Cuda kernel crash

[jermainewang]

Hi Yangqing,

We met problem when running Caffe. We run Caffe following the step on http://caffe.berkeleyvision.org/imagenet.html for ImageNet training. Our training set is 1000 images selected from the whole imagenet dataset and the testing set is the same.
It crashes after 600 iterations, the error message is:
“F0118 21:19:41.088841 1589 padding_layer.cu:131] Cuda kernel failed. Error: unspecified launch failure”
We run it again and it still crashes after 740 iterators, with error message:
“F0118 20:57:41.093628 27945 math_functions.cpp:45] Check failed: (cublasSgemm_v2(Caffe::cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N)) == CUBLAS_STATUS_SUCCESS (14 vs. 0)”

Our testbed is Ubuntu 12.04 LTS with GTX Titan, CUDA 5.5

Do you have any idea what causes this crash ? Thank you very much.

Best regards,
Minjie

[mavenlin]

I have not encounter this crash, but the error code 14 often means memory release or copy before the preceding kernel is finished. It might be due to early time out of cudaDeviceSynchronize, which means the kernel is still executing when memory processing occurs. I also run into this error code in cuda-convnet in the conserve memory mode, not yet figure out the problem. My clue is that the kernel may take longer than the time limit allowed by the watchdog, thus early termination of cudaDeviceSynchronize, In my case, cudaDeviceSynchronize returns 4. In devices like K20, they may have a longer timeout, so the problem may not be noticed, but I'm probably wrong.

[Yangqing]

It would be great if you could provide a backtrack record (e.g. from gdb).
My feeling is that the GPU might be set to some mode that cause race
conditions...? Not sure about the exact cause, as we have also run the
program on Titans and it seemed working fine.

Yangqing

On Sun, Jan 19, 2014 at 7:22 AM, Lin Min notifications@github.com wrote:

I have not encounter this crash, but the error code 14 often means memory
release or copy before the preceding kernel is finished. It might be due to
early time out of cudaDeviceSynchronize, which means the kernel is still
executing when memory processing occurs. I also run into this error code in
cuda-convnet in the conserve memory mode, not yet figure out the problem.
My clue is that the kernel may take longer than the time limit allowed by
the watchdog, thus early termination of cudaDeviceSynchronize, In my case,
cudaDeviceSynchronize returns 4. In devices like K20, they may have a
longer timeout, so the problem may not be noticed, but I'm probably wrong.

—
Reply to this email directly or view it on GitHubhttps://github.com//issues/39#issuecomment-32710616
.

[jermainewang]

Hi Lin, Yangqing,

It seems that it is not related to cudaDeviceSynchronize, since it still
crashes without this statement.

Here is the backtrace from the log Caffe printed:

*** Check failure stack trace: ***
@ 0x7fd5b3fb8b7d google::LogMessage::Fail()
@ 0x7fd5b3fbac7f google::LogMessage::SendToLog()
@ 0x7fd5b3fb876c google::LogMessage::Flush()
@ 0x7fd5b3fbb51d google::LogMessageFatal::~LogMessageFatal()
@ 0x4622b9 caffe::PaddingLayer<>::Backward_gpu()
@ 0x42cfab caffe::Net<>::Backward()
@ 0x424858 caffe::Solver<>::Solve()
@ 0x40e745 main
@ 0x7fd5b269976d (unknown)
@ 0x40fa2d (unknown)

And also the backtrace from gdb

(gdb) bt
#0 0x00007f7108833425 in __GI_raise (sig=) at
../nptl/sysdeps/unix/sysv/linux/raise.c:64
#1 0x00007f7108836b8b in __GI_abort () at abort.c:91
#2 0x00007f710a147039 in google::DumpStackTraceAndExit () at
src/utilities.cc:147
#3 0x00007f710a13db7d in google::LogMessage::Fail () at
src/logging.cc:1458
#4 0x00007f710a13fc7f in google::LogMessage::SendToLog
(this=0x7fff9907ed60) at src/logging.cc:1412
#5 0x00007f710a13d76c in google::LogMessage::Flush (this=0x7fff9907ed60)
at src/logging.cc:1281
#6 0x00007f710a14051d in google::LogMessageFatal::~LogMessageFatal
(this=0x7fff9907ed60, __in_chrg=) at src/logging.cc:1984
#7 0x0000000000436512 in caffe::caffe_gpu_gemm (TransA=CblasTrans,
TransB=CblasNoTrans, M=1728, N=169, K=128, alpha=1, A=0x235b9f8000,
B=0x237b36ba00, beta=0, C=0x238289d300)
at src/caffe/util/math_functions.cpp:44
#8 0x0000000000461069 in caffe::ConvolutionLayer::Backward_gpu
(this=0x5b63dc0, top=..., propagate_down=true, bottom=0x2332778) at
src/caffe/layers/conv_layer.cpp:245
#9 0x000000000043fc7f in caffe::Layer::Backward (this=0x5b63dc0,
top=..., propagate_down=true, bottom=0x2332778) at
./include/caffe/layer.hpp:114
#10 0x000000000043a5ff in caffe::Net::Backward (this=0x2332420) at
src/caffe/net.cpp:232
#11 0x0000000000431cc3 in caffe::Net::ForwardBackward
(this=0x2332420, bottom=...) at ./include/caffe/net.hpp:52
#12 0x000000000042e21c in caffe::Solver::Solve
(this=0x7fff9907f080, resume_file=0x0) at src/caffe/solver.cpp:58
#13 0x000000000040e6f4 in main (argc=2, argv=0x7fff9907f2e8) at
examples/train_net.cpp:32

Hope this could help.

