• Journal of KIISE:Databases
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• v.36 no.5
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• pp.399-404
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• 2009

In a multiple-server DBMS using the share-disk model, when a server process updates data, the updated ones are not immediately reflected to the buffers of the other server processes. Thus, the other server processes may read invalid data. In this paper, we propose a novel method to solve this problem. In this method the server process stores the identifiers and timestamps of the pages that have been updated during a transaction into the coherency volume when the transaction commits. Then, the server process invalidates its buffers of the pages updated by the other server processes by accessing the coherency volume when the lock is acquired, and, subsequently, read the up-to-date versions of the pages from disk. This method needs only a very small coherency volume and shows a good performance because the amount of data that need to be accessed is very small.

• Journal of KIISE
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• v.43 no.3
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• pp.304-313
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• 2016

We present a novel memory-efficient parallel ray casting algorithm for unstructured grid volume rendering on multi-core CPUs. Our method is based on the Bunyk ray casting algorithm. To solve the high memory overhead problem of the Bunyk algorithm, we allocate a fixed size local buffer for each thread and the local buffers contain information of recently visited faces. The stored information is used by other rays or replaced by other face's information. To improve the utilization of local buffers, we propose an image-plane based ray grouping algorithm that makes ray groups have high coherency. The ray groups are then distributed to computing threads and each thread processes the given groups independently. We also propose a novel hash function that uses the index of faces as keys for calculating the buffer index each face will use to store the information. To see the benefits of our method, we applied it to three unstructured grid datasets with different sizes and measured the performance. We found that our method requires just 6% of the memory space compared with the Bunyk algorithm for storing face information. Also it shows compatible performance with the Bunyk algorithm even though it uses less memory. In addition, our method achieves up to 22% higher performance for a large-scale unstructured grid dataset with less memory than Bunyk algorithm. These results show the robustness and efficiency of our method and it demonstrates that our method is suitable to volume rendering for a large-scale unstructured grid dataset.