runtime error in 'bench_cg.py'

Hello,
I'm getting a runtime error running the bench_cg.py example:
RuntimeError: In function std::vector cuda::compileToPTX(const char*, std::string)

Also, the af.info() seems to see the GPU device, but doesn't assign it an ID, not could detect its memory and compute capability. At the same time, the bench_fft.py seems to work.

```
(venv3) igu@demeter:~$ nvidia-smi
Fri Apr 19 17:45:53 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.39 Driver Version: 418.39 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 980 Off | 00000000:02:00.0 Off | N/A |
| 30% 42C P0 45W / 195W | 0MiB / 4040MiB | 0% Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
(venv3) igu@demeter:~$ export | grep LD_LILI
declare -x LD_LIBRARY_PATH=":/usr/local/cuda/lib64:/opt/arrayfire/lib64:/usr/local/cuda/nvvm/lib64"
(venv3) igu@demeter:~$ python /mnt/Simulations/IvanG/WORKSPACE/arrayfire_examples/examples/benchmarks/bench_fft.py
ArrayFire v3.6.2 (CUDA, 64-bit Linux, build dc38ef1)
Platform: CUDA Toolkit , Driver: 418.39
[] GeForce GTX 980, MB, CUDA Compute .
Benchmark N x N 2D fft on arrayfire
Time taken for 128 x 128: 0.9280 Gflops
Time taken for 256 x 256: 174.0147 Gflops
Time taken for 512 x 512: 300.4586 Gflops
Time taken for 1024 x 1024: 298.9022 Gflops
Time taken for 2048 x 2048: 287.5326 Gflops
Time taken for 4096 x 4096: 309.7844 Gflops
Benchmark N x N 2D fft on numpy
Time taken for 128 x 128: 4.4689 Gflops
Time taken for 256 x 256: 4.8368 Gflops
Time taken for 512 x 512: 4.4340 Gflops
Time taken for 1024 x 1024: 3.8707 Gflops
(venv3) igu@demeter:~$ python /mnt/Simulations/IvanG/WORKSPACE/arrayfire_examples/examples/benchmarks/bench_cg.py
ArrayFire v3.6.2 (CUDA, 64-bit Linux, build dc38ef1)
Platform: CUDA Toolkit , Driver: 418.39
[] GeForce GTX 980, MB, CUDA Compute .

Testing benchmark functions...
Traceback (most recent call last):
File "/mnt/Simulations/IvanG/WORKSPACE/arrayfire_examples/examples/benchmarks/bench_cg.py", line 199, in
test()
File "/mnt/Simulations/IvanG/WORKSPACE/arrayfire_examples/examples/benchmarks/bench_cg.py", line 141, in test
A, b, x0 = setup_input(n=50, sparsity=7) # dense A
File "/mnt/Simulations/IvanG/WORKSPACE/arrayfire_examples/examples/benchmarks/bench_cg.py", line 51, in setup_input
A = A.T + A + n*af.identity(n, n, dtype=af.Dtype.f32)
File "/opt/venv3/lib/python3.6/site-packages/arrayfire/array.py", line 664, in T
return transpose(self, False)
File "/opt/venv3/lib/python3.6/site-packages/arrayfire/array.py", line 318, in transpose
safe_call(backend.get().af_transpose(c_pointer(out.arr), a.arr, conj))
File "/opt/venv3/lib/python3.6/site-packages/arrayfire/util.py", line 79, in safe_call
raise RuntimeError(to_str(err_str))
RuntimeError: In function std::vector cuda::compileToPTX(const char*, std::string)
In file src/backend/cuda/jit.cpp:
(venv3) igu@demeter:~$
```

Also, setting the backend doesn't seem to have an effect:

```>>> import arrayfire as af
>>> af.get_backend()
'unified'
>>> af.set_backend('cuda')
>>> af.get_backend()
'unified'
```

Thank you,
Ivan

Start by running examples/benchmarks/bench_cg.py and compare its setup_input path at line 51 with the working bench_fft.py example. Check the reported af.info() and af.get_backend()/af.set_backend() behavior, including the CUDA runtime details in the report. Done means identifying the cause of the PTX compilation error and the missing device metadata or backend change.

Written by the indexing model from the issue text.