Hi,
I recently began using Amazon EC2 as a testbed for multi-GPU computations.
Since I currently do not neet the ECC feature, I stop it to gain more performance. However, I was very surprized (and annoyed) by the fact that I am only able to do this for the first GPU. Both GPUs are identical, M2050, as reported by deviceQuery:
./deviceQuery Starting…
CUDA Device Query (Runtime API) version (CUDART static linking)
There are 2 devices supporting CUDA
Device 0: “Tesla M2050”
CUDA Driver Version: 3.20
CUDA Runtime Version: 3.20
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 2817982464 bytes
Multiprocessors x Cores/MP = Cores: 14 (MP) x 32 (Cores/MP) = 448 (Cores)
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per block: 1024
Maximum sizes of each dimension of a block: 1024 x 1024 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 1
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Clock rate: 1.15 GHz
Concurrent copy and execution: Yes
Run time limit on kernels: No
Integrated: No
Support host page-locked memory mapping: Yes
Compute mode: Default (multiple host threads can use this device simultaneously)
Concurrent kernel execution: Yes
Device has ECC support enabled: Yes
Device is using TCC driver mode: No
Device 1: “Tesla M2050”
CUDA Driver Version: 3.20
CUDA Runtime Version: 3.20
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 2817982464 bytes
Multiprocessors x Cores/MP = Cores: 14 (MP) x 32 (Cores/MP) = 448 (Cores)
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per block: 1024
Maximum sizes of each dimension of a block: 1024 x 1024 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 1
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Clock rate: 1.15 GHz
Concurrent copy and execution: Yes
Run time limit on kernels: No
Integrated: No
Support host page-locked memory mapping: Yes
Compute mode: Default (multiple host threads can use this device simultaneously)
Concurrent kernel execution: Yes
Device has ECC support enabled: Yes
Device is using TCC driver mode: No
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 3.20, CUDA Runtime Version = 3.20, NumDevs = 2, Device = Tesla M2050, Device = Tesla M2050
PASSED
Press to Quit…
For nvidia-smi, I get the following:
nvidia-smi -r
ECC configuration for GPU 0:
Current: 1
After reboot: 1
ECC is not supported by GPU 1
and, needless to say, when I try to
nvidia-smi -g 0 --ecc-config=1
everything works, but when I try to
nvidia-smi -g 1 --ecc-config=0
i get
ECC is not supported by GPU 1 or the ECC configuration cannot be changed
Has anybody seen this problem before? Is there a solution?
Cheers,
Serban