The same happens for the blocks and the grid. When defining a variable of type dim3, any component left unspecified is initialized to 1. However, the access pattern depends on how you are interpreting your data and also how you are accessing them by 1D, 2D and 3D blocks of threads.ĭim3 is an integer vector type based on uint3 that is used to specify dimensions. The memory is always a 1D continuous space of bytes. CaveatsĬUDA zero copy memory disables data cache on GPUs, there might be performance drop for math bound kernels.The way you arrange the data in memory is independently on how you would configure the threads of your kernel. This is because the using mapped memory truly eliminated the memory copy between host and device on unified memory. We could see that for memory-bound kernel, on a platform that uses integrated GPU and unified memory, using mapped pinned memory is almost 6x faster than using non-mapped pinned memory. 1ĬUDA Kernel With Non-Mapped Pinned Memory Latency: 2.343 msĬUDA Kernel With Mapped Pinned Memory Latency: 0.431 ms This is the latency profiling on a NVIDIA Jetson Xavier. We could see that for memory-bound kernel, on a platform that uses discrete GPU, separate host memory, and device memory, using mapped pinned memory is almost 30% faster than using non-mapped pinned memory. $ nvcc mapped_memory.cu -o mapped_memory -std=c++14ĬUDA Kernel With Non-Mapped Pinned Memory Latency: 0.964 msĬUDA Kernel With Mapped Pinned Memory Latency: 0.631 ms This is the latency profiling on a desktop that has Intel Core i9-9900K CPU and NVIDIA RTX 3090 GPU. << latency_mapped_pinned_memory << " ms" << std::endl ĬHECK_CUDA_ERROR( cudaFreeHost(h_input_1)) ĬHECK_CUDA_ERROR( cudaFreeHost(h_input_2)) ĬHECK_CUDA_ERROR( cudaFreeHost(h_output)) ĬHECK_CUDA_ERROR( cudaFreeHost(a_input_1)) ĬHECK_CUDA_ERROR( cudaFreeHost(a_input_2)) ĬHECK_CUDA_ERROR( cudaFreeHost(a_output)) ĬHECK_CUDA_ERROR( cudaStreamDestroy(stream)) << "CUDA Kernel With Mapped Pinned Memory Latency: " << latency_non_mapped_pinned_memory << " ms" << std::endl << "CUDA Kernel With Non-Mapped Pinned Memory Latency: " Std::cout << std::fixed << std:: setprecision( 3) Void initialize_host_memory ( float* h_buffer, uint32_t n, float value)įor ( int i Void launch_float_addition_mapped_pinned_memory ( float* d_output, Void check (T err, const char* const func, const char* const file,įloat measure_performance (std::function bound_function,ĬudaStream_t stream, int num_repeats = 100,ĬHECK_CUDA_ERROR( cudaEventCreate(&start)) ĬHECK_CUDA_ERROR( cudaEventCreate(&stop)) ĬHECK_CUDA_ERROR( cudaMemcpyAsync(h_output, d_output, n * sizeof( float), # define CHECK_CUDA_ERROR(val) check((val), #val, _FILE_, _LINE_) For CUDA pinned memory, we still need to allocate device memory and transfer the data between the host memory and the device memory, whereas for CUDA mapped memory, the device memory allocation and memory transfer, if there is any, are abstracted. The following implementation compares the latency of a memory-bound kernel and its memory copy between host and device if necessary.ĬUDA mapped memory also uses pinned memory. Zero copy can be used in place of streams because kernel-originated data transfers automatically overlap kernel execution without the overhead of setting up and determining the optimal number of streams. Because the data is not cached on the GPU, mapped pinned memory should be read or written only once, and the global loads and stores that read and write the memory should be coalesced. On discrete GPUs, mapped pinned memory is advantageous only in certain cases. On integrated GPUs (i.e., GPUs with the integrated field of the CUDA device properties structure set to 1), mapped pinned memory is always a performance gain because it avoids superfluous copies as integrated GPU and CPU memory are physically the same. For this purpose, it requires mapped pinned (non-pageable, page-locked) memory. CUDA Pinned Mapped MemoryĬUDA pinned mapped memory enables GPU threads to directly access host memory. In this blog post, I would like to discuss the CUDA mapped pinned memory versus CUDA non-mapped pinned memory and compare their performance on memory bound kernels. In this way, the system performance could be improved significantly in some use cases.
Since the same memory is used for both the CPU and the integrated GPU, it is possible to eliminate the CUDA memory copy between host and device that normally happens on a system that uses discrete GPU so that the GPU can directly the access the outputs from CPU and the CPU can also directly access the outputs from GPU.
#DIM3 CUDA DEFINITION SERIES#
Unified memory is used on NVIDIA embedding platforms, such as NVIDIA Drive series and NVIDIA Jetson series.
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