NVIDIA cuFFT, a library that provides GPU-accelerated Fast Fourier Transform (FFT) implementations, is used for building applications across disciplines, such as deep learning, computer vision, computational physics, molecular dynamics, quantum chemistry, and seismic and medical imaging.
Vendor
NVIDIA
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NVIDIA cuFFT is a library that provides GPU-accelerated Fast Fourier Transform (FFT) implementations, used for building applications across disciplines such as deep learning, computer vision, computational physics, molecular dynamics, quantum chemistry, and seismic and medical imaging. The FFT is a divide-and-conquer algorithm for efficiently computing discrete Fourier transforms of complex or real-valued datasets. It’s one of the most important and widely used numerical algorithms in computational physics and general signal processing. The cuFFT library provides a simple interface for computing FFTs on an NVIDIA GPU, allowing users to quickly leverage the GPU’s floating-point power and parallelism in a highly optimized and tested FFT library.
Features
- Complete Support for BLAS Routines: Supports all 152 standard BLAS routines, ensuring comprehensive functionality for various linear algebra operations.
- Optimized GEMM Extensions: Includes GEMM and GEMM extensions with fusion optimized for Tensor Cores, providing high performance for matrix multiplication tasks.
- Multi-GPU and Multi-Node Capabilities: Offers APIs for single-process multi-GPU (cuBLASXt) and multi-node multi-GPU (cuBLASMp) operations, enabling efficient dispatching of workloads across multiple GPUs.
- Mixed- and Low-Precision Execution: Supports half-precision and integer matrix multiplication, leveraging tensor cores for acceleration.
- CUDA Streams: Utilizes CUDA streams for concurrent operations, enhancing performance and efficiency.
- Device-Side API Extensions: Provides device-side API extensions (cuBLASDx) for performing BLAS calculations inside CUDA kernels, reducing latency and improving performance.
Benefits
- High Performance: Optimized for NVIDIA GPUs, leveraging tensor cores for accelerated matrix multiplication.
- Scalability: Supports multi-GPU and multi-node operations, making it suitable for large-scale HPC applications.
- Flexibility: Offers a range of APIs for different levels of operations, from vector-vector to matrix-matrix calculations.
- Efficiency: Reduces latency and improves performance with device-side API extensions and CUDA streams.
- Comprehensive Functionality: Supports a wide range of BLAS routines and GEMM extensions, ensuring versatility for various applications.