The Linux release for simplecuFFT assumes that the root install directory is /usr/local/cuda and that the locations of the products are contained there as follows. Modify the Makefile as appropriate for your system.
Functions in the cuFFT and cuFFTW library assume that the data is in GPU visible memory. This means any memory allocated by cudaMalloc, cudaMallocHost and cudaMallocManaged or registered with cudaHostRegister can be used as input, output or plan work area with cuFFT and cuFFTW functions. For the best performance input data, output data and plan work area should reside in device memory.
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If the advanced parameters are to be used, then all of the advanced interface parameters must be specified correctly. Advanced parameters are defined in units of the relevant data type (cufftReal, cufftDoubleReal, cufftComplex, or cufftDoubleComplex).
On a single GPU users may call cudaMalloc() and cudaFree() to allocate and free GPU memory. To provide similar functionality in the multiple GPU case, cuFFT includes cufftXtMalloc() and cufftXtFree() functions. The function cufftXtMalloc() returns a descriptor which specifies the location of these memories.
On a single GPU users may call cudaMemcpy() to transfer data between host and GPU memory. To provide similar functionality in the multiple GPU case, cuFFT includes cufftXtMemcpy() which allows users to copy between host and multiple GPU memories or even between the GPU memories.
hostCopyOfCallbackPtr then contains the device address of the callback routine, that should be passed to cufftXtSetCallback. Note that, for multi-GPU transforms, hostCopyOfCallbackPtr will need to be an array of pointers, and the cudaMemcpyFromSymbol will have to be invoked for each GPU. Please note that __managed__ variables are not suitable to pass to cufftSetCallback due to restrictions on variable usage (See the NVIDIA CUDA Programming Guide for more information about __managed__ variables).
Note that in this case, the library cuda is not needed. The CUDA Runtime will try to open explicitly the cuda library if needed. In the case of a system which does not have the CUDA driver installed, this allows the application to gracefully manage this issue and potentially run if a CPU-only path is available.
The memory assigned as work area needs to be GPU visible. In addition to the regular memory acquired with cudaMalloc, usage of CUDA Unified Virtual Addressing enables cuFFT to use the following types of memory as work area memory: pinned host memory, managed memory, memory on GPU other than the one performing the calculations. While this provides flexibility, it comes with a performance penalty whose magnitude depends on the available memory bandwidth.
Function cufftXtExecDescriptor() executes any cuFFT transform regardless of precision and type. In case of complex-to-real and real-to-complex transforms direction parameter is ignored. cuFFT uses the GPU memory pointed to by cudaLibXtDesc *input descriptor as input data and cudaLibXtDesc *output as output data.
cufftXtExecDescriptorC2C() (cufftXtExecDescriptorZ2Z()) executes a single-precision (double-precision) complex-to-complex transform plan in the transform direction as specified by direction parameter. cuFFT uses the GPU memory pointed to by cudaLibXtDesc *input as input data. Since only in-place multiple GPU functionality is supported, this function also stores the result in the cudaLibXtDesc *input arrays.
cufftXtExecDescriptorR2C() (cufftXtExecDescriptorD2Z()) executes a single-precision (double-precision) real-to-complex transform plan. cuFFT uses the GPU memory pointed to by cudaLibXtDesc *input as input data. Since only in-place multiple GPU functionality is supported, this function also stores the result in the cudaLibXtDesc *input arrays.
cufftXtExecDescriptorC2R() (cufftXtExecDescriptorZ2D()) executes a single-precision (double-precision) complex-to-real transform plan in the transform direction as specified by direction parameter. cuFFT uses the GPU memory pointed to by cudaLibXtDesc *input as input data. Since only in-place multiple GPU functionality is supported, this function also stores the result in the cudaLibXtDesc *input arrays.
CUFFT_COPY_HOST_TO_DEVICE copies data from a contiguous host buffer to multiple device buffers, in the layout cuFFT requires for input data. dstPointer must point to a cudaLibXtDesc structure, and srcPointer must point to a host memory buffer.
CUFFT_COPY_DEVICE_TO_HOST copies data from multiple device buffers, in the layout cuFFT produces for output data, to a contiguous host buffer. dstPointer must point to a host memory buffer, and srcPointer must point to a cudaLibXtDesc structure.
