In-Situ Bitmaps Generation and Efficient Data Analysis based on Bitmaps

Neither the memory capacity, memory access speeds, nor disk bandwidths are increasing at the same rate as the computing power in current and upcoming parallel machines. This has led to considerable recent research on in-situ data analytics. However, many open questions remain on how to perform such analytics, especially in memory constrained systems. Building on our earlier work that demonstrated bitmap indices (bitmaps) can be a suitable summary structure for key (offline) analytics tasks, this paper develops an in-situ analysis approach that performs data reduction (such as time-steps selection) using just bitmaps, and subsequently, stores only the selected bitmaps for post-analysis. We construct compressed bitmaps on the fly, show that many kinds of in-situ analyses can be supported by bitmaps without requiring the original data (and thus reducing memory requirements for in-situ analysis), and instead of writing the original simulation output, we only write the selected bitmaps to the disks (reducing the I/O requirements). We also demonstrate that we are able to use bitmaps for key offline analysis steps. We extensively evaluate our method with different simulations and applications, and demonstrate the effectiveness of our approach.