int main(int argc, char const *argv[]) { pthread_t threads[NUM_THREADS]; int rc; long t; for (t = 0; t < NUM_THREADS; t++) { rc = pthread_create(&threads[t], NULL, printHello, (void *)t); if (rc) { printf("ERORR; return code from pthread_create() is %d\n", rc)...
int s, tnum, opt, num_threads; struct thread_info *tinfo; pthread_attr_t attr; int stack_size; void *res; /* The "-s" option specifies a stack size for our threads */ stack_size = -1; while ((opt = getopt(argc, argv, "s:")) != -1) { switch (opt) { case 's': s...
#define NUMTHREADS 3 void sighand(int signo); void *threadfunc(void *parm) { pthread_t tid = pthread_self(); int rc; printf("Thread %u entered/n", tid); rc = sleep(30); /* 若有信号中断则返回剩余秒数 */ printf("Thread %u did not get expected results! rc=%d/n",...
int main() { pthread_t threads[NUM_THREADS]; for (int i = 0; i < NUM_THREADS; ++i) { pthread_create(&threads[i], NULL, thread_function, NULL); } for (int i = 0; i < NUM_THREADS; ++i) { pthread_join(threads[i], NULL); } return 0;}```在上述代码中,创建了四个线程,...
for (int i = 0; i < numThreads; ++i) { // 创建线程,并传递参数 pthread_create(&threads[i], NULL, threadFunc, NULL); } // 等待所有线程执行完毕 for (int i = 0; i < numThreads; ++i) { pthread_join(threads[i], NULL); } free(threads); return 0; } ```2. 多进程执行命令...
exportOMP_NUM_THREADS=1 ./stream_c.exe # 结果分析,看 Copy、Scale、Add、Triad,数值越大,性能越好 磁盘IO ⚠️ 测试时请准备裸的数据盘,测试完成后请重新格式化磁盘 测试方法和结果分析和文件 IO 测试相同,--filename 改为具体的数据盘即可,比如/dev/sda ,这里不再赘述 ...
num_threads:该任务所在的线程组里的线程个数,在图中值为1。 it_real_value:由于计时间隔导致的下一个SIGALRM发送进程的时延(单位为jiffies), 在图中值为0。 start_time:在系统启动后,到与该任务启动时的间隔(单位为jiffies), 在图中值为247。
const int numThreads = 4; double step, pi; pthread_mutex_t mut; double sum = 0.0; void * thread(void *pArg) { double x; int i ; int temp = *((int *)pArg); int start = temp*(num_steps/4); int end = start + num_steps/4; ...
OMP_NUM_THREADS是控制OpenMP并行线程数的标准环境变量。Multiwfn的内置控制方法虽然便于理解，但是写成脚本...
in fact,my pc:cpu_num =128 I only use torchrun and ddp ,6 gpu on one machine, however,i get 194 bypass run ps -ef|grep python train.py |wc -l I think it should thread = num_gpu*workers=24, pipe = Pipeline( batch_size=batch_size, num_threads=4, device_id=local_rank % 6...