TCLAP使多线程程序变慢

Question

TCLAP是C ++模板化的仅标头库，用于解析命令行参数。

我正在使用TCLAP处理多线程程序中的命令行参数：在主函数中读取参数，然后启动多个线程以处理参数定义的任务（NLP任务的某些参数）。

我已经开始显示线程每秒处理的单词数量，而且我发现，如果我将参数硬编码到main中，而不是使用TCLAP从cli中读取它们，那么吞吐量将提高6倍！

我正在将gcc与-O2参数一起使用，与在编译过程中未进行优化时（未使用TCLAP的情况）相比，我看到的速度提高了大约10倍。因此，似乎使用TCLAP会以某种方式抵消部分优势编译器优化。

这是我使用TCLAP的唯一功能，主要功能如下所示：

int main(int argc, char** argv)                                                 
{                                                                               
uint32_t mincount;                                                          
uint32_t dim;                                                               
uint32_t contexthalfwidth;                                                  
uint32_t negsamples;                                                        
uint32_t numthreads;                                                        
uint32_t randomseed;                                                        
string corpus_fname;                                                        
string output_basefname;                                                    
string vocab_fname;                                                         

Eigen::initParallel();                                                      

try {                                                                       
TCLAP::CmdLine cmd("Driver for various word embedding models", ' ', "0.1"); 
TCLAP::ValueArg<uint32_t> dimArg("d","dimension","dimension of word representations",false,300,"uint32_t");
TCLAP::ValueArg<uint32_t> mincountArg("m", "mincount", "required minimum occurrence count to be added to vocabulary",false,5,"uint32_t");
TCLAP::ValueArg<uint32_t> contexthalfwidthArg("c", "contexthalfwidth", "half window size of a context frame",false,15,"uint32_t");
TCLAP::ValueArg<uint32_t> numthreadsArg("t", "numthreads", "number of threads",false,12,"uint32_t");
TCLAP::ValueArg<uint32_t> negsamplesArg("n", "negsamples", "number of negative samples for skipgram model",false,15,"uint32_t");
TCLAP::ValueArg<uint32_t> randomseedArg("s", "randomseed", "seed for random number generator",false,2014,"uint32_t");
TCLAP::UnlabeledValueArg<string> corpus_fnameArg("corpusfname", "file containing the training corpus, one paragraph or sentence per line", true, "corpus", "corpusfname");
TCLAP::UnlabeledValueArg<string> output_basefnameArg("outputbasefname", "base filename for the learnt word embeddings", true, "wordreps-", "outputbasefname");
TCLAP::ValueArg<string> vocab_fnameArg("v", "vocabfname", "filename for the vocabulary and word counts", false, "wordsandcounts.txt", "filename");
cmd.add(dimArg);                                                            
cmd.add(mincountArg);                                                       
cmd.add(contexthalfwidthArg);                                               
cmd.add(numthreadsArg);                                                     
cmd.add(randomseedArg);                                                     
cmd.add(corpus_fnameArg);                                                   
cmd.add(output_basefnameArg);                                               
cmd.add(vocab_fnameArg);                                                    
cmd.parse(argc, argv);                                                      

mincount = mincountArg.getValue();                                          
dim = dimArg.getValue();                                                    
contexthalfwidth = contexthalfwidthArg.getValue();                          
negsamples = negsamplesArg.getValue();                                      
numthreads = numthreadsArg.getValue();                                      
randomseed = randomseedArg.getValue();                                      
corpus_fname = corpus_fnameArg.getValue();                                  
output_basefname = output_basefnameArg.getValue();                          
vocab_fname = vocab_fnameArg.getValue();                                    
}                                                                           
catch (TCLAP::ArgException &e) {};         

/*                                                                          
uint32_t mincount = 5;                                                      
uint32_t dim = 50;                                                          
uint32_t contexthalfwidth = 15;                                             
uint32_t negsamples = 15;                                                   
uint32_t numthreads = 10;                                                   
uint32_t randomseed = 2014;                                                 
string corpus_fname = "imdbtrain.txt";                                      
string output_basefname = "wordreps-";                                      
string vocab_fname = "wordsandcounts.txt";                                  
*/                                                                          

string test_fname = "imdbtest.txt";                                         
string output_fname = "parreps.txt";                                        
string countmat_fname = "counts.hdf5";                                      
Vocabulary * vocab;                                                                                                              

vocab = determineVocabulary(corpus_fname, mincount);                        
vocab->dump(vocab_fname);                                                   

Par2VecModel p2vm = Par2VecModel(corpus_fname, vocab, dim, contexthalfwidth, negsamples, randomseed);
p2vm.learn(numthreads);                                                     
p2vm.save(output_basefname);                                                
p2vm.learnparreps(test_fname, output_fname, numthreads); 

}    

使用多线程的唯一地方是在Par2VecModel :: learn函数中：

void Par2VecModel::learn(uint32_t numthreads) {                                 
thread* workers;                                                            
workers = new thread[numthreads];                                           
uint64_t numwords = 0;                                                      
bool killflag = 0;                                                          
uint32_t randseed;                                                          

ifstream filein(corpus_fname.c_str(), ifstream::ate | ifstream::binary);    
uint64_t filesize = filein.tellg();                                         

fprintf(stderr, "Total number of in vocab words to train over: %u\n", vocab->gettotalinvocabwords());

for(uint32_t idx = 0; idx < numthreads; idx++) {                            
    randseed = eng();                                                       
    workers[idx] = thread(skipgram_training_thread, this, numthreads, idx, filesize, randseed, std::ref(numwords));
}                                                                           

thread monitor(monitor_training_thread, this, numthreads, std::ref(numwords), std::ref(killflag));

for(uint32_t idx = 0; idx < numthreads; idx++)                              
    workers[idx].join();                                                    

killflag = true;                                                            
monitor.join();                                                             
}

本节完全不涉及TCLAP，所以怎么回事？ （我也在使用c ++ 11功能，因此-std = c ++ 11标志，如果有区别的话）

Answer 1

因此，它已经开放了很长时间，并且该建议可能不再有用，但是我首先要检查如果用“简单”解析器替换TCLAP会发生什么情况（即，仅在命令行中输入参数特定的固定顺序并将其转换为正确的类型）。 问题极不可能是由TCLAP引起的（即，我无法想象这种行为的任何机制）。 但是，可以想象的是，对于硬编码的值，编译器能够执行一些编译时优化，而这些值必须是变量时是不可能的。 但是，性能差异的程度似乎有些病态，因此我仍然怀疑没有其他事情在发生。