{'fraction_non_alphanumeric': 0.021953896816684963, 'fraction_numerical': 0.0, 'mean_word_length': 5.8059701492537314, '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}}
Understanding Data Access Patterns Using Object-Differentiated Memory Profiling

The information provided by commonly used code-oriented profilers can be complemented by means of data-oriented profiling techniques. Based on a data-oriented approach, in this study we leverage techniques developed in previous papers to analyze the data access characteristics of a range of U.S. Department of Energy applications representative of different application domains. By analyzing object-differentiated memory access profiles, we identify markedly different access patterns across application stages. We find read-only and read-write periods, relatively large periods without accessing particular objects, and a variety of data access rates. This information is useful for devising software optimizations, for software and hardware code sign, and for data distribution and partitioning in heterogeneous memory systems.