This question is almost impossible to answer because OpenGL by itself is just a front end API, and as long as an implementations adheres to the specification and the outcome conforms to this it can be done any way you like. The question may have been: How does an OpenGL driver work on the lowest … Read more
First of all, you should be aware of the fact that CUDA will not automagically make computations faster. On the one hand, because GPU programming is an art, and it can be very, very challenging to get it right. On the other hand, because GPUs are well-suited only for certain kinds of computations. This may … Read more
There is an undocumented method called device_lib.list_local_devices() that enables you to list the devices available in the local process. (N.B. As an undocumented method, this is subject to backwards incompatible changes.) The function returns a list of DeviceAttributes protocol buffer objects. You can extract a list of string device names for the GPU devices as … Read more
No, I don’t think “open CUDA library” is enough to tell, because different nodes of the graph may be on different devices. When using tensorflow2: print(“Num GPUs Available: “, len(tf.config.list_physical_devices(‘GPU’))) For tensorflow1, to find out which device is used, you can enable log device placement like this: sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) Check your console for this … Read more
There are two parts to that answer (I wrote it). One part is easy to quantify, the other is more empirical. Hardware Constraints: This is the easy to quantify part. Appendix F of the current CUDA programming guide lists a number of hard limits which limit how many threads per block a kernel launch can … Read more