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As CPU processing power becomes more powerful and storage capacity increases, performing data-intensive visualization computations involving large data on a desktop computer becomes an increasingly viable option. Desktops, though, usually lack the memory capacity required to load such large data at once, and thus the cost of I/O becomes a major bottleneck for the necessary out-of-core computation. Among techniques that reduce runtime I/O cost, reordering the file layout to increase data locality has become popular in recent years. However, file layout techniques for time-varying scientific data, especially for time-varying flow fields, have been rarely discussed. In this paper, we evaluate the performance impact of utilizing a file layout method for out-of-core time-varying flow visualization. We extend a graph-based representation of flow fields, originally developed for static vector fields, to time-varying flow fields, and apply a graph layout algorithm to order data blocks to be written to disk. Benefits from the generated file layouts are evaluated using various parameters and seeding scenarios.