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Cuda_error_launch_failed

All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA. See Also:Constant Field Values CUDA_ERROR_INVALID_PTX public static finalint CUDA_ERROR_INVALID_PTX This indicates that a PTX JIT compilation failed. In doubt, I might try to "patch" the Visual Profiler (blatantly violating the EULA, by the way), but am not sure whether this is possible. This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls).

static int CUDA_ERROR_PROFILER_NOT_INITIALIZED Deprecated. Each CPU runs ONLY a single stream and each CPU thread gives commands to execute a set of kernels on different, independent data blocks. Here's the code: https://gist.github.com/arunmallya/c7b6c6cafa6252172727 And here's the imdb: https://www.dropbox.com/s/xapokyw8iinbcyl/imdbHICO.mat?dl=0 I really hope it isn't some silly mistake on my part causing the error :) Sign up for free to join Is there any reason to think that a large multivector should run into resizing problems of this type? > > — > Reply to this email directly or view it on https://www.mathworks.com/matlabcentral/answers/34052-cuda_error_launch_failed-problem

Foren-Regeln -- Standard Style -- Default Mobile Style -- Deutsch (Du) -- Deutsch (Sie) -- English Kontakt Byte-Welt Archiv Impressum Nach oben Alle Zeitangaben in WEZ +1. Running in the debugger shows that this exception is raised from the calling sequence bool boost::numeric::odeint::controlled_runge_kutta, double, vex::multivector, double, boost::numeric::odeint::vector_space_algebra, boost::numeric::odeint::default_operations, boost::numeric::odeint::initially_resizer>, boost::numeric::odeint::default_error_checker, boost::numeric::odeint::initially_resizer, boost::numeric::odeint::explicit_error_stepper_fsal_tag>::resize_m_xnew_impl >(vex::multivectorindicates that a resource has already been acquired.

All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA. static int CUDA_ERROR_ASSERT A device-side assert triggered during kernel execution. The context cannot be used (and must be destroyed similar to ::CUDA_ERROR_LAUNCH_FAILED). In case, multiple contexts are allowed to run concurrently, will it provide similar timing output as compared to scenario 1 (assuming "n" streams in scenario 1 and "n" CPU threads, each

a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error. The context cannot be used anymore, and must be destroyed. Compare the fused kernel for the sliced expressions: extern "C" __global__ void vexcl_multivector_kernel ( ulong n, double * prm_tag_0_1, ulong lhs_1_slice_start, ulong lhs_1_slice_length0, long lhs_1_slice_stride0, ulong lhs_1_slice_length1, long lhs_1_slice_stride1, ulong rhs_1_slice_start, Sign in to comment Contact GitHub API Training Shop Blog About © 2016 GitHub, Inc.

arunmallya commented Nov 30, 2015 No, I'm not using cuDNN I can share the model I am using but the exact training setting might be hard to reproduce since I just With some kernels this works very well, but with my largest system of equations I am encountering problems when odeint-v2 asks for the state vector to be resized following a step. It is no longer an error to attempt to enable/disable the profiling via ::cuProfilerStart or ::cuProfilerStop without initialization. This result is not actually an error, but must be indicated differently than ::CUDA_SUCCESS (which indicates completion).

This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had cuCtxDestroy() invoked on it). more info here The context cannot be used, so it must be destroyed (and a new one should be created). CUDA_ERROR_ARRAY_IS_MAPPED This indicates that the specified array is currently mapped and thus cannot be destroyed. See Also:Constant Field Values CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES public static finalint CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES This indicates that a launch did not occur because it did not have appropriate resources.

Reload to refresh your session. You might get away by just generating matrices of zeros in getBatch (hence without any need to sharing images). … On 30 Nov 2015, at 01:03, Arun Mallya ***@***.***> wrote: No, ddemidov closed this Dec 18, 2013 Sign up for free to join this conversation on GitHub. static int CUDA_ERROR_NOT_SUPPORTED This error indicates that the attempted operation is not supported on the current system or device.

vexcl owner ddemidov commented Dec 18, 2013 I had to do this for the kernel in rhs_functor::operator() to compile: diff --git a/launch_failure_kernel.cpp b/launch_failure_kernel.cpp index 00235e2..44e29e5 100644 --- a/launch_failure_kernel.cpp +++ b/launch_failure_kernel.cpp @@ It is no longer an error to attempt to push the active context via cuCtxPushCurrent(). During the development phase, you might just use Eclipse -> "Run" menu -> "Run Configurations" -> [Your run config for the application] -> "Environment" Tab -> "New..." Button to add environment static int CUDA_ERROR_ILLEGAL_INSTRUCTION While executing a kernel, the device encountered an illegal instruction.

In the case of query calls, this can also mean that the operation being queried is complete (see cuEventQuery() and cuStreamQuery()). Related Content Join the 15-year community celebration. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

We would like to set it to 32 but are unaware as to how to set this environment variable (in my .bashrc?

I have no problems with a very analogous kernel which uses a vex::multivector as the state. What might be the possible cause of error? auto x0 = slice[0](X); auto x1 = slice[1](X); auto x2 = slice[2](X); // write individual components: x0 = 1; x1 = 2; x2 = 3; // Do the fused call: vex::tie(x0, vexcl owner ddemidov commented Dec 18, 2013 Usually this kind of error comes from the previous kernel launch.

static int CUDA_ERROR_ECC_UNCORRECTABLE This indicates that an uncorrectable ECC error was detected during execution. Reload to refresh your session. arunmallya commented Nov 30, 2015 You're absolutely right, using zero matrices also causes the error on iter 30! But with CUDA 5.0, they introduced something like a more fine-grained control over the processes that are to be profiled, and in the Visual Profiler, the corresponding flag is not set

All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA. It was an address calculation error in the kernel. It is possible to use the commmand-line profiler "nvprof", or the Visual Profiler of an older CUDA version (4.2), but of course, these are workarounds that are not satisfactory. Is there any reason to think that a large multivector should run into resizing problems of this type?

static int CUDA_ERROR_HARDWARE_STACK_ERROR While executing a kernel, the device encountered a stack error. Thanks! 0 Comments Show all comments Log In to answer or comment on this question. static int CUDA_ERROR_CONTEXT_ALREADY_CURRENT This indicated that the context being supplied as a parameter to the API call was already the active context. So there might probably be arise some questions that I also can not answer off the top of my head, but I'll try: Depending on how the application will be started,