An effective 3-D fast fourier transform framework for multi-GPU accelerated distributed-memory systems

This paper introduces an efficient and flexible 3D FFT framework for state-of-the-art multi-GPU distributed-memory systems. In contrast to the traditional pure MPI implementation, the multi-GPU distributed-memory systems can be exploited by employing a hybrid multi-GPU programming model that combines MPI with OpenMP to achieve effective communication. An asynchronous strategy that creates multiple streams and threads to reduce blocking time is adopted to accelerate intra-node communication. Furthermore, we combine our scheme with the GPU-Aware MPI implementation to perform GPU-GPU data transfers without CPU involvement. We also optimize the local FFT and transpose by creating fast parallel kernels to accelerate the total transform. Results show that our framework outperforms the state-of-the-art distributed 3D FFT library, being up to achieve 2x faster in a single node and 1.65x faster using two nodes.

Automated summary

This paper introduces an efficient 3D FFT framework for multi-GPU distributed-memory systems, which utilizes a hybrid programming model combining MPI and OpenMP for effective communication, and adopts an asynchronous strategy and fast parallel kernels for acceleration.