On Fri, Feb 12, 2016 at 5:58 PM, Sarah Anderson <saraha.cray.com> wrote:
> Tried
> openmpi/1.8.4_gcc 2) cudatoolkit/7.5.18 3) gcc/4.9.1
Another thing you may want to try (if you haven't already) is MPICH or
MVAPICH. I've haven't had a lot of success with OpenMPI and Amber over
the years, but MPICH/MVAPICH usually works great for me.
-Dan
>
> The single-GPU configure -mpi -cuda -noX11 gnu
> worked fine, but two-GPUs produced the same error.
>> gpu_allreduce cudaDeviceSynchronize failed an illegal memory access was encountered
>> srun: error: prd2-0171: task 0: Exited with exit code 255
> I tried a MPI only gnu build, which worked fine with 16 or 32 ranks.
>
> I dropped back to cudatoolkit/7.0.28
> Same message. I'm out of ideas for now. When I have the output of
>> lspci -d "10b5:*" -vvv | grep ACSCtl
> I'll send it to you.
>
> Sarah
>
>
> On 2/12/16 4:36 PM, Ross Walker wrote:
>> Hi Sarah,
>>
>> I suspect this is indeed an MPI issue - likely related to it screwing up how P2P communication works - but it could also be a bios issue or a compiler issue - again related to peer to peer communication. I've seen race conditions in a few of the Intel compiler versions but never been able to track it down and concluded it was a compiler bug. Can you check a couple of things please:
>>
>> 1) Make sure all the latest updates are applied.
>>
>> 2) Build with the GNU compilers and see if the problem is still there.
>>
>> Also the output from the following command would be useful in determining if it is a bios bug on the hardware or not:
>>
>> lspci -d "10b5:*" -vvv | grep ACSCtl
>>
>> (But it unfortunately needs to be run as root).
>>
>> All the best
>> Ross
>>
>>> On Feb 12, 2016, at 14:24, Sarah Anderson<saraha.cray.com> wrote:
>>>
>>> Hi Ross,
>>>
>>> I was running this test:
>>>
>>> Amber14_Benchmark_Suite/PME/JAC_production_NVE
>>>
>>> srun -n 2 --mpi=pmi2 --cpu_bind=socket $AMBERHOME/bin/pmemd.cuda.MPI -O -i mdin.GPU -o mdout.2GPU -p prmtop -c inpcrd
>>>
>>> Yes, a single GPU run completes normally.
>>>
>>> I tried another test in that suite, and though the 2GPU test completed, it came up with different (nonsense) answers.
>>> Maybe there's something wrong with the MPI/build. It's just Intel impi & "configure -cuda -mpi -noX11 intel"
>>>
>>> Thanks,
>>> Sarah
>>>
>>> Two GPUs
>>>
>>> R M S F L U C T U A T I O N S
>>>
>>>
>>> NSTEP = 1000 TIME(PS) = 102.000 TEMP(K) = 6.13 PRESS = 0.0
>>> Etot = 10445.2658 EKtot = 388.9169 EPtot = 10213.0555
>>> BOND = 138.3458 ANGLE = 184.8487 DIHED = 119.1381
>>> 1-4 NB = 35.9507 1-4 EEL = 842.2853 VDWAALS = 9138.2936
>>> EELEC = 2970.4534 EGB = 3580.7687 RESTRAINT = 0.0000
>>> ------------------------------------------------------------------------------
>>>
>>>
>>>
>>> Single GPU:
>>>
>>> R M S F L U C T U A T I O N S
>>>
>>>
>>> NSTEP = 1000 TIME(PS) = 102.000 TEMP(K) = 0.91 PRESS = 0.0
>>> Etot = 22.9288 EKtot = 57.4768 EPtot = 49.0089
>>> BOND = 32.3643 ANGLE = 82.1033 DIHED = 80.2520
>>> 1-4 NB = 14.6668 1-4 EEL = 107.9454 VDWAALS = 148.9948
>>> EELEC = 280.7436 EGB = 212.3126 RESTRAINT = 0.0000
>>> ------------------------------------------------------------------------------
>>>
>>>
>>>
>>>
>>> On 2/12/16 1:16 PM, Ross Walker wrote:
>>>> Hi Sarah,
>>>>
>>>> The error message doesn't tell a lot here unfortunately. The issue with GPU error messages is that if you have an array in memory that contains a NAN - say the force array got an infinite force - then things will be fine 'until' the code tries to upload or download from the GPU (or do a similar copy operation) - NAN's are not supported in these operations and thus you get the error that you see. The real error - e.g. 2 atoms sitting on top of each other occurred somewhere else entirely within the code. The net result is that just because the error is in the gpu_allreduce does not mean it is related to something wrong with the GPUs, or a driver issue or even a multi-GPU issue.
>>>>
>>>> What it likely means there is something wrong with the simulation itself that you are running. Have you tried running on just 1 GPU and see if that crashes as well (and also CPU?). Can you provide some more details about what you are actually simulating.
>>>>
>>>> All the best
>>>> Ross
>>>>
>>>>> On Feb 12, 2016, at 10:42, Sarah Anderson<saraha.cray.com> wrote:
>>>>>
>>>>> Has anyone seen this message lately? I saw some notes about in mid 2015 but no particular fix suggested.
>>>>>
>>>>> This is with cuda 7.5 using a pair of K80 GPUS in peer-to-peer mode.
