如何知道使用哪個malloc？

Question

我理解它的方式，存在許多不同的malloc實現：

• dlmalloc - 通用分配器
• ptmalloc2 - glibc
• jemalloc - FreeBSD和Firefox
• tcmalloc - 谷歌
• libumem - Solaris

有沒有辦法確定我的（linux）系統上實際使用了哪個malloc？

我讀到“由於ptmalloc2的線程支持，它成為了linux的默認內存分配器。” 我有什么方法可以自己檢查一下嗎？

我問，因為我似乎沒有通過在下面的代碼中對malloc循環進行並列化來加快速度：

for (int i = 1; i <= 16; i += 1 ) {
    parallelMalloc(i);
}

 void parallelMalloc(int parallelism, int mallocCnt = 10000000) {

    omp_set_num_threads(parallelism);

    std::vector<char*> ptrStore(mallocCnt);

    boost::posix_time::ptime t1 = boost::posix_time::microsec_clock::local_time();

    #pragma omp parallel for
    for (int i = 0; i < mallocCnt; i++) {
        ptrStore[i] = ((char*)malloc(100 * sizeof(char)));
    }

    boost::posix_time::ptime t2 = boost::posix_time::microsec_clock::local_time();

    #pragma omp parallel for
    for (int i = 0; i < mallocCnt; i++) {
        free(ptrStore[i]);
    }

    boost::posix_time::ptime t3 = boost::posix_time::microsec_clock::local_time();


    boost::posix_time::time_duration malloc_time = t2 - t1;
    boost::posix_time::time_duration free_time   = t3 - t2;

    std::cout << " parallelism = "  << parallelism << "\t itr = " << mallocCnt <<  "\t malloc_time = " <<
            malloc_time.total_milliseconds() << "\t free_time = " << free_time.total_milliseconds() << std::endl;
}

這給了我一個輸出

 parallelism = 1         itr = 10000000  malloc_time = 1225      free_time = 1517
 parallelism = 2         itr = 10000000  malloc_time = 1614      free_time = 1112
 parallelism = 3         itr = 10000000  malloc_time = 1619      free_time = 687
 parallelism = 4         itr = 10000000  malloc_time = 2325      free_time = 620
 parallelism = 5         itr = 10000000  malloc_time = 2233      free_time = 550
 parallelism = 6         itr = 10000000  malloc_time = 2207      free_time = 489
 parallelism = 7         itr = 10000000  malloc_time = 2778      free_time = 398
 parallelism = 8         itr = 10000000  malloc_time = 1813      free_time = 389
 parallelism = 9         itr = 10000000  malloc_time = 1997      free_time = 350
 parallelism = 10        itr = 10000000  malloc_time = 1922      free_time = 291
 parallelism = 11        itr = 10000000  malloc_time = 2480      free_time = 257
 parallelism = 12        itr = 10000000  malloc_time = 1614      free_time = 256
 parallelism = 13        itr = 10000000  malloc_time = 1387      free_time = 289
 parallelism = 14        itr = 10000000  malloc_time = 1481      free_time = 248
 parallelism = 15        itr = 10000000  malloc_time = 1252      free_time = 297
 parallelism = 16        itr = 10000000  malloc_time = 1063      free_time = 281

Answer 1

我讀到“由於ptmalloc2的線程支持，它成為了linux的默認內存分配器。” 我有什么方法可以自己檢查一下嗎？

glibc內部使用ptmalloc2 ，這不是最近的開發。 無論哪種方式，做getconf GNU_LIBC_VERSION並不是非常困難，然后交叉檢查版本以查看是否在該版本中使用了ptmalloc2 ，但是我願意打賭你會浪費你的時間。

我問，因為我似乎沒有通過在下面的代碼中對malloc循環進行並列化來加快速度

將您的示例轉換為MVCE （此處為了簡潔省略代碼），並使用g++ 5.3.1編譯g++ -Wall -pedantic -O3 -pthread -fopenmp ，這是我的結果。

