Is there a trace or debug function that I can place in a CUDA kernel? I'm writing a program on Windows 7, VS2010 and I just discovered that to use NSIGHT Monitor I have to have 2 GPUs. I'm on a laptop unfortunately so this isn't really an option. I figured that I would fall back to tried and true debug/trace functions. Something akin to OutputDebugString. I don't see if one exists though.
Any help is appreciated. Thanks,
mj
Parallel Nsight 2.2 supports local single GPU debugging.
CUDA compute capability >= 2.0 support printf from device code.
Related
My CUDA program have too many kernel functions and if I open the CUDA debugging mode, I'll have to wait for an whole hour after the breakpoint in certain kernel function is triggered.
Is there any way for Nsight to start debugging after certain kernel functions, or only debug the certain kernel function?
I'm using Nsight with VS2012
In theory you can follow the instructions in the Nsight help file (either the online help or local help. At the time of writing the page is here).
In short:
In the Nsight Monitor options, CUDA » Use this Monitor for CUDA attach should be True.
Before starting your application, set an environment variable called NSIGHT_CUDA_DEBUGGER to 1.
Then in your CUDA kernel, you can add a breakpoint like this:
asm("brkpt;");
This will work similar to the __debugbreak() intrinsic or int 3 assembly instruction in host code. When hit you will get a dialog prompting you to attach the CUDA debugger.
In practice, at least for me it Just Doesn't Work™. Maybe you'll have more luck.
I'm trying to use nvvp to profile opencl kernels.
I'm running ubuntu 12.04 64b with a GTX 580 and have verified the CUDA toolkit is working fine (i can run and profile cuda code).
When trying to debug my opencl code i get:
Warning: No CUDA application was profiled, exiting
Any hints?
Nvidia's visual profiler (nvvp) can be used to profile OpenCL programs, but it is more of a pain than profiling in CUDA directly.
Simon McIntosh's High Performance Computing group over at the University of Bristol came up with the original solution (here), and I can verify it works.
I'll summarise the basics:
Firstly, the environment variable COMPUTE_PROFILE must be set, this is done with COMPUTE_PROFILE=1
Secondly a COMPUTE_PROFILE_CONFIG must be provided, a sample I use (called nvvp.cfg) contains:
profilelogformat CSV
streamid
gpustarttimestamp
gpuendtimestamp
Next to perform the actual profiling, in this case I'll profile an OpenCL application called HuffFramework, using:
COMPUTE_PROFILE=1 COMPUTE_PROFILE_CONFIG=nvvp.cfg ./HuffFramework
This then generates a series of opencl_profile_*.log files, where * is the number of threads.
These log files can't be loaded by nvvp just yet as all kernel function symbols have a leading OPENCL_ instead of an expected CUDA_, thus replace these symbols with a quick script like so:
sed 's/OPENCL_/CUDA_/g' opencl_profile_0.log > cuda_profile_0.log
Finally cuda_profile_0.log can now be imported by nvvp, by starting nvvp and going File->Import...->Command-line Profiler, point it to cuda_profile_0.log and preso!
nvvp can only profile CUDA applications.
I've been googling around and have only been able to find a trivial example of the new dynamic parallelism in Compute Capability 3.0 in one of their Tech Briefs linked from here. I'm aware that the HPC-specific cards probably won't be available until this time next year (after the nat'l labs get theirs). And yes, I realize that the simple example they gave is enough to get you going, but the more the merrier.
Are there other examples I've missed?
To save you the trouble, here is the entire example given in the tech brief:
__global__ ChildKernel(void* data){
//Operate on data
}
__global__ ParentKernel(void *data){
ChildKernel<<<16, 1>>>(data);
}
// In Host Code
ParentKernel<<<256, 64>>(data);
// Recursion is also supported
__global__ RecursiveKernel(void* data){
if(continueRecursion == true)
RecursiveKernel<<<64, 16>>>(data);
}
EDIT:
The GTC talk New Features In the CUDA Programming Model focused mostly on the new Dynamic Parallelism in CUDA 5. The link has the video and slides. Still only toy examples, but a lot more detail than the tech brief above.
Here is what you need, the Dynamic parallelism programming guide. Full of details and examples: http://docs.nvidia.com/cuda/pdf/CUDA_Dynamic_Parallelism_Programming_Guide.pdf
Just to confirm that dynamic parallelism is only supported on GPU's with a compute capability of 3.5 upwards.
I have a 3.0 GPU with cuda 5.0 installed I have compiled the Dynamic Parallelism examples
nvcc -arch=sm_30 test.cu
and received the below compile error
test.cu(10): error: calling a global function("child_launch") from a global function("parent_launch") is only allowed on the compute_35 architecture or above.
