TCLAP使多線程程序變慢

Question

TCLAP是C ++模板化的僅標頭庫，用於解析命令行參數。

我正在使用TCLAP處理多線程程序中的命令行參數：在主函數中讀取參數，然后啟動多個線程以處理參數定義的任務（NLP任務的某些參數）。

我已經開始顯示線程每秒處理的單詞數量，而且我發現，如果我將參數硬編碼到main中，而不是使用TCLAP從cli中讀取它們，那么吞吐量將提高6倍！

我正在將gcc與-O2參數一起使用，與在編譯過程中未進行優化時（未使用TCLAP的情況）相比，我看到的速度提高了大約10倍。因此，似乎使用TCLAP會以某種方式抵消部分優勢編譯器優化。

這是我使用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); 

}    

使用多線程的唯一地方是在Par2VecModel :: learn函數中：

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();                                                             
}

本節完全不涉及TCLAP，所以怎么回事？ （我也在使用c ++ 11功能，因此-std = c ++ 11標志，如果有區別的話）

Answer 1

因此，它已經開放了很長時間，並且該建議可能不再有用，但是我首先要檢查如果用“簡單”解析器替換TCLAP會發生什么情況（即，僅在命令行中輸入參數特定的固定順序並將其轉換為正確的類型）。 問題極不可能是由TCLAP引起的（即，我無法想象這種行為的任何機制）。 但是，可以想象的是，對於硬編碼的值，編譯器能夠執行一些編譯時優化，而這些值必須是變量時是不可能的。 但是，性能差異的程度似乎有些病態，因此我仍然懷疑沒有其他事情在發生。