Performance engineering for HEVC transform and quantization kernel on GPUs

Continuous growth of video traffic and video services, especially in the field of high resolution and high-quality video content, places heavy demands on video coding and its implementations. High Efficiency Video Coding (HEVC) standard doubles the compression efficiency of its predecessor H.264/AVC at the cost of high computational complexity. To address those computing issues high-performance video processing takes advantage of heterogeneous multiprocessor platforms. In this paper, we present a highly performance-optimized HEVC transform and quantization kernel with all-zero-block (AZB) identification designed for execution on a Graphics Processor Unit (GPU). Performance optimization strategy involved all three aspects of parallel design, exposing as much of the application’s intrinsic parallelism as possible, exploitation of high throughput memory and efficient instruction usage. It combines efficient mapping of transform blocks to thread-blocks and efficient vectorized access patterns to shared memory for all transform sizes supported in the standard. Two different GPUs of the same architecture were used to evaluate proposed implementation. Achieved processing times are 6.03 and 23.94 ms for DCI 4K and 8K Full Format, respectively. Speedup factors compared to CPU, cuBLAS and AVX2 implementations are up to 80, 19 and 4 times respectively. Proposed implementation outperforms previous work 1.22 times

Efficient execution of Java programs on GPU

Dissertação de mestrado em Informatics EngineeringWith the overwhelming increase of demand of computational power made by fields as Big Data, Deep Machine learning and Image processing the Graphics Processing Units (GPUs) has been seen as a valuable tool to compute the main workload involved. Nonetheless, these solutions have limited support for object-oriented languages that often require manual memory handling which is an obstacle to bringing together the large community of object oriented programmers and the high-performance computing field. In this master thesis, different memory optimizations and their impacts were studied in a GPU Java context using Aparapi. These include solutions for different identifiable bottlenecks of commonly used kernels exploiting its full capabilities by studying the GPU hardware and current techniques available. These results were set against common used C/OpenCL benchmarks and respective optimizations proving, that high-level languages can be a solution to high-performance software demand.Com o aumento de poder computacional requisitado por campos como Big Data, Deep Machine Learning e Processamento de Imagens, as unidades de processamento gráfico (GPUs) tem sido vistas como uma ferramenta valiosa para executar a principal carga de trabalho envolvida. No entanto, esta solução tem suporte limitado para linguagens orientadas a objetos. Frequentemente estas requerem manipulação manual de memória, o que é um obstáculo para reunir a grande comunidade de programadores orientados a objetos e o campo da computação de alto desempenho. Nesta dissertação de mestrado, diferentes otimizações de memória e os seus impactos foram estudados utilizando Aparapi. As otimizações estudadas pretendem solucionar bottle-necks identificáveis em kernels frequentemente utilizados. Os resultados obtidos foram comparados com benchmarks C / OpenCL populares e as suas respectivas otimizações, provando que as linguagens de alto nível podem ser uma solução para programas que requerem computação de alto desempenho

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Autotuning of performance-relevant source-code parameters allows to automatically tune applications without hard coding optimizations and thus helps with keeping the performance portable. In this paper, we introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA. Using our Kernel Tuning Toolkit, we show that with autotuning most of the kernels reach near-peak performance on various GPUs and outperform baseline implementations on CPUs and Xeon Phis. Our evaluation also demonstrates that autotuning is key to performance portability. In addition to offline tuning, we also introduce dynamic autotuning of code optimization parameters during application runtime. With dynamic tuning, the Kernel Tuning Toolkit enables applications to re-tune performance-critical kernels at runtime whenever needed, for example, when input data changes. Although it is generally believed that autotuning spaces tend to be too large to be searched during application runtime, we show that it is not necessarily the case when tuning spaces are designed rationally. Many of our kernels reach near peak-performance with moderately sized tuning spaces that can be searched at runtime with acceptable overhead. Finally we demonstrate, how dynamic performance tuning can be integrated into a real-world application from cryo-electron microscopy domain

Aceleración de algoritmos de procesamiento de imágenes para el análisis de partículas individuales con microscopia electrónica

