The 2D wavelet transform on emerging architectures: GPUs and multicores

Because of the computational power of today's GPUs, they are starting to be harnessed more and more to help out CPUs on high-performance computing. In addition, an increasing number of today's state-of-the-art supercomputers include commodity GPUs to bring us unprecedented levels of performance in terms of raw GFLOPS and GFLOPS/cost. In this work, we present a GPU implementation of an image processing application of growing popularity: The 2D fast wavelet transform (2D-FWT). Based on a pair of Quadrature Mirror Filters, a complete set of application-specific optimizations are developed from a CUDA perspective to achieve outstanding factor gains over a highly optimized version of 2D-FWT run in the CPU. An alternative approach based on the Lifting Scheme is also described in Franco et al. (Acceleration of the 2D wavelet transform for CUDA-enabled Devices, 2010). Then, we investigate hardware improvements like multicores on the CPU side, and exploit them at thread-level parallelism using the OpenMP API and pthreads . Overall, the GPU exhibits better scalability and parallel performance on large-scale images to become a solid alternative for computing the 2D-FWT versus those thread-level methods run on emerging multicore architectures.