Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Exploiting frame coherence in real-time rendering for energy-efficient GPUs

The computation capabilities of mobile GPUs have greatly evolved in the last generations, allowing real-time rendering of realistic scenes. However, the desire for processing complex environments clashes with the battery-operated nature of smartphones, for which users expect long operating times per charge and a low-enough temperature to comfortably hold them. Consequently, improving the energy-efficiency of mobile GPUs is paramount to fulfill both performance and low-power goals. The work of the processors from within the GPU and their accesses to off-chip memory are the main sources of energy consumption in graphics workloads. Yet most of this energy is spent in redundant computations, as the frame rate required to produce animations results in a sequence of extremely similar images. The goal of this thesis is to improve the energy-efficiency of mobile GPUs by designing micro-architectural mechanisms that leverage frame coherence in order to reduce the redundant computations and memory accesses inherent in graphics applications. First, we focus on reducing redundant color computations. Mobile GPUs typically employ an architecture called Tile-Based Rendering, in which the screen is divided into tiles that are independently rendered in on-chip buffers. It is common that more than 80% of the tiles produce exactly the same output between consecutive frames. We propose Rendering Elimination (RE), a mechanism that accurately determines such occurrences by computing and storing signatures of the inputs of all the tiles in a frame. If the signatures of a tile across consecutive frames are the same, the colors computed in the preceding frame are reused, saving all computations and memory accesses associated to the rendering of the tile. We show that RE vastly outperforms related schemes found in the literature, achieving a reduction of energy consumption of 37% and execution time of 33% with minimal overheads. Next, we focus on reducing redundant computations of fragments that will eventually not be visible. In real-time rendering, objects are processed in the order they are submitted to the GPU, which usually causes that the results of previously-computed objects are overwritten by new objects that turn occlude them. Consequently, whether or not a particular object will be occluded is not known until the entire scene has been processed. Based on the fact that visibility tends to remain constant across consecutive frames, we propose Early Visibility Resolution (EVR), a mechanism that predicts visibility based on information obtained in the preceding frame. EVR first computes and stores the depth of the farthest visible point after rendering each tile. Whenever a tile is rendered in the following frame, primitives that are farther from the observer than the stored depth are predicted to be occluded, and processed after the ones predicted to be visible. Additionally, this visibility prediction scheme is used to improve Rendering Elimination’s equal tile detection capabilities by not adding primitives predicted to be occluded in the signature. With minor hardware costs, EVR is shown to provide a reduction of energy consumption of 43% and execution time of 39%. Finally, we focus on reducing computations in tiles with low spatial frequencies. GPUs produce pixel colors by sampling triangles once per pixel and performing computations on each sampling location. However, most screen regions do not include sufficient detail to require high sampling rates, leading to a significant amount of energy wasted computing the same color for neighboring pixels. Given that spatial frequencies are maintained across frames, we propose Dynamic Sampling Rate, a mechanism that analyzes the spatial frequencies of tiles and determines the best sampling rate for them, which is applied in the following frame. Results show that Dynamic Sampling Rate significantly reduces processor activity, yielding energy savings of 40% and execution time reductions of 35%.La capacitat de càlcul de les GPU mòbils ha augmentat en gran mesura en les darreres generacions, permetent el renderitzat de paisatges complexos en temps real. Nogensmenys, el desig de processar escenes cada vegada més realistes xoca amb el fet que aquests dispositius funcionen amb bateries, i els usuaris n’esperen llargues durades i una temperatura prou baixa com per a ser agafats còmodament. En conseqüència, millorar l’eficiència energètica de les GPU mòbils és essencial per a aconseguir els objectius de rendiment i baix consum. Els processadors de la GPU i els seus accessos a memòria són els principals consumidors d’energia en càrregues gràfiques, però molt d’aquest consum és malbaratat en càlculs redundants, ja que les animacions produïdes s¿aconsegueixen renderitzant una seqüència d’imatges molt similars. L’objectiu d’aquesta tesi és millorar l’eficiència energètica de les GPU mòbils mitjançant el disseny de mecanismes microarquitectònics que aprofitin la coherència entre imatges per a reduir els càlculs i accessos redundants inherents a les aplicacions gràfiques. Primerament, ens centrem en reduir càlculs redundants de colors. A les GPU mòbils, sovint s'empra una arquitectura anomenada Tile-Based Rendering, en què la pantalla es divideix en regions que es processen independentment dins del xip. És habitual que més del 80% de les regions de pantalla produeixin els mateixos colors entre imatges consecutives. Proposem Rendering Elimination (RE), un mecanisme que determina acuradament aquests casos computant una signatura de les entrades de totes les regions. Si les signatures de dues imatges són iguals, es reutilitzen els colors calculats a la imatge anterior, el que estalvia tots els càlculs i accessos a memòria de la regió. RE supera àmpliament propostes relacionades de la literatura, aconseguint una reducció del consum energètic del 37% i del temps d’execució del 33%. Seguidament, ens centrem en reduir càlculs redundants en fragments que eventualment no seran visibles. En aplicacions gràfiques, els objectes es processen en l’ordre en què son enviats a la GPU, el que sovint causa que resultats ja processats siguin sobreescrits per nous objectes que els oclouen. Per tant, no se sap si un objecte serà visible o no fins que tota l’escena ha estat processada. Fonamentats en el fet que la visibilitat tendeix a ser constant entre imatges, proposem Early Visibility Resolution (EVR), un mecanisme que prediu la visibilitat basat en informació obtinguda a la imatge anterior. EVR computa i emmagatzema la profunditat del punt visible més llunyà després de processar cada regió de pantalla. Quan es processa una regió a la imatge següent, es prediu que les primitives més llunyanes a el punt guardat seran ocloses i es processen després de les que es prediuen que seran visibles. Addicionalment, aquest esquema de predicció s’empra en millorar la detecció de regions redundants de RE al no afegir les primitives que es prediu que seran ocloses a les signatures. Amb un cost de maquinari mínim, EVR aconsegueix una millora del consum energètic del 43% i del temps d’execució del 39%. Finalment, ens centrem a reduir càlculs en regions de pantalla amb poca freqüència espacial. Les GPU actuals produeixen colors mostrejant els triangles una vegada per cada píxel i fent càlculs a cada localització mostrejada. Però la majoria de regions no tenen suficient detall per a necessitar altes freqüències de mostreig, el que implica un malbaratament d’energia en el càlcul del mateix color en píxels adjacents. Com les freqüències tendeixen a mantenir-se en el temps, proposem Dynamic Sampling Rate (DSR)¸ un mecanisme que analitza les freqüències de les regions una vegada han estat renderitzades i en determina la menor freqüència de mostreig a la que es poden processar, que s’aplica a la següent imatge..