>>>>>
>>>>> It fails with all combinations of CUDA_VISIBLE_DEVICES 0,1 2,3
>>>>>
>>>>>> gpu_allreduce cudaDeviceSynchronize failed an illegal memory access was encountered
>>>>> |------------------- GPU DEVICE INFO --------------------
>>>>> |
>>>>> | Task ID: 0
>>>>> | CUDA_VISIBLE_DEVICES: 0,1
>>>>> | CUDA Capable Devices Detected: 2
>>>>> | CUDA Device ID in use: 0
>>>>> | CUDA Device Name: Tesla K80
>>>>> | CUDA Device Global Mem Size: 11519 MB
>>>>> | CUDA Device Num Multiprocessors: 13
>>>>> | CUDA Device Core Freq: 0.82 GHz
>>>>> |
>>>>> |
>>>>> | Task ID: 1
>>>>> | CUDA_VISIBLE_DEVICES: 0,1
>>>>> | CUDA Capable Devices Detected: 2
>>>>> | CUDA Device ID in use: 1
>>>>> | CUDA Device Name: Tesla K80
>>>>> | CUDA Device Global Mem Size: 11519 MB
>>>>> | CUDA Device Num Multiprocessors: 13
>>>>> | CUDA Device Core Freq: 0.82 GHz
>>>>> |
>>>>> |--------------------------------------------------------
>>>>>
>>>>> |---------------- GPU PEER TO PEER INFO -----------------
>>>>> |
>>>>> | Peer to Peer support: ENABLED
>>>>> |
>>>>> |--------------------------------------------------------
>>>>>
>>>>>
>>>>> Here is deviceQuery
>>>>>
>>>>> CUDA Device Query (Runtime API) version (CUDART static linking)
>>>>>
>>>>> Detected 2 CUDA Capable device(s)
>>>>>
>>>>> Device 0: "Tesla K80"
>>>>> CUDA Driver Version / Runtime Version 7.5 / 7.5
>>>>> CUDA Capability Major/Minor version number: 3.7
>>>>> Total amount of global memory: 11520 MBytes (12079136768 bytes)
>>>>> MapSMtoCores for SM 3.7 is undefined. Default to use 192 Cores/SM
>>>>> MapSMtoCores for SM 3.7 is undefined. Default to use 192 Cores/SM
>>>>> (13) Multiprocessors, (192) CUDA Cores/MP: 2496 CUDA Cores
>>>>> GPU Clock rate: 824 MHz (0.82 GHz)
>>>>> Memory Clock rate: 2505 Mhz
>>>>> Memory Bus Width: 384-bit
>>>>> L2 Cache Size: 1572864 bytes
>>>>> Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
>>>>> Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
>>>>> Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
>>>>> Total amount of constant memory: 65536 bytes
>>>>> Total amount of shared memory per block: 49152 bytes
>>>>> Total number of registers available per block: 65536
>>>>> Warp size: 32
>>>>> Maximum number of threads per multiprocessor: 2048
>>>>> Maximum number of threads per block: 1024
>>>>> Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
>>>>> Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
>>>>> Maximum memory pitch: 2147483647 bytes
>>>>> Texture alignment: 512 bytes
>>>>> Concurrent copy and kernel execution: Yes with 2 copy engine(s)
>>>>> Run time limit on kernels: No
>>>>> Integrated GPU sharing Host Memory: No
>>>>> Support host page-locked memory mapping: Yes
>>>>> Alignment requirement for Surfaces: Yes
>>>>> Device has ECC support: Enabled
>>>>> Device supports Unified Addressing (UVA): Yes
>>>>> Device PCI Bus ID / PCI location ID: 5 / 0
>>>>> Compute Mode:
>>>>> < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
>>>>>
>>>>> Device 1: "Tesla K80"
>>>>> CUDA Driver Version / Runtime Version 7.5 / 7.5
>>>>> CUDA Capability Major/Minor version number: 3.7
>>>>> Total amount of global memory: 11520 MBytes (12079136768 bytes)
>>>>> MapSMtoCores for SM 3.7 is undefined. Default to use 192 Cores/SM
>>>>> MapSMtoCores for SM 3.7 is undefined. Default to use 192 Cores/SM
>>>>> (13) Multiprocessors, (192) CUDA Cores/MP: 2496 CUDA Cores
>>>>> GPU Clock rate: 824 MHz (0.82 GHz)
>>>>> Memory Clock rate: 2505 Mhz
>>>>> Memory Bus Width: 384-bit
>>>>> L2 Cache Size: 1572864 bytes
>>>>> Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
>>>>> Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
>>>>> Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
>>>>> Total amount of constant memory: 65536 bytes
>>>>> Total amount of shared memory per block: 49152 bytes
>>>>> Total number of registers available per block: 65536
>>>>> Warp size: 32
>>>>> Maximum number of threads per multiprocessor: 2048
>>>>> Maximum number of threads per block: 1024
>>>>> Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
>>>>> Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
>>>>> Maximum memory pitch: 2147483647 bytes
>>>>> Texture alignment: 512 bytes
>>>>> Concurrent copy and kernel execution: Yes with 2 copy engine(s)
>>>>> Run time limit on kernels: No
>>>>> Integrated GPU sharing Host Memory: No
>>>>> Support host page-locked memory mapping: Yes
>>>>> Alignment requirement for Surfaces: Yes
>>>>> Device has ECC support: Enabled
>>>>> Device supports Unified Addressing (UVA): Yes
>>>>> Device PCI Bus ID / PCI location ID: 6 / 0
>>>>> Compute Mode:
>>>>> < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
>>>>>> Peer access from Tesla K80 (GPU0) -> Tesla K80 (GPU1) : Yes
>>>>>> Peer access from Tesla K80 (GPU1) -> Tesla K80 (GPU0) : Yes
>>>>> deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 2, Device0 = Tesla K80, Device1
>>>>> = Tesla K80
>>>>> Result = PASS
>>>>>
>>>>>
>>>>>
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--
-------------------------
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Department of Medicinal Chemistry
University of Utah
30 South 2000 East, Room 307
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Received on Sat Feb 13 2016 - 08:30:04 PST
