{'fraction_non_alphanumeric': 0.010764262648008612, 'fraction_numerical': 0.0, 'mean_word_length': 5.2, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Iteration Aware Prefetching for Large Multidimensional Scientific Datasets

Most caching and prefetching research does not take advantage of prior knowledge of access patterns, or does not adequately address the storage issues associated with multidimensional scientific data. Armed with an access pattern specified at run time as an iteration over a multidimensional array stored as a disk file, we use prefetching to greatly reduce the number of disk accesses and mitigate the cost of read latency. We call this iteration aware prefetching. We assume the pattern of access is not known until runtime, in contrast to chunking methods that preprocess a file for a particular access pattern. Our approach results in dramatic performance improvements over file system caching. We also significantly outperform chunking without having to reorganize the data, and can do even better by applying our approach on top of a chunked file.