August 2016 - PyCUDA - tiker.net Mailing Lists

by Chris

Could I get some insight on how to convert this kernel code from C++ to Pycuda? cudaExtent volumeSize = make_cudaExtent(128, 128, 128); //sweep construction convert distance to material if needed (sweepfun == 1) void convert_dis2material(cudaExtent volumeSize,int originaldim,int sweepfun) { // Specify texture struct cudaResourceDesc resDescsurface; memset(&resDescsurface, 0, sizeof(resDescsurface)); resDescsurface.resType = cudaResourceTypeArray; resDescsurface.res.array.array = d_volumeArray1; //blue // Specify texture object parameters struct cudaTextureDesc texDescsurface; memset(&texDescsurface, 0, sizeof(texDescsurface)); texDescsurface.addressMode[0] = cudaAddressModeClamp; texDescsurface.addressMode[1] = cudaAddressModeClamp; texDescsurface.addressMode[2] = cudaAddressModeClamp; texDescsurface.filterMode = cudaFilterModePoint; texDescsurface.readMode = cudaReadModeElementType; texDescsurface.normalizedCoords = 0; cudaTextureObject_t texObj1; cudaCreateTextureObject(&texObj1, &resDescsurface, &texDescsurface, NULL); surfaceintegral(texObj1,originaldim,volumeSize); //finishing integration on blue cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc<VolumeType>(); dim3 dimBlock1 (8,8,8); dim3 dimGrid1 ((volumeSize.width+dimBlock1.x-1)/dimBlock1.x,(volumeSize.height+dimBlock1.y-1)/dimBlock1.y,(volumeSize.depth+dimBlock1.z-1)/dimBlock1.z); float *d_volume; checkCudaErrors(cudaMalloc((void**)&d_volume, volumeSize.depth*volumeSize.depth*volumeSize.depth*sizeof(float))); dis2material_kernel<<<dimGrid1,dimBlock1>>>(d_volume,texObj1,sweepfun,volumeSize,originaldim,maxnegdis,objcoorminx,objcoormaxx,objcoorminy,objcoormaxy,objcoorminz,objcoormaxz); cudaDeviceSynchronize(); // create 3D array cudaFreeArray(d_volumeArray1); checkCudaErrors(cudaMalloc3DArray(&d_volumeArray1, &channelDesc, volumeSize)); // copy data to 3D array cudaMemcpy3DParms copyParams = {0}; copyParams.srcPtr = make_cudaPitchedPtr(d_volume, volumeSize.width*sizeof(VolumeType), volumeSize.width, volumeSize.height); copyParams.dstArray = d_volumeArray1; copyParams.extent = volumeSize; copyParams.kind = cudaMemcpyDeviceToDevice; checkCudaErrors(cudaMemcpy3D(&copyParams)); cudaFree(d_volume); cudaDestroyTextureObject(texObj1); //destroy texobj before update the new d_volumeArray to it. } -- View this message in context: http://pycuda.2962900.n2.nabble.com/C-to-Python-code-conversion-help-tp75... Sent from the PyCuda mailing list archive at Nabble.com.

2 years, 10 months

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Re: [PyCUDA] Questions about PyCUDA from a former CS450 student

by Andreas Kloeckner

Yiming Peng <ypeng(a)u.northwestern.edu> writes: > Hi Andreas, > > I am a former student of your CS 450 and now I am a incoming PhD student in > operations research at Northwestern. > > Since I am interested in applying parallel computing, preferably using > Python, to my future research, I have been looking for software which > combines Python with CUDA. Then I found PyCUDA on your website. And I found > NumbaPro. It seems that these two are the most popular choices for people > with needs like mine. > > So my question is: which one do I begin to learn and use first? Could you > give some comments on pros and cons about the two? Cc'ing the PyCUDA list for archival/searchability. - PyCUDA lets you/forces you to write CUDA C for your kernels. - Numba lets you write (a narrow subset of) Python for your kernels, including arrays I believe. - The code you write for both will be roughly equivalent modulo spelling, since you'll have to - PyCUDA exposes (nearly) the entire CUDA runtime, including streams, profiling, textures, ... Numba is more restricted. - PyCUDA comes with an on-device array type. I'm not sure if Numba's arrays stay on-device after the computation finishes--i.e. you may have some implicit copying. - PyCUDA comes with some pre-made parallel algorithms such as scans and reductions. - You may also want to take a look at - https://documen.tician.de/pyopencl/ - https://documen.tician.de/loopy/ Hope that helps, Andreas

2 years, 10 months

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Re: [PyCUDA] Pycuda multiple gpus

by Andreas Kloeckner

Irving Enrique Reyna Nolasco <irvingenrique.reynanolasco(a)kaust.edu.sa> writes: > I am a student in physics. I am pretty new > in pycuda. Currently I am interesting in finit volume methods running on > multiple GPUS in a single node. I have not found relevant documentation > related to this issue, specifically how to communicate different contexts > or how to run the same kernel on different devices at the same time. > Would you suggest me some literature/documentation about that? I think the common approach is to have multiple (CPU) threads and have each thread manage one GPU. Less common (but also possible, if cumbersome) is to only use one thread and switch contexts. (FWIW, (Py)OpenCL makes it much easier to talk to multiple devices from a single thread.) Lastly, if you're thinking of scaling up, you could just have one MPI rank per device. Hope that helps, Andreas

