Shader optimization and specialization

In the field of real-time graphics for computer games, performance has a significant effect on the player’s enjoyment and immersion. Graphics processing units (GPUs) are hardware accelerators that run small parallelized shader programs to speed up computationally expensive rendering calculations. This thesis examines optimizing shader programs and explores ways in which data patterns on both the CPU and GPU can be analyzed to automatically speed up rendering in games. Initially, the effect of traditional compiler optimizations on shader source-code was explored. Techniques such as loop unrolling or arithmetic reassociation provided speed-ups on several devices, but different GPU hardware responded differently to each set of optimizations. Analyzing execution traces from numerous popular PC games revealed that much of the data passed from CPU-based API calls to GPU-based shaders is either unused, or remains constant. A system was developed to capture this constant data and fold it into the shaders’ source-code. Re-running the game’s rendering code using these specialized shader variants resulted in performance improvements in several commercial games without impacting their visual quality

CGiS : high-level data-parallel GPU programming

In the last few years, PC technology underwent a paradigm shift. The current trend leads aways from raising sequential performance to enhancing the available parallelism. The rapid performance increase of Graphics Processing Units (GPUs) is a part of this trend. However, it is difficult to harness the computational potential because for the longest time GPUs could be directed only through graphics APIs and in low-level code. The language CGiS has been developed to remedy this situation. CGiS is a data-parallel programming language, which offers a high-level abstraction of GPUs, letting programmers use GPUs as co-processors for massively parallel algorithms. This work presents the language and the compiler for CGiS in the context of general purpose programming on GPUs (GPGPU).Seit einigen Jahren zeichnet sich bei handelsüblichen PCs ein Trend weg von der Erhöhung der sequentiellen Leistung hin zur Parallelverarbeitung ab. Ein Bestandteil dieses Trends ist die rasche Leistungsentwicklung der Grafikkarten (GPUs), deren Rechenleistung die aktueller CPUs mittlerweile übertrifft. Es ist jedoch schwierig, diese Leistung auch abzurufen, da diese Geräte lange Zeit nur hardwarenah und über Grafik-APIs ansteuerbar waren. Um dies zu ändern, ist CGiS entwickelt worden, eine datenparallele Programmiersprache, die die GPUs abstrahiert und ihre Benutzung als Co-Prozessoren für massiv-datenparallele Algorithmen ermöglicht. Diese Arbeit stellt die Sprache und den Compiler im Kontext dieser Entwicklung vor

Indexed dependence metadata and its applications in software performance optimisation

To achieve continued performance improvements, modern microprocessor design is tending to concentrate an increasing proportion of hardware on computation units with less automatic management of data movement and extraction of parallelism. As a result, architectures increasingly include multiple computation cores and complicated, software-managed memory hierarchies. Compilers have difficulty characterizing the behaviour of a kernel in a general enough manner to enable automatic generation of efficient code in any but the most straightforward of cases. We propose the concept of indexed dependence metadata to improve application development and mapping onto such architectures. The metadata represent both the iteration space of a kernel and the mapping of that iteration space from a given index to the set of data elements that iteration might use: thus the dependence metadata is indexed by the kernel’s iteration space. This explicit mapping allows the compiler or runtime to optimise the program more efficiently, and improves the program structure for the developer. We argue that this form of explicit interface specification reduces the need for premature, architecture-specific optimisation. It improves program portability, supports intercomponent optimisation and enables generation of efficient data movement code. We offer the following contributions: an introduction to the concept of indexed dependence metadata as a generalisation of stream programming, a demonstration of its advantages in a component programming system, the decoupled access/execute model for C++ programs, and how indexed dependence metadata might be used to improve the programming model for GPU-based designs. Our experimental results with prototype implementations show that indexed dependence metadata supports automatic synthesis of double-buffered data movement for the Cell processor and enables aggressive loop fusion optimisations in image processing, linear algebra and multigrid application case studies

