Optimizing Float Parsing for Large Datasets
Parsing space-separated floats from large files can be a time-consuming task. This is especially true when handling millions of lines with multiple floats per line. To address this challenge, it's essential to adopt efficient parsing techniques that minimize performance bottlenecks.
Measuring Parsing Speed
To evaluate the effectiveness of different parsing methods, a benchmark was conducted using a 515Mb input file containing millions of space-separated floats. The results revealed significant variations in parsing times between different approaches.
Boost Spirit: A Top Performer
Surprisingly, Boost Spirit emerged as the fastest parsing solution. This powerful library offers several advantages over traditional methods:
Other Parsing Techniques
While Boost Spirit took the lead in parsing speed, other techniques also demonstrated promising results.
Benchmark Results
The following chart summarizes the parsing times for different methods using memory-mapped files:
[Image of parsing time benchmark results]
Choosing the Right Approach
The best parsing method depends on the specific requirements of the application. If speed and accuracy are paramount, Boost Spirit is an excellent choice. For more straightforward scenarios, Eigen or C 14 regular expressions may suffice.
.hpp File (Old Implementation)
std::vector<data> read_float3_data(std::string const &in) { namespace spirit = boost::spirit; namespace qi = boost::spirit::qi; typedef std::vector<data> list; qi::rule<it, list(), qi::locals<bool>, data> triplet_rule = qi::phrase( (qi::double_ > qi::double_ > qi::double_) % qi::eol, qi::space, data()); it first = in.begin(); it last = in.end(); it err = in.end(); bool parsing_ok = qi::phrase_parse(first, last, triplet_rule, qi::space, data(), qi::_pass, err); assert(parsing_ok && first == last); (void)err; return data(); }
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