Querying current device is slow compared to CuPy

[shwina]

Getting the current device using cuda.core is quite a bit slower than CuPy:

In [1]: import cupy as cp

In [2]: %timeit cp.cuda.Device()
69 ns ± 0.496 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

In [3]: from cuda.core.experimental import Device

In [4]: %timeit Device()
795 ns ± 0.273 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

Ultimately, my goal is to get the compute capability of the current device, and this is even slower:

In [5]: %timeit cp.cuda.Device().compute_capability
89.1 ns ± 0.413 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

In [6]: %timeit Device().compute_capability
2.64 μs ± 122 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Are there tricks (e.g., caching) CuPy is employing here that cuda.core can use as well? Alternately, is there another way for me to use cuda.core or cuda.bindings to get this information quickly? Note that for my use case, I'm not super concerned about the first call to Device(), but I do want subsequent calls to be trivially inexpensive if the current device hasn't changed.

---

Using the low-level cuda.bindings is also not quite as fast:

In [11]: def get_cc():
 ...: dev = runtime.cudaGetDevice()[1]
 ...: return driver.cuDeviceComputeCapability(dev)
 ...:

In [12]: get_cc()
Out[12]: (<CUresult.CUDA_SUCCESS: 0>, 7, 5)

In [13]: %timeit get_cc()
597 ns ± 0.494 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

[leofang]

We'll have to cache CC on a per-Device object level to bring this down to O(10) ns level.

In [32]: def get_cc(dev):
 ...: if dev in data:
 ...: return data[dev]
 ...: data[dev] = (driver.cuDeviceGetAttribute(driver.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, dev)[1],
 ...: driver.cuDeviceGetAttribute(driver.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, dev)[1])
 ...: return data[dev]
 ...: 

In [33]: 

In [33]: get_cc(1)
Out[33]: (12, 0)

In [36]: %timeit get_cc(1)
51.7 ns ± 0.0214 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

In [37]: %timeit cp.cuda.Device().compute_capability
179 ns ± 1.07 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

which is also what CuPy does internally:
https://github.com/cupy/cupy/blob/1f9c9d4d1eb2edcbeb2a9294def57c2252e18b92/cupy/cuda/device.pyx#L213-L214

[leofang]

I did some refactoring of Device.__new__() to replace cudart APIs by driver APIs, and found the perf gets even worse. Out of curiosity, I did this quick profiling and got very surprised: (the following already has the primary context set to current)

In [19]: %timeit runtime.cudaGetDevice()
338 ns ± 0.463 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

In [20]: %timeit driver.cuCtxGetDevice()
406 ns ± 1.79 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

In [21]: %timeit cp.cuda.runtime.getDevice()
112 ns ± 0.822 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

A simple get-device call using cuda.bindings is 3-4x slower than CuPy. @vzhurba01 have we seen something like this before?

[leofang]

Accessing Device().compute_capability is being addressed in #459. Let me re-label this issue to track the remaining binding performance issue.