PC-grade parallel processing and hardware acceleration for large-scale data analysis

Arguably, modern graphics processing units (GPU) are the first commodity, and desktop parallel processor. Although GPU programming was originated from the interactive rendering in graphical applications such as computer games, researchers in the field of general purpose computation on GPU (GPGPU) are showing that the power, ubiquity and low cost of GPUs makes them an ideal alternative platform for high-performance computing. This has resulted in the extensive exploration in using the GPU to accelerate general-purpose computations in many engineering and mathematical domains outside of graphics. However, limited to the development complexity caused by the graphics-oriented concepts and development tools for GPU-programming, GPGPU has mainly been discussed in the academic domain so far and has not yet fully fulfilled its promises in the real world. This thesis aims at exploiting GPGPU in the practical engineering domain and presented a novel contribution to GPGPU-driven linear time invariant (LTI) systems that are employed by the signal processing techniques in stylus-based or optical-based surface metrology and data processing. The core contributions that have been achieved in this project can be summarized as follow. Firstly, a thorough survey of the state-of-the-art of GPGPU applications and their development approaches has been carried out in this thesis. In addition, the category of parallel architecture pattern that the GPGPU belongs to has been specified, which formed the foundation of the GPGPU programming framework design in the thesis. Following this specification, a GPGPU programming framework is deduced as a general guideline to the various GPGPU programming models that are applied to a large diversity of algorithms in scientific computing and engineering applications. Considering the evolution of GPU’s hardware architecture, the proposed frameworks cover through the transition of graphics-originated concepts for GPGPU programming based on legacy GPUs and the abstraction of stream processing pattern represented by the compute unified device architecture (CUDA) in which GPU is considered as not only a graphics device but a streaming coprocessor of CPU. Secondly, the proposed GPGPU programming framework are applied to the practical engineering applications, namely, the surface metrological data processing and image processing, to generate the programming models that aim to carry out parallel computing for the corresponding algorithms. The acceleration performance of these models are evaluated in terms of the speed-up factor and the data accuracy, which enabled the generation of quantifiable benchmarks for evaluating consumer-grade parallel processors. It shows that the GPGPU applications outperform the CPU solutions by up to 20 times without significant loss of data accuracy and any noticeable increase in source code complexity, which further validates the effectiveness of the proposed GPGPU general programming framework. Thirdly, this thesis devised methods for carrying out result visualization directly on GPU by storing processed data in local GPU memory through making use of GPU’s rendering device features to achieve realtime interactions. The algorithms employed in this thesis included various filtering techniques, discrete wavelet transform, and the fast Fourier Transform which cover the common operations implemented in most LTI systems in spatial and frequency domains. Considering the employed GPUs’ hardware designs, especially the structure of the rendering pipelines, and the characteristics of the algorithms, the series of proposed GPGPU programming models have proven its feasibility, practicality, and robustness in real engineering applications. The developed GPGPU programming framework as well as the programming models are anticipated to be adaptable for future consumer-level computing devices and other computational demanding applications. In addition, it is envisaged that the devised principles and methods in the framework design are likely to have significant benefits outside the sphere of surface metrology.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mobile ray-tracing