Exploiting frame coherence in real-time rendering for energy-efficient GPUs

The computation capabilities of mobile GPUs have greatly evolved in the last generations, allowing real-time rendering of realistic scenes. However, the desire for processing complex environments clashes with the battery-operated nature of smartphones, for which users expect long operating times per charge and a low-enough temperature to comfortably hold them. Consequently, improving the energy-efficiency of mobile GPUs is paramount to fulfill both performance and low-power goals. The work of the processors from within the GPU and their accesses to off-chip memory are the main sources of energy consumption in graphics workloads. Yet most of this energy is spent in redundant computations, as the frame rate required to produce animations results in a sequence of extremely similar images. The goal of this thesis is to improve the energy-efficiency of mobile GPUs by designing micro-architectural mechanisms that leverage frame coherence in order to reduce the redundant computations and memory accesses inherent in graphics applications. First, we focus on reducing redundant color computations. Mobile GPUs typically employ an architecture called Tile-Based Rendering, in which the screen is divided into tiles that are independently rendered in on-chip buffers. It is common that more than 80% of the tiles produce exactly the same output between consecutive frames. We propose Rendering Elimination (RE), a mechanism that accurately determines such occurrences by computing and storing signatures of the inputs of all the tiles in a frame. If the signatures of a tile across consecutive frames are the same, the colors computed in the preceding frame are reused, saving all computations and memory accesses associated to the rendering of the tile. We show that RE vastly outperforms related schemes found in the literature, achieving a reduction of energy consumption of 37% and execution time of 33% with minimal overheads. Next, we focus on reducing redundant computations of fragments that will eventually not be visible. In real-time rendering, objects are processed in the order they are submitted to the GPU, which usually causes that the results of previously-computed objects are overwritten by new objects that turn occlude them. Consequently, whether or not a particular object will be occluded is not known until the entire scene has been processed. Based on the fact that visibility tends to remain constant across consecutive frames, we propose Early Visibility Resolution (EVR), a mechanism that predicts visibility based on information obtained in the preceding frame. EVR first computes and stores the depth of the farthest visible point after rendering each tile. Whenever a tile is rendered in the following frame, primitives that are farther from the observer than the stored depth are predicted to be occluded, and processed after the ones predicted to be visible. Additionally, this visibility prediction scheme is used to improve Rendering Elimination’s equal tile detection capabilities by not adding primitives predicted to be occluded in the signature. With minor hardware costs, EVR is shown to provide a reduction of energy consumption of 43% and execution time of 39%. Finally, we focus on reducing computations in tiles with low spatial frequencies. GPUs produce pixel colors by sampling triangles once per pixel and performing computations on each sampling location. However, most screen regions do not include sufficient detail to require high sampling rates, leading to a significant amount of energy wasted computing the same color for neighboring pixels. Given that spatial frequencies are maintained across frames, we propose Dynamic Sampling Rate, a mechanism that analyzes the spatial frequencies of tiles and determines the best sampling rate for them, which is applied in the following frame. Results show that Dynamic Sampling Rate significantly