GPU info
Device 0: "GeForce GT 640"
CUDA Driver Version / Runtime Version 5.0 / 5.0
CUDA Capability Major/Minor version number: 3.0
hope this helps
I edited the question title to "...CUDA 5...", since Dynamic Parallelism is new in CUDA 5, not CUDA 4. We don't have any public examples available yet, because we don't have public hardware available that can run them. CUDA 5.0 will support dynamic parallelism but only on Compute Capability 3.5 and later (GK110, for example). These will be available later in the year.
We will release some examples with a CUDA 5 release candidate closer to the time the hardware is available.
I think compute capability 3.0 doesn´t include dynamic paralelism. It will be included in the GK110 architecture (aka "Big Kepler"), I don´t know what compute capability number will have assigned (3.1? maybe). Those cards won´t be available until late this year (I´m waiting sooo much for those). As far as I know the 3.0 corresponds to the GK104 chips like the GTX690 o the GT640M for laptops.
Just wanted to check in with you all given that the CUDA 5 RC was released recently. I looked in the SDK examples and wasn't able to find any dynamic parallelism there. Someone correct me if I'm wrong. I searched for kernel launches within kernels by grepping for "<<<" and found nothing.
I have problem running samples provided by Nvidia in their GPU Computing SDK (there's a library of compiled sample codes).
For cuda I get message "No CUDA-capable device is detected", for OpenCL there's error from function that should find OpenCL capable units.
I have installed all three parts from Nvidia to develop with OpenCL - devdriver for win7 64bit v.301.27, cuda toolkit 4.2.9 and gpu computing sdk 4.2.9.
I think this might have to do with Optimus technology that reroutes output from Nvidia GPU to Intel to render things (this notebook has also Intel 3000HD accelerator), but in Nvidia control pannel I set to use high performance Nvidia GPU, set power profile to prefer maximum performance and for PhysX I changed from automatic selection to Nvidia processor again. Nothing has changed though, those samples won't run (not even those targeted for GF8000 cards).
I would like to play somewhat with OpenCL and see what it is capable of but without ability to test things it's useless. I have found some info about this on forums, but it was mostly about linux users where you need Bumblebee to access Nvidia GPU. There's no such problem on Windows however, drivers are better and so you can access it without dark magic (or I thought so until I found this problem).
My laptop has a GeForce 540M as well, in an Optimus configuration since my Sandy Bridge CPU also has Intel's integrated graphics. To run CUDA codes, I have to:
Install NVIDIA Driver
Go to NVIDIA Control Panel
Click 3D Settings -> Manage 3D Settings -> Global Settings
In the Preferred Graphics processor drop down, select "High-performance NVIDIA processor"
Apply the settings
Note that the instructions above apply the settings for all applications, so you don't have to worry about CUDA errors any more. But it will drain more battery.
Here is a video recap as well. Good luck!
Ok this has proven to be totally crazy solution. I was thinking if something isn't hooking between the hardware and application and only thing that came to my mind was AV software. I'm using Comodo with sandbox and Defense+ on and after turning them off I could run all those samples. What's more, only Defense+ needs to be turned off.
Now I just think about how much apps could have been blocked from accessing that GPU..
That's most likely because of the architecture of Optimus. So I'd suggest you to read
NVIDIA CUDA Developer Guide for NVIDIA Optimus Platforms, especially the section "Querying for a CUDA Device" which addresses this issue, I believe.
I upgraded CUDA GPU computing SDK and CUDA computing toolkit to 4.1. I was testing simpleStreams programs, but consistently it is taking more time that non-streamed execution. my device is with compute capability 2.1 and i'm using VS2008,windows OS.
This sample constantly has issues. If you tweak the sample to have equal duration for the kernel and memory copy the overlap will improve. Normally breadth first submission is better for concurrency; however, on WDDM OS this sample will usually have better overlap if you issue the memory copy right after kernel launch.
I noticed this as well. I thought it was just me but I didn't notice any improvement and tried searching the forums but didn't find anyone else with the issue.
I also ran the source code in the Cuda By Example book (which is really helpful and I recommend you pick it up if you're serious about GPU programming).
Chapter 10 examples has the progression of examples showing how streams should be used.
http://developer.nvidia.com/content/cuda-example-introduction-general-purpose-gpu-programming-0
But comparing the,
1. non-streamed version(which is basically the single stream version)
2. the streamed (incorrectly queued asyncmemcpy and kernel launch)
3. the streamed (correctly queued asyncmemcpy and kernel launch)
I find no benefit in using cuda streams. It might be a win7 issue as I found some sources online discussing that win vista didn't support the cuda streams correctly.
Let me know what you find with the example I linked. My setup is: Win7 64bit Pro, Cuda 4.1, Dual Geforce GTX460 cards, 8GB RAM.
I'm pretty new to Cuda so may not be able to help but generally its very hard to help without you posting any code. If posting is not possible then I suggest you take a look at Nvidia's visual profiler. Its cross platform and can show you were your bottlenecks are.