Spatial Data Dynamic Balancing Distribution Method Based on the Minimum Spatial Proximity for Parallel Spatial Database

Zhou, Yan; Zhu, Qing; Zhang, Yeting

doi:10.4304/jsw.6.7.1337-1344

Cited by 4 publications

(5 citation statements)

References 13 publications

(11 reference statements)

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“…The drawback of static methods is that it cannot explore the benefit of varying data locality in a running environment. The other category of previous techniques is dynamic data distribution [9] [10] [11]. For those dynamic methods, program will be divided into different regions.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Data Distribution for Loops on Embedded Multicore with Scratch-Pad Memory

Gao¹,

Zhuge²,

Zhang³

et al. 2014

JCP

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Software-controlled Scratch-Pad Memory (SPM) is a desirable candidate for on-chip memory units in embedded multi-core systems due to its advantages of small die area and low power consumption. In particular, data placement on SPMs can be explicitly controlled by software. Therefore, the technique of data distribution on SPMs for multi-core system becomes critical in exploiting the advantages of SPM. Previous research efforts on data allocation did not consider the placement of array data accessed in loops. Loops are the most time-consuming and energy-consuming part for most of the computationintensive applications. In this paper, we propose a highperformance, low-overhead data distribution technique, the Iterational Optimal Loop Data Distribution Algorithm based on dynamic programming. It optimizes data allocation of both scalar and array data for embedded multi-core systems with SPMs. The experimental results show that the IOLDD algorithm reduces the energy consumption by 30.12% and 14.52% on average compared with random data distribution and greedy stretagy, respectively. It also reduces the memory access time by 18.45% and 18.38% on average compared with the random distribution strategy and the greedy strategy, respectively.

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Section: Related Workmentioning

confidence: 99%

Optimizing Data Distribution for Loops on Embedded Multicore with Scratch-Pad Memory

Gao¹,

Zhuge²,

Zhang³

et al. 2014

JCP

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show abstract

“…It requires requests' information including data address, data size and read/write flag. According the reallocate strategy described in section 2.A, the SMs in level 2 will fill the mapping table or calculate the corresponding arranged address according to a linear hashing function such as (1).…”

Section: B Operation Commands Of Smmentioning

confidence: 99%

“…Data placement is a fascinated field in data storage [1][2]. Common consideration in data placement is effectivity, even distribution, and less energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Size-based Data Placement Strategy in SAN

Lan¹,

Zhang²,

Ren³

2013

JSW

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This paper introduced an even data distribution strategy in SAN. Even data distribution is always the target of distributed storage design. Many articles dedicate the purpose. We use size-based data placement here to guarantee the data distributed evenly at furthest. Size-based data distribution separates the SAN into zones, and each zone has some storage devices (SD), different sized data will fill into different zones. Two kind of allocating algorithms are used to keep the data distribute evenly: orderly and best fit first. Our design will achieve 1) the data will even distributed on each storage devices at furthest; 2) read and write requests will even access the SDs, and 3) bandwidth can be improved at our system. Experimental results showed our design can optimize data distribution

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“…Spatial data is the one that represents the position, shape, size and distribution characteristics of space entity and the information about other aspects. In addition, spatial data is unstructured variable length data, and has non-uniform distribution and spatial locality relationship characteristics [10] . The spatial data of passenger dedicated line is divided into basic spatial data, prospective design spatial data and thematic spatial data.…”

Section: A Basic Data Platform Constructingmentioning

confidence: 99%

Research on Passenger Dedicated Line Track Maintenance Information System

Guo¹,

Han²,

Li³

et al. 2013

JSW

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Track Maintenance is crucial for the safe and efficient of railway transportation. After analysis of foreign management techniques for high-speed railway track maintenance, combined with the characteristic of passenger dedicated line in China, this paper has explored new ideas of the information system of passenger dedicated line track maintenance and at the same time researched on the theory and method of system construction in depth. Combined with parts of after-operation data of Beijing-Tianjin intercity passenger dedicated line and based on the key technologies and system design in the paper, a prototype information system of track maintenance for Beijing-Tianjin intercity passenger dedicated line has been developed.

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Spatial Data Dynamic Balancing Distribution Method Based on the Minimum Spatial Proximity for Parallel Spatial Database

Cited by 4 publications

References 13 publications

Optimizing Data Distribution for Loops on Embedded Multicore with Scratch-Pad Memory

Optimizing Data Distribution for Loops on Embedded Multicore with Scratch-Pad Memory

Size-based Data Placement Strategy in SAN

Research on Passenger Dedicated Line Track Maintenance Information System

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