CUFFT_COPY_DEVICE_TO_DEVICE copies data from multiple device buffers, in the layout cuFFT produces for output data, to multiple device buffers, in the layout cuFFT requires for input data. dstPointer and srcPointer must point to different cudaLibXtDesc structures (and therefore memory locations). That is, the copy cannot be in-place. Note that device_to_device cufftXtMemcpy() for 2D and 3D data is not currently supported.
I have successfully used the 11.1 converter for Cuda 11.2, however with Cuda 11.3 it sometimes gives the following error: RuntimeError: CUDA error: an illegal memory access was encountered
The other times it runs correctly except for the fact that the maximum confidence the detections can have are 73% where the exact same model on the earlier version had 100% confidence for those test cases.
Yes then it works fine, even release version 21.03 works okay. However I wanted to use some of the other backends prebuild in the 21.04 version. I can use the older version and build the backends myself but I thought to check if it was possible to have the tlt-converter updated.
The HIP runtime API generally mirrors the CUDA one, simply by replacing the cuda text in the call with hip gets you the equivalent HIP runtime call in most cases. Table 2 shows a simple comparison with how the calls change between HIP and CUDA, the HIP version will naturally also include different header files for the runtime API. There are cases where the conversion is not direct, in some cases certain arguments need passing in different ways, but generally if there is an equivalent HIP call it is just a question of replacing cuda with hip and the call will work.
The easier to use tool is the hipify-perl tool, it will attempt to hipify the CUDA code through basic find and replace techniques the cuda string in API calls is replaced with hip, it is and however a bit more intelligent, it will also add the appropriate headers and for HIP calls where the arguments are different it will attempt to correct this. The majority of cases the script will manage to do the entire conversion, but you should always check the correctness of the translation.
Hipify-clang is a more advanced tool for doing the conversion, it is based on the clang compiler and thus can have more context for the code when doing the conversion than just a simple find/replace. Since this tool is based on clang it comes with the caveat that the code needs to be compliable, meaning at times you need to add headers, defines etc. to make sure the code can be compiled.
In this example the file saxpycuda.cpp includes saxpy code written in CUDA, it consists of a few memory allocation and copy calls as well as the kernel and the code to launch it. In order to convert it these CUDA API calls would have to be translated to HIP.
Accessor objects have a relatively high level interface, with .size() and.stride() methods and multi-dimensional indexing. The .accessorinterface is designed to access data efficiently on cpu tensor. The equivalentfor cuda tensors are packed_accessor64 and packed_accessor32, whichproduce Packed Accessors with either 64-bit or 32-bit integer indexing.
The CUDA code that illustrates this approach is shown in the supporting file pctdemo_life_cuda_shmem.cu. To access this supporting file, open this example as a live script. The CUDA device function in this file operates as follows:
CUDA is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming.[2] CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC and OpenCL;[3][1] and HIP by compiling such code to CUDA.
So, I tried to compile Cmake with Cuda support.But unfortunately it does not accept specified file for Cuda.I have checke the cuda file and libraries which is available in opencv.They are all available in " C:/opencv1/opencv/sources/modules/" direction but Cmake does not see it.
However, when I list the supported encoder settings using ffmpeg -h encoder=hevc_nvenc (output of command pasted below), even though it supports main10, no 10-bit pixel format is supported: Supported pixel formats: yuv420p nv12 p010le yuv444p p016le yuv444p16le bgr0 rgb0 cuda d3d11. Does the encoding work for my use case, or not?
This document provides an overview of GPUs support in TensorFlow Lite, and someadvanced uses for GPU processors. For more specific information aboutimplementing GPU support on specific platforms, see the following guides:
You can use additional, advanced techniques with GPU processing to enable evenbetter performance for your models, including quantization and serialization.The following sections describe these techniques in further detail.
The CNTK 2.7 release has full support for ONNX 1.4.1, and we encourage those seeking to operationalize their CNTK models to take advantage of ONNX and the ONNX Runtime. Moving forward, users can continue to leverage evolving ONNX innovations via the number of frameworks that support it. For example, users can natively export ONNX models from PyTorch or convert TensorFlow models to ONNX with the TensorFlow-ONNX converter.
In this work we describe HIPCL, a new tool which allows running HIP programs on OpenCL platforms with sufficient capabilities. HIPCL thus expands the scope of the CUDA portability route from AMD ROCm platform supported targets to platforms with advanced OpenCL support. We highlight the implementation challenges of HIPCL related to the feature mismatches between CUDA and OpenCL and exemplify its runtime overheads in comparison to directly executing OpenCL applications. be457b7860
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