使用OpenMP：

 parallelism = 1     itr = 10000000  malloc_time = 746   free_time = 263
 parallelism = 2     itr = 10000000  malloc_time = 541   free_time = 267
 parallelism = 3     itr = 10000000  malloc_time = 405   free_time = 259
 parallelism = 4     itr = 10000000  malloc_time = 324   free_time = 221
 parallelism = 5     itr = 10000000  malloc_time = 330   free_time = 242
 parallelism = 6     itr = 10000000  malloc_time = 287   free_time = 244
 parallelism = 7     itr = 10000000  malloc_time = 257   free_time = 226
 parallelism = 8     itr = 10000000  malloc_time = 270   free_time = 225
 parallelism = 9     itr = 10000000  malloc_time = 253   free_time = 225
 parallelism = 10    itr = 10000000  malloc_time = 236   free_time = 226
 parallelism = 11    itr = 10000000  malloc_time = 225   free_time = 239
 parallelism = 12    itr = 10000000  malloc_time = 276   free_time = 258
 parallelism = 13    itr = 10000000  malloc_time = 241   free_time = 228
 parallelism = 14    itr = 10000000  malloc_time = 254   free_time = 225
 parallelism = 15    itr = 10000000  malloc_time = 278   free_time = 272
 parallelism = 16    itr = 10000000  malloc_time = 235   free_time = 220

23.87 user 
2.11 system 
0:10.41 elapsed 
249% CPU

沒有OpenMP：

 parallelism = 1     itr = 10000000  malloc_time = 748   free_time = 263
 parallelism = 2     itr = 10000000  malloc_time = 344   free_time = 256
 parallelism = 3     itr = 10000000  malloc_time = 751   free_time = 254
 parallelism = 4     itr = 10000000  malloc_time = 339   free_time = 262
 parallelism = 5     itr = 10000000  malloc_time = 748   free_time = 253
 parallelism = 6     itr = 10000000  malloc_time = 330   free_time = 256
 parallelism = 7     itr = 10000000  malloc_time = 734   free_time = 260
 parallelism = 8     itr = 10000000  malloc_time = 334   free_time = 259
 parallelism = 9     itr = 10000000  malloc_time = 750   free_time = 256
 parallelism = 10    itr = 10000000  malloc_time = 339   free_time = 255
 parallelism = 11    itr = 10000000  malloc_time = 743   free_time = 267
 parallelism = 12    itr = 10000000  malloc_time = 342   free_time = 261
 parallelism = 13    itr = 10000000  malloc_time = 739   free_time = 252
 parallelism = 14    itr = 10000000  malloc_time = 333   free_time = 252
 parallelism = 15    itr = 10000000  malloc_time = 740   free_time = 252
 parallelism = 16    itr = 10000000  malloc_time = 330   free_time = 252

13.38 user 
4.66 system 
0:18.08 elapsed 
99% CPU 

並行性似乎更快約8秒。 還是不相信？ 好。 我繼續抓住dlmalloc ，運行make來生成libmalloc.a 。 我的新命令就像g++ -Wall -pedantic -O3 -pthread -fopenmp -L$HOME/Development/test/dlmalloc/lib test.cpp -lmalloc

使用OpenMP：

parallelism = 1  itr = 10000000  malloc_time = 814   free_time = 277

我在37秒后按CTRL - C.

沒有OpenMP：

 parallelism = 1     itr = 10000000  malloc_time = 772   free_time = 271
 parallelism = 2     itr = 10000000  malloc_time = 780   free_time = 272
 parallelism = 3     itr = 10000000  malloc_time = 783   free_time = 272
 parallelism = 4     itr = 10000000  malloc_time = 792   free_time = 277
 parallelism = 5     itr = 10000000  malloc_time = 813   free_time = 281
 parallelism = 6     itr = 10000000  malloc_time = 800   free_time = 275
 parallelism = 7     itr = 10000000  malloc_time = 795   free_time = 277
 parallelism = 8     itr = 10000000  malloc_time = 790   free_time = 273
 parallelism = 9     itr = 10000000  malloc_time = 788   free_time = 277
 parallelism = 10    itr = 10000000  malloc_time = 784   free_time = 276
 parallelism = 11    itr = 10000000  malloc_time = 786   free_time = 284
 parallelism = 12    itr = 10000000  malloc_time = 807   free_time = 279
 parallelism = 13    itr = 10000000  malloc_time = 791   free_time = 277
 parallelism = 14    itr = 10000000  malloc_time = 790   free_time = 273
 parallelism = 15    itr = 10000000  malloc_time = 785   free_time = 276
 parallelism = 16    itr = 10000000  malloc_time = 787   free_time = 275

6.48 user 
11.27 system 
0:17.81 elapsed 
99% CPU

相當顯着的差異。 我懷疑問題出在你更復雜的代碼中，或者你的基准測試出了什么問題。