reduces processor activity, yielding energy savings of 40% and execution time reductions of 35%.La capacitat de càlcul de les GPU mòbils ha augmentat en gran mesura en les darreres generacions, permetent el renderitzat de paisatges complexos en temps real. Nogensmenys, el desig de processar escenes cada vegada més realistes xoca amb el fet que aquests dispositius funcionen amb bateries, i els usuaris n’esperen llargues durades i una temperatura prou baixa com per a ser agafats còmodament. En conseqüència, millorar l’eficiència energètica de les GPU mòbils és essencial per a aconseguir els objectius de rendiment i baix consum. Els processadors de la GPU i els seus accessos a memòria són els principals consumidors d’energia en càrregues gràfiques, però molt d’aquest consum és malbaratat en càlculs redundants, ja que les animacions produïdes s¿aconsegueixen renderitzant una seqüència d’imatges molt similars. L’objectiu d’aquesta tesi és millorar l’eficiència energètica de les GPU mòbils mitjançant el disseny de mecanismes microarquitectònics que aprofitin la coherència entre imatges per a reduir els càlculs i accessos redundants inherents a les aplicacions gràfiques. Primerament, ens centrem en reduir càlculs redundants de colors. A les GPU mòbils, sovint s'empra una arquitectura anomenada Tile-Based Rendering, en què la pantalla es divideix en regions que es processen independentment dins del xip. És habitual que més del 80% de les regions de pantalla produeixin els mateixos colors entre imatges consecutives. Proposem Rendering Elimination (RE), un mecanisme que determina acuradament aquests casos computant una signatura de les entrades de totes les regions. Si les signatures de dues imatges són iguals, es reutilitzen els colors calculats a la imatge anterior, el que estalvia tots els càlculs i accessos a memòria de la regió. RE supera àmpliament propostes relacionades de la literatura, aconseguint una reducció del consum energètic del 37% i del temps d’execució del 33%. Seguidament, ens centrem en reduir càlculs redundants en fragments que eventualment no seran visibles. En aplicacions gràfiques, els objectes es processen en l’ordre en què son enviats a la GPU, el que sovint causa que resultats ja processats siguin sobreescrits per nous objectes que els oclouen. Per tant, no se sap si un objecte serà visible o no fins que tota l’escena ha estat processada. Fonamentats en el fet que la visibilitat tendeix a ser constant entre imatges, proposem Early Visibility Resolution (EVR), un mecanisme que prediu la visibilitat basat en informació obtinguda a la imatge anterior. EVR computa i emmagatzema la profunditat del punt visible més llunyà després de processar cada regió de pantalla. Quan es processa una regió a la imatge següent, es prediu que les primitives més llunyanes a el punt guardat seran ocloses i es processen després de les que es prediuen que seran visibles. Addicionalment, aquest esquema de predicció s’empra en millorar la detecció de regions redundants de RE al no afegir les primitives que es prediu que seran ocloses a les signatures. Amb un cost de maquinari mínim, EVR aconsegueix una millora del consum energètic del 43% i del temps d’execució del 39%. Finalment, ens centrem a reduir càlculs en regions de pantalla amb poca freqüència espacial. Les GPU actuals produeixen colors mostrejant els triangles una vegada per cada píxel i fent càlculs a cada localització mostrejada. Però la majoria de regions no tenen suficient detall per a necessitar altes freqüències de mostreig, el que implica un malbaratament d’energia en el càlcul del mateix color en píxels adjacents. Com les freqüències tendeixen a mantenir-se en el temps, proposem Dynamic Sampling Rate (DSR)¸ un mecanisme que analitza les freqüències de les regions una vegada han estat renderitzades i en determina la menor freqüència de mostreig a la que es poden processar, que s’aplica a la següent imatge...Postprint (published version

