Vicente Galiano

Parallel strategies for 2D Discrete Wavelet Transform in shared memory systems and GPUs

In this work, we analyze the behavior of several parallel algorithms developed to compute the two-dimensional discrete wavelet transform using both OpenMP over a multicore platform and CUDA over a GPU. The proposed parallel algorithms are based on both regular filter-bank convolution and lifting transform with small implementations changes focused on both the memory requirements reduction and the complexity reduction. We compare our implementations against sequential CPU algorithms and other recently proposed algorithms like the SMDWT algorithm over different CPUs and the Wippig&Klauer algorithm over a GTX280 GPU. Finally, we analyze their behavior when algorithms are adapted to each architecture. Significant execution times improvements are achieved on both multicore platforms and GPUs. Depending on the multicore platform used, we achieve speed-ups of 1.9 and 3.4 using two and four processes, respectively, when compared to the sequential CPU algorithm, or we obtain speed-ups of 7.1 and 8.9 using eight and ten processes. Regarding GPUs, the GPU convolution algorithm using the GPU shared memory obtains speed-ups up to 20 when compared to the CPU sequential algorithm.

GPU-based 3D lower tree wavelet video encoder

The 3D-DWT is a mathematical tool of increasing importance in those applications that require an efficient processing of huge amounts of volumetric info. Other applications like professional video editing, video surveillance applications, multi-spectral satellite imaging, HQ video delivery, etc, would rather use 3D-DWT encoders to reconstruct a frame as fast as possible. In this article, we introduce a fast GPU-based encoder which uses 3D-DWT transform and lower trees. Also, we present an exhaustive analysis of the use of GPU memory. Our proposal shows good trade off between R/D, coding delay (as fast as MPEG-2 for High definition) and memory requirements (up to 6 times less memory than x264).

PyPnetCDF: A high level framework for parallel access to netCDF files

A Python tool for manipulating netCDF files in a parallel infrastructure is proposed. The parallel interface, PyPnetCDF, manages netCDF properties in a similar way to the serial version from ScientificPython, but hiding parallelism to the user. Implementations details and capabilities of the developed interfaces are given. Numerical experiments that show the friendly use of the interfaces and their behaviour compared with the native routines, are presented.

Performance analysis of frame partitioning in parallel HEVC encoders

The new video coding standard HEVC includes two concepts that allow to partition a frame into regions that can be independently encoded and decoded. These two concepts are named “Tiles” and “Slices”. In this paper, we present and analyze optimized parallel versions of the HEVC encoder based on tile and slice partitioning. We have evaluated the benefits and drawbacks of both approaches in terms of computational times and rate distortion performance. The results show that both approaches obtain good speed-ups, being the parallel version based on tiles the one that obtains the best trade-off between speed-up achieved (up to 9.3

Parallel nonlinear preconditioners on multicore architectures

\times

Shared Memory Tile-Based vs Hybrid Memory GOP-Based Parallel Algorithms for HEVC Encoder

×) and rate distortion performance loss (1.6% BD rate for AI mode and 2.2% for LB mode on average).

Multicore-based 3D-DWT video encoder

When optimizing a wavelet image coder, the two main targets are to (1) improve its rate-distortion (R/D) performance and (2) reduce the coding times. In general, the encoding engine is mainly responsible for achieving R/D performance. It is usually more complex than the decoding part. A large number of works about R/D or complexity optimizations can be found, but only a few tackle the problem of increasing R/D performance while reducing the computational cost at the same time, like Kakadu, an optimized version of JPEG2000. In this work we propose an optimization of the E_LTW encoder with the aim to increase its R/D performance through perceptual encoding techniques and reduce the encoding time by means of a graphics processing unit-optimized version of the two-dimensional discrete wavelet transform. The results show that in both performance dimensions, our enhanced encoder achieves good results compared with Kakadu and SPIHT encoders, achieving speedups of 6 times with respect to the original E_LTW encoder.

Fast 3D wavelet transform on multicore and many-core computing platforms

Parallel nonlinear preconditioners, for solving mildly nonlinear systems, are proposed. These algorithms are based on both the Fletcher–Reeves version of the nonlinear conjugate gradient method and a polynomial preconditioner type based on block two-stage methods. The behavior of these algorithms is analyzed when incomplete LU factorizations are used in order to obtain the inner splittings of the block two-stage method. As our illustrative example we have considered a nonlinear elliptic partial differential equation, known as the Bratu problem. The reported experiments show the performance of the algorithms designed in this work on two multicore architectures.