Issue with DeviceResource (`cudaMallocAsync`) on vGPU without memory pool support

When attempting to use the AWS EC2 g6f instance type for CI in fvdb-core (working prototype on this branch), we receive an error at the first call to cudaMallocAsync at nanovdb/cuda/DeviceResource.h:22: CUDA error 801: operation not supported

Through some investigation, it appears that the stream-ordered memory pools capability is not exposed by the vGPU (g6f is a fractional L4 GPU instance). Confirmed when running:

> #include <cuda_runtime.h>
> #include <stdio.h>
>
> int main() {
>   int supported = 0, driver = 0;
>   cudaDriverGetVersion(&driver);
>   cudaError_t e = cudaDeviceGetAttribute(
>       &supported, cudaDevAttrMemoryPoolsSupported, 0);
>   printf("driver=%d, query=%s, memoryPoolsSupported=%d\n",
>          driver, cudaGetErrorString(e), supported);
>   return e != cudaSuccess || !supported;
> }

driver=13000, query=no error, memoryPoolsSupported=0

These instances are running the latest AWS-supplied GRID driver (version 19.5/580.159.03) and can confirm the GPU device is available:

>nvidia-smi
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.159.03             Driver Version: 580.159.03     CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA L4-3Q                   On  |   00000000:31:00.0 Off |                    0 |
| N/A   N/A    P0            N/A  /  N/A  |       0MiB /   3072MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

From reading, it appears that memory pools can be enabled/disabled via hypervisor settings on the vGPU's setup. This seems likely to be disabled due to security concerns; I have opened up an AWS support ticket to confirm if this behaviour is intentional.

Regardless, support for this operating scenario (driver/hardware/CUDA support for cudaMallocAsync but unavailable capability) would require utilizing cudaMalloc (or a custom allocator) instead. This is a related issue in the work for #2232 and allowing NanoVDB to use an alternative allocator to enable successful execution on a vGPU configured in this way could be acceptance criteria for that work.

Start at nanovdb/cuda/DeviceResource.h:22 and inspect how the first cudaMallocAsync call is used. Compare the proposed allocator work in #2232 with the g6f/vGPU capability result; done means the CI prototype can allocate successfully when memoryPoolsSupported=0 without relying on unsupported cudaMallocAsync.