2 years, 11 months

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Pycuda multiple gpus

by Irving Enrique Reyna Nolasco

I am a student in physics. I am pretty new in pycuda. Currently I am interesting in finit volume methods running on multiple GPUS in a single node. I have not found relevant documentation related to this issue, specifically how to communicate different contexts or how to run the same kernel on different devices at the same time. Would you suggest me some literature/documentation about that? Regards -- ------------------------------ This message and its contents, including attachments are intended solely for the original recipient. If you are not the intended recipient or have received this message in error, please notify me immediately and delete this message from your computer system. Any unauthorized use or distribution is prohibited. Please consider the environment before printing this email.

2 years, 11 months

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Re: [PyCUDA] New to OpenGL Interoperability

by Andreas Kloeckner

Hi Chris, Not sure what you're asking. The code you show doesn't apply--it uses the 'runtime API' (cudaXyz...), PyCUDA uses the 'driver API' (cuXyz...). And the piece of Peter's example that worries about exchanging data with PyCUDA (lines 162-192) is about the same in complexity as what you're showing. Andreas Chris Uchytil <uchytilc(a)uw.edu> writes: > I am brand new to CUDA and OpenGL and I have found that tutorials and > resources on a lot of this material is rather scares or not straight > forward so I am hoping I can get some assistance here. I am working on a > project attempting to convert some CUDA and OpenGL C++ code over to Python. > The code is a basic Kernal that computes distance from a point (To emulate > light on a wall from a flashlight) and sends the calculated array to OpenGL > to display the light intensity. You can move your mouse/"Flashlight" around > to move the light around on the screen. I have been successful in > converting the Kernal code over to Python using the Numba python package. > What I am having trouble with is the Open GL Interoperability stuff. I > can't really find an info that describes the process of interop in a simple > fashion so I'm not really even sure what the setup process is. It sounds > like you need to create something called a pixel buffer and send that to > the kernal. From what I can tell the C++ code uses this simple function to > do this. > > // texture and pixel objects > GLuint pbo = 0; > GLuint tex = 0 > struct cudaGraphicsResource * cuda_pbo_resource; > > void render() { > unchar4 *d_out = 0; > cudaGraphicsMapResources(1, &cuda_pbo_resource, 0); > cudaGraphicsResourceGetMappedPointer((void**)&d_out, NULL, > cuda_pbo_resource); > kernelLauncher(d_out, W, H, loc); > cudaGraphicsUnmapResources(1, &cuda_pbo_resource, 0); > } > > > I can't find any info that describes the python equivalent of > cudaGraphicsMapResources, cudaGraphicsResourceGetMappedPointer, and > cudaGraphicsUnmapResources. I've found a GL interop example by by Peter > Berrington (https://wiki.tiker.net/PyCuda/Examples/GlInterop) but it seems > to me to be overly complicated in how it creates PBO's and textures and > such when compared to the C++ code. > _______________________________________________ > PyCUDA mailing list > PyCUDA(a)tiker.net > https://lists.tiker.net/listinfo/pycuda

2 years, 11 months

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New to OpenGL Interoperability

by Chris Uchytil

I am brand new to CUDA and OpenGL and I have found that tutorials and resources on a lot of this material is rather scares or not straight forward so I am hoping I can get some assistance here. I am working on a project attempting to convert some CUDA and OpenGL C++ code over to Python. The code is a basic Kernal that computes distance from a point (To emulate light on a wall from a flashlight) and sends the calculated array to OpenGL to display the light intensity. You can move your mouse/"Flashlight" around to move the light around on the screen. I have been successful in converting the Kernal code over to Python using the Numba python package. What I am having trouble with is the Open GL Interoperability stuff. I can't really find an info that describes the process of interop in a simple fashion so I'm not really even sure what the setup process is. It sounds like you need to create something called a pixel buffer and send that to the kernal. From what I can tell the C++ code uses this simple function to do this. // texture and pixel objects GLuint pbo = 0; GLuint tex = 0 struct cudaGraphicsResource * cuda_pbo_resource; void render() { unchar4 *d_out = 0; cudaGraphicsMapResources(1, &cuda_pbo_resource, 0); cudaGraphicsResourceGetMappedPointer((void**)&d_out, NULL, cuda_pbo_resource); kernelLauncher(d_out, W, H, loc); cudaGraphicsUnmapResources(1, &cuda_pbo_resource, 0); } I can't find any info that describes the python equivalent of cudaGraphicsMapResources, cudaGraphicsResourceGetMappedPointer, and cudaGraphicsUnmapResources. I've found a GL interop example by by Peter Berrington (https://wiki.tiker.net/PyCuda/Examples/GlInterop) but it seems to me to be overly complicated in how it creates PBO's and textures and such when compared to the C++ code.

2 years, 11 months

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PyCUDA August 2016