Dissertação de mestrado em Computer ScienceThe technological advances and the massification of information technologies have allowed a huge and positive proliferation of the number of libraries and APIs. This large offer has made life easier for programmers in general, because they easily find a library, free or commercial, that helps them solve the daily challenges they have at hand. One area of information technology where libraries are critical is in Computer Graphics, due to the wide range of rendering techniques it offers. One of these techniques is ray tracing. Ray tracing allows to simulate natural electromagnetic phenomena such as the path of light and mechanical phenomena such as the propagation of sound. Similarly, it also allows to simulate technologies developed by men, like Wi-Fi networks. These simulations can have a spectacular realism and accuracy, at the expense of a very high computational cost. The constant evolution of technology allowed to leverage and massify new areas, such as mobile devices. Devices today are increasingly faster, replacing and often complementing tasks that were previously performed only on computers or on dedicated hardware. However, the number of image rendering libraries available for mobile devices is still very scarce, and no ray tracing based image rendering library has been able to assert itself on these devices. This dissertation aims to explore the possibilities and limitations of using mobile devices to execute rendering algorithms that use ray tracing, such as progressive path tracing. Its main goal is to provide a rendering library for mobile devices based on ray tracing.Os avanços tecnológicos e a massificação das tecnologias de informação permitiu uma enorme e positiva proliferação do número de bibliotecas e APIs. Esta maior oferta permitiu facilitar a vida dos programadores em geral, porque facilmente encontram uma biblioteca, gratuita ou comercial, que os ajudam a resolver os desafios diários que têm em mãos. Uma área das tecnologias de informação onde as bibliotecas são fundamentais é na Computação Gráfica, devido à panóplia de métodos de renderização que oferece. Um destes métodos é o ray tracing. O ray tracing permite simular fenómenos eletromagnéticos naturais como os percursos da luz e fenómenos mecânicos como a propagação do som. Da mesma forma também permite simular tecnologias desenvolvidas pelo homem, como por exemplo redes Wi-Fi. Estas simulações podem ter um realismo e precisão impressionantes, porém têm um custo computacional muito elevado. A constante evolução da tecnologia permitiu alavancar e massificar novas áreas, como os dispositivos móveis. Os dispositivos são hoje cada vez mais rápidos e cada vez mais substituem e/ou complementam tarefas que anteriormente eram apenas realizadas em computadores ou em hardware dedicado. Porém, o número de bibliotecas para renderização de imagens disponíveis para dispositivos móveis é ainda muito reduzido e nenhuma biblioteca de renderização de imagens baseada em ray tracing conseguiu afirmar-se nestes dispositivos. Esta dissertação tem como objetivo explorar possibilidades e limitações da utilização de dispositivos móveis para a execução de algoritmos de renderização que utilizem ray tracing, como por exemplo, o path tracing progressivo. O objetivo principal é disponibilizar uma biblioteca de renderização para dispositivos móveis baseada em ray tracing

Application of multi-core and cluster computing to the Transmission Line Matrix method

The Transmission Line Matrix (TLM) method is an existing and established mathematical method for conducting computational electromagnetic (CEM) simulations. TLM models Maxwell s equations by discretising the contiguous nature of an environment and its contents into individual small-scale elements and it is a computationally intensive process. This thesis focusses on parallel processing optimisations to the TLM method when considering the opposing ends of the contemporary computing hardware spectrum, namely large-scale computing systems versus small-scale mobile computing devices. Theoretical aspects covered in this thesis are: The historical development and derivation of the TLM method. A discrete random variable (DRV) for rain-drop diameter,allowing generation of a rain-field with raindrops adhering to a Gaussian size distribution, as a case study for a 3-D TLM implementation. Investigations into parallel computing strategies for accelerating TLM on large and small-scale computing platforms. Implementation aspects covered in this thesis are: A script for modelling rain-fields using free-to-use modelling software. The first known implementation of 2-D TLM on mobile computing devices. A 3-D TLM implementation designed for simulating the effects of rain-fields on extremely high frequency (EHF) band signals. By optimising both TLM solver implementations for their respective platforms, new opportunities present themselves. Rain-field simulations containing individual rain-drop geometry can be simulated, which was previously impractical due to the lengthy computation times required. Also, computationally time-intensive methods such as TLM were previously impractical on mobile computing devices. Contemporary hardware features on these devices now provide the opportunity for CEM simulations at speeds that are acceptable to end users, as well as providing a new avenue for educating relevant user cohorts via dynamic presentations of EM phenomena

GPU PERFORMANCE MODELLING AND OPTIMIZATION

Ph.DNUS-TU/E JOINT PH.D

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included

Proceedings of the 7th Conference on Real Numbers and Computers (RNC'7)

These are the proceedings of RNC7