Scheduling(TSAS)algorithm based on the hierarchical queue,which can reduce the queuing delay of the dataflow,improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow scheduling.The experimental results show that the TSAS algorithm can reduce latency,minimize energy consumption,and increase system throughput.展开更多
Sub Farm Interface is the event builder of the ATLAS（A Toroidal LHC ApparatuS） Dataflow System. It receives event fragments from the Read Out System, builds full events and sends complete events to the Event Filter ...Sub Farm Interface is the event builder of the ATLAS（A Toroidal LHC ApparatuS） Dataflow System. It receives event fragments from the Read Out System, builds full events and sends complete events to the Event Filter for high level event selection. This paper describes the implementation of the Sub Farm Interface. Furthermore, this paper introduces some issues on SFI（Sub Farm Interface） optimization and the monitoring service inside SFI.展开更多
Driven by continuous scaling of nanoscale semiconductor technologies,the past years have witnessed the progressive advancement of machine learning techniques and applications.Recently,dedicated machine learning accele...Driven by continuous scaling of nanoscale semiconductor technologies,the past years have witnessed the progressive advancement of machine learning techniques and applications.Recently,dedicated machine learning accelerators,especially for neural networks,have attracted the research interests of computer architects and VLSI designers.State-of-the-art accelerators increase performance by deploying a huge amount of processing elements,however still face the issue of degraded resource utilization across hybrid and non-standard algorithmic kernels.In this work,we exploit the properties of important neural network kernels for both perception and control to propose a reconfigurable dataflow processor,which adjusts the patterns of data flowing,functionalities of processing elements and on-chip storages according to network kernels.In contrast to stateof-the-art fine-grained data flowing techniques,the proposed coarse-grained dataflow reconfiguration approach enables extensive sharing of computing and storage resources.Three hybrid networks for MobileNet,deep reinforcement learning and sequence classification are constructed and analyzed with customized instruction sets and toolchain.A test chip has been designed and fabricated under UMC 65 nm CMOS technology,with the measured power consumption of 7.51 mW under 100 MHz frequency on a die size of 1.8×1.8 mm^2.展开更多
For multi-way tables with ordered categories, the present paper gives a decomposition of the point-symmetry model into the ordinal quasi point-symmetry and equality of point-symmetric marginal moments. The ordinal qua...For multi-way tables with ordered categories, the present paper gives a decomposition of the point-symmetry model into the ordinal quasi point-symmetry and equality of point-symmetric marginal moments. The ordinal quasi point-symmetry model indicates asymmetry for cell probabilities with respect to the center point in the table.展开更多
Multi-way join is critical for many big data applications such as data mining and knowledge discovery. Even though lots of research have been devoted to processing multi-way joins using MapReduce, there are still seve...Multi-way join is critical for many big data applications such as data mining and knowledge discovery. Even though lots of research have been devoted to processing multi-way joins using MapReduce, there are still several problems in general to be further improved, such as transferring numerous unpromising intermediate data and lacking of better coordination mechanisms. This work proposes an efficient multi-way joins processing model using MapReduce, named Sharing-Coordination-MapReduce (SC-MapReduce), which has the functions of sharing and coordination. Our SC-MapReduce model can filter the unpromising intermediatedata largely by using the sharing mechanism and optimize the multiple tasks coordination of multi-way joins. Extensive experiments show that the proposed model is efficient, robust and scalable.展开更多
基金This work were supported in part by the National Natural Science Foundation of China(No.61572191)Natural Science Foundation of Hunan Province(Nos.2022JJ30398,2022JJ40277 and 2022JJ40278)Scientific Research Fund of Hunan Provincial Education Department(No.17A130).
文摘Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user experience.Edge computing is well known as a prospective method for the development of the Internet of Things(IoT).However,with the development of smart terminals,much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream dataflow.In this paper,we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network architecture.We propose a Time-Slicing Self-Adaptive Scheduling(TSAS)algorithm based on the hierarchical queue,which can reduce the queuing delay of the dataflow,improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow scheduling.The experimental results show that the TSAS algorithm can reduce latency,minimize energy consumption,and increase system throughput.
文摘Sub Farm Interface is the event builder of the ATLAS（A Toroidal LHC ApparatuS） Dataflow System. It receives event fragments from the Read Out System, builds full events and sends complete events to the Event Filter for high level event selection. This paper describes the implementation of the Sub Farm Interface. Furthermore, this paper introduces some issues on SFI（Sub Farm Interface） optimization and the monitoring service inside SFI.
基金supported by NSFC with Grant No. 61702493, 51707191Science and Technology Planning Project of Guangdong Province with Grant No. 2018B030338001+2 种基金Shenzhen S&T Funding with Grant No. KQJSCX20170731163915914Basic Research Program No. JCYJ20170818164527303, JCYJ20180507182619669SIAT Innovation Program for Excellent Young Researchers with Grant No. 2017001
文摘Driven by continuous scaling of nanoscale semiconductor technologies,the past years have witnessed the progressive advancement of machine learning techniques and applications.Recently,dedicated machine learning accelerators,especially for neural networks,have attracted the research interests of computer architects and VLSI designers.State-of-the-art accelerators increase performance by deploying a huge amount of processing elements,however still face the issue of degraded resource utilization across hybrid and non-standard algorithmic kernels.In this work,we exploit the properties of important neural network kernels for both perception and control to propose a reconfigurable dataflow processor,which adjusts the patterns of data flowing,functionalities of processing elements and on-chip storages according to network kernels.In contrast to stateof-the-art fine-grained data flowing techniques,the proposed coarse-grained dataflow reconfiguration approach enables extensive sharing of computing and storage resources.Three hybrid networks for MobileNet,deep reinforcement learning and sequence classification are constructed and analyzed with customized instruction sets and toolchain.A test chip has been designed and fabricated under UMC 65 nm CMOS technology,with the measured power consumption of 7.51 mW under 100 MHz frequency on a die size of 1.8×1.8 mm^2.
文摘For multi-way tables with ordered categories, the present paper gives a decomposition of the point-symmetry model into the ordinal quasi point-symmetry and equality of point-symmetric marginal moments. The ordinal quasi point-symmetry model indicates asymmetry for cell probabilities with respect to the center point in the table.
基金This work was supported by the National Natural Science Foundation of China under Grant No.60873068,61472169 the Program for Excellent Talents in Liaoning Province under Grant No.LR201017.
文摘Multi-way join is critical for many big data applications such as data mining and knowledge discovery. Even though lots of research have been devoted to processing multi-way joins using MapReduce, there are still several problems in general to be further improved, such as transferring numerous unpromising intermediate data and lacking of better coordination mechanisms. This work proposes an efficient multi-way joins processing model using MapReduce, named Sharing-Coordination-MapReduce (SC-MapReduce), which has the functions of sharing and coordination. Our SC-MapReduce model can filter the unpromising intermediatedata largely by using the sharing mechanism and optimize the multiple tasks coordination of multi-way joins. Extensive experiments show that the proposed model is efficient, robust and scalable.
