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we assign a minterm id to each of these classes (e.g., 1 for letters, 0 for non-letters), and then compute derivatives based on these ids instead of characters. this is a huge win for performance and results in an absolutely enormous compression of memory, especially with large character classes like \w for word-characters in unicode, which would otherwise require tens of thousands of transitions alone (there’s a LOT of dotted umlauted squiggly characters in unicode). we show this in numbers as well, on the word counting \b\w{12,}\b benchmark, RE# is over 7x faster than the second-best engine thanks to minterm compressionremark here i’d like to correct, the second place already uses minterm compression, the rest are far behind. the reason we’re 7x faster than the second place is in the \b lookarounds :^).，更多细节参见体育直播
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这意味着，面对海量翻译、文本分类等高频低耗任务，开发者可以切断冗余的算力消耗；而在构建复杂用户界面或逻辑推演时，又能调高算力预算。这种“丰俭由人”的资源分配权，是谷歌在商业化落地层面给出的一记重拳。
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