University:
Boston University
Course:
ENG EC 410 | Introduction to Electronics
Academic year:

2024
Views:

123

Pages:

19
Author:

Patricia West

CUFFT ‣ CUFFT: CUDA library for FFTs on the GPU ‣ Supported by NVIDIA ‣ Features: - 1D, 2D, 3D transforms for complex and real data - Batch execution for multiple transforms - Up to 128 million elements (limited by memory) - In-place or out-of-place transforms - Double precision on GT200 or later - Allows streamed execution: simultaneous computation and data movement 14 CUFFT - Types ‣ cufftHandle - Handle type to store CUFFT plans ‣ cufftResult - Return values, like CUFFT_SUCCESS, CUFFT_INVALID_PLAN, CUFFT_ALLOC_FAILED, CUFFT_INVALID_TYPE, etc. ‣ cufftReal ‣ cufftDoubleReal ‣ cufftComplex ‣ cufftDoubleComplex 15 CUFFT - Transform types ‣ R2C: real to complex ‣ C2R: Complex to real ‣ C2C: complex to complex ‣ D2Z: double to double complex ‣ Z2D: double complex to double ‣ Z2Z: double complex to double complex 16 CUFFT - Plans ‣ cufftPlan1d() ‣ cufftPlan2d() ‣ cufftPlan3d() ‣ cufftPlanMany() 17 CUFFT - Functions ‣ cufftDestroy - Free GPU resources ‣ cufftExecC2C, R2C, C2R, Z2Z, D2Z, Z2D - Performs the specified FFT ‣ For more details see the CUFFT Library documentation available in the NVIDIA website! 18 CUFFT - Performance considerations ‣ Several algorithms for different sizes ‣ Performance recommendations - Restrict size to be a multiple of 2, 3, 5 or 7 - Restrict the power-of-two factorization term of the X-dimension to be at least a multiple of 16 for single and 8 for double - Restrict the X-dimension of single precision transforms to be strictly a power of two between 2(2) and 2048(8192) for Tesla (Fermi) GPUs 19 CUFFT - Performance considerations ‣ CUFFT vs FFTW - CUFFT is good for larger, power of two sized FFTs - CUFFT is not good for small sized FFTs ‣ CPU can store all data in cache ‣ GPU data transfers take too long http://www.sharcnet.ca/~merz/CUDA_benchFFT/