Thanks,
Minjie

2014/1/20 Yangqing Jia notifications@github.com

It would be great if you could provide a backtrack record (e.g. from gdb).
My feeling is that the GPU might be set to some mode that cause race
conditions...? Not sure about the exact cause, as we have also run the
program on Titans and it seemed working fine.

Yangqing

On Sun, Jan 19, 2014 at 7:22 AM, Lin Min notifications@github.com
wrote:

I have not encounter this crash, but the error code 14 often means
memory
release or copy before the preceding kernel is finished. It might be due
to
early time out of cudaDeviceSynchronize, which means the kernel is still
executing when memory processing occurs. I also run into this error code
in
cuda-convnet in the conserve memory mode, not yet figure out the
problem.
My clue is that the kernel may take longer than the time limit allowed
by
the watchdog, thus early termination of cudaDeviceSynchronize, In my
case,
cudaDeviceSynchronize returns 4. In devices like K20, they may have a
longer timeout, so the problem may not be noticed, but I'm probably
wrong.

—
Reply to this email directly or view it on GitHub<
https://github.com/BVLC/caffe/issues/39#issuecomment-32710616>
.

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Reply to this email directly or view it on GitHubhttps://github.com//issues/39#issuecomment-32715229
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Minjie Wang
Shanghai Jiao Tong University | Computer Science & Technology
Embedded & Pervasive Computing Center (EPCC)
No.800 Dongchuan Road, Shanghai, China
Homepage: https://sites.google.com/site/minjiewanghomepage/home

[kloudkl]

@Lin-Min, CCCP(Cascaded Cross Channel Parametric) Pooling is cool ! There used to be another CCCP (Союз Советских Социалистических Республик) which was cold. :)

[jermainewang]

We've found the problem. It is due to the configuration of our GPU card. After we adjust the fan speed, it runs without crashings.

[shelhamer]

@sergeyk the install docs could perhaps mention CUDA failures and advise checking hardware settings, since in every case so far it has been a hardware issue and not Caffe problem. Then this issue could be closed.

[Cheng-Wang]

@jermainewang , I encountered same problem, can you let me know the method that you used to adjust fan speed? I tried some methods, but it seems doesn't work.

thanks
Cheng

[jermainewang]

Hi cheng,

In fact, we still encounter the problem, but not that frequently. I guess
this is because GTX Titan card is not with ECC, which harms the stability
of a long-term running, especially programs of intensive computations like
what caffe does.

Best regards,
Minjie

2014-07-23 17:27 GMT+08:00 Cheng-Wang notifications@github.com:

@jermainewang https://github.com/jermainewang , I encountered same
problem, can you let me know the method that you used to adjust fan speed?
I tried some methods, but it seems doesn't work.

thanks
Cheng

—
Reply to this email directly or view it on GitHub
#39 (comment).

Minjie Wang
Shanghai Jiao Tong University | Computer Science & Technology
Embedded & Pervasive Computing Center (EPCC)
No.800 Dongchuan Road, Shanghai, China
Homepage: https://sites.google.com/site/minjiewanghomepage/home

[shelhamer]

ECC doesn't matter. We have run all our Caffe jobs with ECC disabled on
cards that support it or cards that don't, like Titans and GTX 770s and
never had these issues.

Look into other system issues.

Le mercredi 23 juillet 2014, jermainewang notifications@github.com a
écrit :

Hi cheng,

In fact, we still encounter the problem, but not that frequently. I guess
this is because GTX Titan card is not with ECC, which harms the stability
of a long-term running, especially programs of intensive computations like
what caffe does.

Best regards,
Minjie

2014-07-23 17:27 GMT+08:00 Cheng-Wang <notifications@github.com
javascript:_e(%7B%7D,'cvml','notifications@github.com');>:

@jermainewang https://github.com/jermainewang , I encountered same
problem, can you let me know the method that you used to adjust fan
speed?
I tried some methods, but it seems doesn't work.

thanks
Cheng

—
Reply to this email directly or view it on GitHub
#39 (comment).

Minjie Wang
Shanghai Jiao Tong University | Computer Science & Technology
Embedded & Pervasive Computing Center (EPCC)
No.800 Dongchuan Road, Shanghai, China
Homepage: home | minjiewanghomepage
https://sites.google.com/site/minjiewanghomepage/home

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Reply to this email directly or view it on GitHub
#39 (comment).