Low energy video processing and compression hardware designs

Digital video processing and compression algorithms are used in many commercial products such as mobile devices, unmanned aerial vehicles, and autonomous cars. Increasing resolution of videos used in these commercial products increased computational complexities of digital video processing and compression algorithms. Therefore, it is necessary to reduce computational complexities of digital video processing and compression algorithms, and energy consumptions of digital video processing and compression hardware without reducing visual quality. In this thesis, we propose a novel adaptive 2D digital image processing algorithm for 2D median filter, Gaussian blur and image sharpening. We designed low energy 2D median filter, Gaussian blur and image sharpening hardware using the proposed algorithm. We propose approximate HEVC intra prediction and HEVC fractional interpolation algorithms. We designed low energy approximate HEVC intra prediction and HEVC fractional interpolation hardware. We also propose several HEVC fractional interpolation hardware architectures. We propose novel computational complexity and energy reduction techniques for HEVC DCT and inverse DCT/DST. We designed high performance and low energy hardware for HEVC DCT and inverse DCT/DST including the proposed techniques. VII We quantified computation reductions achieved and video quality loss caused by the proposed algorithms and techniques. We implemented the proposed hardware architectures in Verilog HDL. We mapped the Verilog RTL codes to Xilinx Virtex 6 and Xilinx ZYNQ FPGAs, and estimated their power consumptions using Xilinx XPower Analyzer tool. The proposed algorithms and techniques significantly reduced the power and energy consumptions of these FPGA implementations in some cases with no PSNR loss and in some cases with very small PSNR loss

Automatic Performance Optimization of Stencil Codes

A widely used class of codes are stencil codes. Their general structure is very simple: data points in a large grid are repeatedly recomputed from neighboring values. This predefined neighborhood is the so-called stencil. Despite their very simple structure, stencil codes are hard to optimize since only few computations are performed while a comparatively large number of values have to be accessed, i.e., stencil codes usually have a very low computational intensity. Moreover, the set of optimizations and their parameters also depend on the hardware on which the code is executed. To cut a long story short, current production compilers are not able to fully optimize this class of codes and optimizing each application by hand is not practical. As a remedy, we propose a set of optimizations and describe how they can be applied automatically by a code generator for the domain of stencil codes. A combination of a space and time tiling is able to increase the data locality, which significantly reduces the memory-bandwidth requirements: a standard three-dimensional 7-point Jacobi stencil can be accelerated by a factor of 3. This optimization can target basically any stencil code, while others are more specialized. E.g., support for arbitrary linear data layout transformations is especially beneficial for colored kernels, such as a Red-Black Gauss-Seidel smoother. On the one hand, an optimized data layout for such kernels reduces the bandwidth requirements while, on the other hand, it simplifies an explicit vectorization. Other noticeable optimizations described in detail are redundancy elimination techniques to eliminate common subexpressions both in a sequence of statements and across loop boundaries, arithmetic simplifications and normalizations, and the vectorization mentioned previously. In combination, these optimizations are able to increase the performance not only of the model problem given by Poisson’s equation, but also of real-world applications: an optical flow simulation and the simulation of a non-isothermal and non-Newtonian fluid flow

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Efficient Evaluation of Data-intensive Batch-queries in Open Simulation Laboratories

Better instruments, faster and bigger supercomputers and easier collaboration and sharing of data in the sciences have introduced the need to manage increasingly large datasets. Advances in high-performance computing (HPC) have empowered many science disciplines' computational branches. However, many scientists lack access to HPC facilities or the necessary sophistication to develop and run HPC codes. The benefits of testing new theories and experimenting with large numerical simulations have thus been restricted to a few top users. In this dissertation, I describe the ``remote immersive analysis" approach to computational science and present new techniques and methods for the efficient evaluation of scientific analysis tasks in analysis cluster environments. I will discuss several techniques developed for the efficient evaluation of data-intensive batch-queries in large numerical simulation databases. An I/O streaming method for the evaluation of decomposable kernel computations utilizes partial-sums to evaluate a batch query by performing a single sequential pass over the data. Spatial filtering computations, which use a box filter, share not only data, but also computation and can be evaluated over an intermediate summed volumes dataset derived from the original data. This is more efficient for certain workloads even when the intermediate dataset is computed dynamically. Threshold queries have immense data requirements and potentially operate over entire time-steps of the simulation. An efficient and scalable data-parallel approach evaluates threshold queries of fields derived from the raw simulation data and stores their results in an application-aware semantic cache for fast subsequent retrieval. Finally, synchronization at a mediator, task parallel and data-parallel approaches for the evaluation of particle tracking queries are compared and examined. These techniques are developed, deployed and evaluated in the Johns Hopkins Turbulence Databases (JHTDB), an open simulation laboratory for turbulence research. The JHTDB stores the output of world-class numerical simulations of turbulence and provides public access to and means to explore their complete space-time history. The techniques discussed implement core scientific analysis routines and significantly increase the utility of the service. Additionally, they improve the performance of these routines by up-to an order of magnitude or more when compared with direct implementations or implementations adapted from the simulation code