如何将任务分配给特定的CPU核心？

Question

I read material of distributed devices in tensorflow that training can be assigned to a specific CPU core.

Can we assign a task/thread to a CPU core to achieve concurrent or parallel processing?

with tf.device("/job:ps/task:0"):
  weights_1 = tf.Variable(...)
  biases_1 = tf.Variable(...)

with tf.device("/job:ps/task:1"):
  weights_2 = tf.Variable(...)
  biases_2 = tf.Variable(...)

Answer 1

You can get the current pid of the python process and use a third party utility like taskset to assign it to a CPU core.

Dont know much about tensorflow but I think the GIL will come into Play here.You will have to use multiprocessing and assign each process to a dufferent core.

Answer 2

You can bind a particular thread of process to an arbitrary core (assuming you are using linux). This works not only for python but for any process. I made a python script to show how you can do that.

You can get thread ids via ps command: [user@dev ~]$ ps -Lo pid,%cpu,lwp -p {pid} Output for me:

  PID %CPU   LWP
28216 98.0 28216
28216  0.0 28217
28216  0.0 28218

Here 28216 is PID of the process, while you can see there are other threads running in a simple python script.

Now you can assign a thread to a particular core via taskset

taskset -cp 0-5 28218

It will show the following output:

pid 28218's current affinity list: 0-11
pid 28218's new affinity list: 0-5

You then can observe that some threads are bound to different set of CPUs:

[user@host ~]$ taskset -cp 28218
pid 28218's current affinity list: 0-5
[user@host ~]$ taskset -cp 28217
pid 28217's current affinity list: 0-11