Tesis Doctoral inédita cotutelada por la Masaryk University (República Checa) y la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 24-10-2022Cryogenic Electron Microscopy (Cryo-EM) is a vital field in current structural biology. Unlike X-ray crystallography and Nuclear Magnetic Resonance, it can be used to analyze membrane proteins and other samples with overlapping spectral peaks. However, one of the significant limitations of Cryo-EM is the computational complexity. Modern electron microscopes can produce terabytes of data per single session, from which hundreds of thousands of particles must be extracted and processed to obtain a near-atomic resolution of the original sample. Many existing software solutions use high-Performance Computing (HPC) techniques to bring these computations to the realm of practical usability. The common approach to acceleration is parallelization of the processing, but in praxis, we face many complications, such as problem decomposition, data distribution, load scheduling, balancing, and synchronization. Utilization of various accelerators further complicates the situation, as heterogeneous hardware brings additional caveats, for example, limited portability, under-utilization due to synchronization, and sub-optimal code performance due to missing specialization. This dissertation, structured as a compendium of articles, aims to improve the algorithms used in Cryo-EM, esp. the SPA (Single Particle Analysis). We focus on the single-node performance optimizations, using the techniques either available or developed in the HPC field, such as heterogeneous computing or autotuning, which potentially needs the formulation of novel algorithms. The secondary goal of the dissertation is to identify the limitations of state-of-the-art HPC techniques. Since the Cryo-EM pipeline consists of multiple distinct steps targetting different types of data, there is no single bottleneck to be solved. As such, the presented articles show a holistic approach to performance optimization. First, we give details on the GPU acceleration of the specific programs. The achieved speedup is due to the higher performance of the GPU, adjustments of the original algorithm to it, and application of the novel algorithms. More specifically, we provide implementation details of programs for movie alignment, 2D classification, and 3D reconstruction that have been sped up by order of magnitude compared to their original multi-CPU implementation or sufficiently the be used on-the-fly. In addition to these three programs, multiple other programs from an actively used, open-source software package XMIPP have been accelerated and improved. Second, we discuss our contribution to HPC in the form of autotuning. Autotuning is the ability of software to adapt to a changing environment, i.e., input or executing hardware. Towards that goal, we present cuFFTAdvisor, a tool that proposes and, through autotuning, finds the best configuration of the cuFFT library for given constraints of input size and plan settings. We also introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA, together with the introduction of complex dynamic autotuning to the KTT tool. Third, we propose an image processing framework Umpalumpa, which combines a task-based runtime system, data-centric architecture, and dynamic autotuning. The proposed framework allows for writing complex workflows which automatically use available HW resources and adjust to different HW and data but at the same time are easy to maintainThe project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660021. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 71367

Techniques of design optimisation for algorithms implemented in software

The overarching objective of this thesis was to develop tools for parallelising, optimising, and implementing algorithms on parallel architectures, in particular General Purpose Graphics Processors (GPGPUs). Two projects were chosen from different application areas in which GPGPUs are used: a defence application involving image compression, and a modelling application in bioinformatics (computational immunology). Each project had its own specific objectives, as well as supporting the overall research goal. The defence / image compression project was carried out in collaboration with the Jet Propulsion Laboratories. The specific questions were: to what extent an algorithm designed for bit-serial for the lossless compression of hyperspectral images on-board unmanned vehicles (UAVs) in hardware could be parallelised, whether GPGPUs could be used to implement that algorithm, and whether a software implementation with or without GPGPU acceleration could match the throughput of a dedicated hardware (FPGA) implementation. The dependencies within the algorithm were analysed, and the algorithm parallelised. The algorithm was implemented in software for GPGPU, and optimised. During the optimisation process, profiling revealed less than optimal device utilisation, but no further optimisations resulted in an improvement in speed. The design had hit a local-maximum of performance. Analysis of the arithmetic intensity and data-flow exposed flaws in the standard optimisation metric of kernel occupancy used for GPU optimisation. Redesigning the implementation with revised criteria (fused kernels, lower occupancy, and greater data locality) led to a new implementation with 10x higher throughput. GPGPUs were shown to be viable for on-board implementation of the CCSDS lossless hyperspectral image compression algorithm, exceeding the performance of the hardware reference implementation, and providing sufficient throughput for the next generation of image sensor as well. The second project was carried out in collaboration with biologists at the University of Arizona and involved modelling a complex biological system – VDJ recombination involved in the formation of T-cell receptors (TCRs). Generation of immune receptors (T cell receptor and antibodies) by VDJ recombination is an enormously complex process, which can theoretically synthesize greater than 1018 variants. Originally thought to be a random process, the underlying mechanisms clearly have a non-random nature that preferentially creates a small subset of immune receptors in many individuals. Understanding this bias is a longstanding problem in the field of immunology. Modelling the process of VDJ recombination to determine the number of ways each immune receptor can be synthesized, previously thought to be untenable, is a key first step in determining how this special population is made. The computational tools developed in this thesis have allowed immunologists for the first time to comprehensively test and invalidate a longstanding theory (convergent recombination) for how this special population is created, while generating the data needed to develop novel hypothesis