Real-Time Adaptive Radiometric Compensation

Recent radiometric compensation techniques make it possible to project images onto colored and textured surfaces. This is realized with projector-camera systems by scanning the projection surface on a per-pixel basis. With the captured information, a compensation image is calculated that neutralizes geometric distortions and color blending caused by the underlying surface. As a result, the brightness and the contrast of the input image is reduced compared to a conventional projection onto a white canvas. If the input image is not manipulated in its intensities, the compensation image can contain values that are outside the dynamic range of the projector. They will lead to clipping errors and to visible artifacts on the surface. In this article, we present a novel algorithm that dynamically adjusts the content of the input images before radiometric compensation is carried out. This reduces the perceived visual artifacts while simultaneously preserving a maximum of luminance and contrast. The algorithm is implemented entirely on the GPU and is the first of its kind to run in real-time

Perceptual model for adaptive local shading and refresh rate

When the rendering budget is limited by power or time, it is necessary to find the combination of rendering parameters, such as resolution and refresh rate, that could deliver the best quality. Variable-rate shading (VRS), introduced in the last generations of GPUs, enables fine control of the rendering quality, in which each 16×16 image tile can be rendered with a different ratio of shader executions. We take advantage of this capability and propose a new method for adaptive control of local shading and refresh rate. The method analyzes texture content, on-screen velocities, luminance, and effective resolution and suggests the refresh rate and a VRS state map that maximizes the quality of animated content under a limited budget. The method is based on the new content-adaptive metric of judder, aliasing, and blur, which is derived from the psychophysical models of contrast sensitivity. To calibrate and validate the metric, we gather data from literature and also collect new measurements of motion quality under variable shading rates, different velocities of motion, texture content, and display capabilities, such as refresh rate, persistence, and angular resolution. The proposed metric and adaptive shading method is implemented as a game engine plugin. Our experimental validation shows a substantial increase in preference of our method over rendering with a fixed resolution and refresh rate, and an existing motion-adaptive techniqu

Real-time quality visualization of medical models on commodity and mobile devices

