![]() Use the cudaOccupancyMaxPotentialBlockSize Function to Optimize Block Size ![]() This can be achieved by reducing the size of the shared memory arrays and by reusing shared memory between threads. To maximize the number of thread blocks that can run concurrently on the GPU, you should minimize the amount of shared memory used by each thread block. Minimize the Amount of Shared Memory Used by Each Thread Block This tells the compiler to allocate the variable in shared memory instead of the default global memory.įor example, to declare a variable a in shared memory, you can use the following code: _shared_ real(kind=8) :: a(1024) To declare variables in shared memory, you need to use the _shared_ keyword in your CUDA Fortran code. Here are some tips and best practices for optimizing shared memory usage in your CUDA Fortran applications: Use the shared Keyword to Declare Shared Memory Variables Tips and Best Practices for Optimizing Shared Memory Usage in CUDA Fortran Once you have determined the maximum amount of shared memory per block, you can use this information to optimize the shared memory usage in your CUDA Fortran program. In this code snippet, sharedMemPerBlock is the maximum amount of shared memory that can be used by a single block of threads, blockDim is the number of threads per block, func is the kernel function that will be executed on the device, devId is the ID of the current device, prop is a structure that contains information about the current device, and status is a variable that holds the status of the CUDA runtime API function calls. SharedMemPerBlock = cudaOccupancyMaxSharedMemoryPerBlock(func, blockDim, prop, status) ! Determine the maximum shared memory per block To use this function, you need to include the cuda_runtime_api module in your CUDA Fortran program and call the cudaOccupancyMaxSharedMemoryPerBlock function with the following parameters: integer(kind=c_int) :: sharedMemPerBlockĬall cudaGetDeviceProperties(prop, devId) This function returns the maximum amount of shared memory that can be used by a single block of threads on the current device. To determine the amount of shared memory used by your CUDA Fortran program, you can use the cudaOccupancyMaxSharedMemoryPerBlock function provided by the CUDA runtime API. How to Determine Shared Memory Usage in CUDA Fortran Accessing shared memory can be up to 100 times faster than accessing global memory, making it a valuable resource for optimizing CUDA applications. Shared memory is a critical resource in CUDA programming because it is much faster than other types of memory, such as global memory or constant memory. This memory is private to the thread block and is not visible to other thread blocks or the host CPU. ![]() Shared memory is a type of memory that is shared among threads within a thread block in CUDA programming. What Is Shared Memory in CUDA Programming? We will also provide some tips and best practices to optimize shared memory usage in your CUDA Fortran applications. In this article, we will explain the concept of shared memory in CUDA programming and demonstrate how to determine the amount of shared memory used by your program. Shared memory is a critical resource in CUDA programming, and understanding how to measure and optimize its usage can significantly improve the performance of your applications. | Miscellaneous How to Determine Shared Memory Usage in CUDA FortranĪs a data scientist or software engineer working with CUDA Fortran, you may encounter situations where you need to determine the amount of shared memory used by your program.
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