1,154 research outputs found
Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services
One of the most widely-implemented service standards provided by the Open
Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS).
WMS is widely employed globally, but there is limited knowledge of the global
distribution, adoption status or the service quality of these online WMS
resources. To fill this void, we investigated global WMSs resources and
performed distributed performance monitoring of these services. This paper
explicates a distributed monitoring framework that was used to monitor 46,296
WMSs continuously for over one year and a crawling method to discover these
WMSs. We analyzed server locations, provider types, themes, the spatiotemporal
coverage of map layers and the service versions for 41,703 valid WMSs.
Furthermore, we appraised the stability and performance of basic operations for
1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major
reasons for request errors and performance issues, as well as the relationship
between service response times and the spatiotemporal distribution of client
monitoring sites. This paper will help service providers, end users and
developers of standards to grasp the status of global WMS resources, as well as
to understand the adoption status of OGC standards. The conclusions drawn in
this paper can benefit geospatial resource discovery, service performance
evaluation and guide service performance improvements.Comment: 24 pages; 15 figure
Evaluation Mappings of Spatial Accelerator Based On Data Placement
The scheduling strategies of workloads are critical to fully exploiting the
performance of spatial accelerators, accurate performance models are required
to evaluate the mapping of workloads.Recent works proposed various cost-model
to describe the dataflow of the spatial accelerator. However, they are less
expressive about customized memory hierarchies and thus lead to inaccurate
performance models. In this paper, we propose, PolyAcc, a framework for
evaluating the mappings of workload on spatial accelerator based on data
placement. The Data placement relation describes the temporal-spatial relation
of data at different memory levels, which can accurately capture the runtime
behavior of hardware units. Based on data placement relations, polyAcc
accurately analyzes the data volume for different reuse patterns and estimate
metrics, including data reuse, latency, and energy. Overall, polyAcc closely
matches the ideal execution time and PE utilization for GEMM and Conv
workloads, respectively achieves 0.82%, 18.8% improvements for execution time
and energy consumption estimates in validation against Eyeriss architecture
compared to the state-of-the-art framework.Comment: 7 pages,8 figures,3 table
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