This thesis concerns the specific field of visualization of medical models using commodity and mobile devices. Mechanisms for medical imaging acquisition such as MRI, CT, and micro-CT scanners are continuously evolving, up to the point of obtaining volume datasets of large resolutions (> 512^3). As these datasets grow in resolution, its treatment and visualization become more and more expensive due to their computational requirements. For this reason, special techniques such as data pre-processing (filtering, construction of multi-resolution structures, etc.) and sophisticated algorithms have to be introduced in different points of the visualization pipeline to achieve the best visual quality without compromising performance times. The problem of managing big datasets comes from the fact that we have limited computational resources. Not long ago, the only physicians that were rendering volumes were radiologists. Nowadays, the outcome of diagnosis is the data itself, and medical doctors need to render them in commodity PCs (even patients may want to render the data, and the DVDs are commonly accompanied with a DICOM viewer software). Furthermore, with the increasing use of technology in daily clinical tasks, small devices such as mobile phones and tablets can fit the needs of medical doctors in some specific areas. Visualizing diagnosis images of patients becomes more challenging when it comes to using these devices instead of desktop computers, as they generally have more restrictive hardware specifications. The goal of this Ph.D. thesis is the real-time, quality visualization of medium to large medical volume datasets (resolutions >= 512^3 voxels) on mobile phones and commodity devices. To address this problem, we use multiresolution techniques that apply downsampling techniques on the full resolution datasets to produce coarser representations which are easier to handle. We have focused our efforts on the application of Volume Visualization in the clinical practice, so we have a particular interest in creating solutions that require short pre-processing times that quickly provide the specialists with the data outcome, maximize the preservation of features and the visual quality of the final images, achieve high frame rates that allow interactive visualizations, and make efficient use of the computational resources. The contributions achieved during this thesis comprise improvements in several stages of the visualization pipeline. The techniques we propose are located in the stages of multi-resolution generation, transfer function design and the GPU ray casting algorithm itself.Esta tesis se centra en la visualización de modelos médicos de volumen en dispositivos móviles y de bajas prestaciones. Los sistemas médicos de captación tales como escáners MRI, CT y micro-CT, están en constante evolución, hasta el punto de obtener modelos de volumen de gran resolución (> 512^3). A medida que estos datos crecen en resolución, su manejo y visualización se vuelve más y más costoso debido a sus requisitos computacionales. Por este motivo, técnicas especiales como el pre-proceso de datos (filtrado, construcción de estructuras multiresolución, etc.) y algoritmos específicos se tienen que introducir en diferentes puntos de la pipeline de visualización para conseguir la mejor calidad visual posible sin comprometer el rendimiento. El problema que supone manejar grandes volumenes de datos es debido a que tenemos recursos computacionales limitados. Hace no mucho, las únicas personas en el ámbito médico que visualizaban datos de volumen eran los radiólogos. Hoy en día, el resultado de la diagnosis son los datos en sí, y los médicos necesitan renderizar estos datos en PCs de características modestas (incluso los pacientes pueden querer visualizar estos datos, pues los DVDs con los resultados suelen venir acompañados de un visor de imágenes DICOM). Además, con el reciente aumento del uso de las tecnologías en la clínica práctica habitual, dispositivos pequeños como teléfonos móviles o tablets son los más convenientes en algunos casos. La visualización de volumen es más difícil en este tipo de dispositivos que en equipos de sobremesa, pues las limitaciones de su hardware son superiores. El objetivo de esta tesis doctoral es la visualización de calidad en tiempo real de modelos grandes de volumen (resoluciones >= 512^3 voxels) en teléfonos móviles y dispositivos de bajas prestaciones. Para enfrentarnos a este problema, utilizamos técnicas multiresolución que aplican técnicas de reducción de datos a los modelos en resolución original, para así obtener modelos de menor resolución. Hemos centrado nuestros esfuerzos en la aplicación de la visualización de volumen en la práctica clínica, así que tenemos especial interés en diseñar soluciones que requieran cortos tiempos de pre-proceso para que los especialistas tengan rápidamente los resultados a su disposición. También, queremos maximizar la conservación de detalles de interés y la calidad de las imágenes finales, conseguir frame rates altos que faciliten visualizaciones interactivas y que hagan un uso eficiente de los recursos computacionales. Las contribuciones aportadas por esta tesis són mejoras en varias etapas de la pipeline de visualización. Las técnicas que proponemos se situan en las etapas de generación de la estructura multiresolución, el diseño de la función de transferencia y el algoritmo de ray casting en la GPU

Real-time quality visualization of medical models on commodity and mobile devices

