Modern simulation systems generate big amount of data, which consequently has to be analyzed in a timely fashion. Traditional database management systems follow principle of pulling the needed data, processing it, and then returning the results. This approach is then optimized by means of caching, storing in different structures, or doing some sacrifices on precision of the results to make it faster. When it comes to the point of doing various queries that require analysis of the whole data, this design has the following disadvantages: considerable overhead on traditional disk random I/O framework while reading from the simulation output files and low throughput of the data that consequently results in long latency, and, if there was any indexing to optimize selections, overhead of storing those becomes too big, too. Beside it, indexing will also cause delay during write operations and since most of the queries work with the entire data sets, indexing loses its point. There is a new approach to this problem – Push-based System for Molecular Simulation Data Analysis for processing network of queries proposed in the previous paper and its primary steps are: i) it uses traditional scan-based I/O framework to load the data from files to the main memory and then ii) the data is pushed through a network of queries which consequently filters the data and collect all the needed information which increases efficiency and data throughput. It has a considerable advantage in analysis of molecular simulation data, because it normally involves all the data sets to be processed by the queries. In this paper, we propose improved version of Push-based System for Molecular Simulation Data Analysis. Its major difference with the previous design is usage of GPU for the actual processing part of the data flow. Using the same scan-based I/O framework the data is pushed through the network of queries which are processed by GPU, and due to the nature of science simulation data, this gives a big advantage for processing it faster and easier (it will be explained more in later sections). In the old approach there were some custom data structures such as quad-tree for calculation of histograms to make the processing faster and those involved loss of data and some expectations from the data nature, too. In the new approach due to high performance of GPU processing and its nature, custom data structures were not even needed much, though it didn’t bear any loss in precision and performance.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-7645 |
| Date | 19 October 2016 |
| Creators | Akhmedov, Iliiazbek |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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