This thesis concerns the specific field of visualization of medical models using commodity and mobile devices. Mechanisms for medical imaging acquisition such as MRI, CT, and micro-CT scanners are continuously evolving, up to the point of obtaining volume datasets of large resolutions (> 512^3). As these datasets grow in resolution, its treatment and visualization become more and more expensive due to their computational requirements. For this reason, special techniques such as data pre-processing (filtering, construction of multi-resolution structures, etc.) and sophisticated algorithms have to be introduced in different points of the visualization pipeline to achieve the best visual quality without compromising performance times. The problem of managing big datasets comes from the fact that we have limited computational resources. Not long ago, the only physicians that were rendering volumes were radiologists. Nowadays, the outcome of diagnosis is the data itself, and medical doctors need to render them in commodity PCs (even patients may want to render the data, and the DVDs are commonly accompanied with a DICOM viewer software). Furthermore, with the increasing use of technology in daily clinical tasks, small devices such as mobile phones and tablets can fit the needs of medical doctors in some specific areas. Visualizing diagnosis images of patients becomes more challenging when it comes to using these devices instead of desktop computers, as they generally have more restrictive hardware specifications. The goal of this Ph.D. thesis is the real-time, quality visualization of medium to large medical volume datasets (resolutions >= 512^3 voxels) on mobile phones and commodity devices. To address this problem, we use multiresolution techniques that apply downsampling techniques on the full resolution datasets to produce coarser representations which are easier to handle. We have focused our efforts on the application of Volume Visualization in the clinical practice, so we have a particular interest in creating solutions that require short pre-processing times that quickly provide the specialists with the data outcome, maximize the preservation of features and the visual quality of the final images, achieve high frame rates that allow interactive visualizations, and make efficient use of the computational resources. The contributions achieved during this thesis comprise improvements in several stages of the visualization pipeline. The techniques we propose are located in the stages of multi-resolution generation, transfer function design and the GPU ray casting algorithm itself.Esta tesis se centra en la visualización de modelos médicos de volumen en dispositivos móviles y de bajas prestaciones. Los sistemas médicos de captación tales como escáners MRI, CT y micro-CT, están en constante evolución, hasta el punto de obtener modelos de volumen de gran resolución (> 512^3). A medida que estos datos crecen en resolución, su manejo y visualización se vuelve más y más costoso debido a sus requisitos computacionales. Por este motivo, técnicas especiales como el pre-proceso de datos (filtrado, construcción de estructuras multiresolución, etc.) y algoritmos específicos se tienen que introducir en diferentes puntos de la pipeline de visualización para conseguir la mejor calidad visual posible sin comprometer el rendimiento. El problema que supone manejar grandes volumenes de datos es debido a que tenemos recursos computacionales limitados. Hace no mucho, las únicas personas en el ámbito médico que visualizaban datos de volumen eran los radiólogos. Hoy en día, el resultado de la diagnosis son los datos en sí, y los médicos necesitan renderizar estos datos en PCs de características modestas (incluso los pacientes pueden querer visualizar estos datos, pues los DVDs con los resultados suelen venir acompañados de un visor de imágenes DICOM). Además, con el reciente aumento del uso de las tecnologías en la clínica práctica habitual, dispositivos pequeños como teléfonos móviles o tablets son los más convenientes en algunos casos. La visualización de volumen es más difícil en este tipo de dispositivos que en equipos de sobremesa, pues las limitaciones de su hardware son superiores. El objetivo de esta tesis doctoral es la visualización de calidad en tiempo real de modelos grandes de volumen (resoluciones >= 512^3 voxels) en teléfonos móviles y dispositivos de bajas prestaciones. Para enfrentarnos a este problema, utilizamos técnicas multiresolución que aplican técnicas de reducción de datos a los modelos en resolución original, para así obtener modelos de menor resolución. Hemos centrado nuestros esfuerzos en la aplicación de la visualización de volumen en la práctica clínica, así que tenemos especial interés en diseñar soluciones que requieran cortos tiempos de pre-proceso para que los especialistas tengan rápidamente los resultados a su disposición. También, queremos maximizar la conservación de detalles de interés y la calidad de las imágenes finales, conseguir frame rates altos que faciliten visualizaciones interactivas y que hagan un uso eficiente de los recursos computacionales. Las contribuciones aportadas por esta tesis són mejoras en varias etapas de la pipeline de visualización. Las técnicas que proponemos se situan en las etapas de generación de la estructura multiresolución, el diseño de la función de transferencia y el algoritmo de ray casting en la GPU.Postprint (published version

Improving the energy efficiency of the graphics pipeline by reducing overshading

The most common task of GPUs is to render images in real time. When rendering a 3D scene, a key step is determining which parts of every object are visible in the final image. There are different approaches to solve the visibility problem, the Z-Test being the most common in modern GPUs. A main factor that significantly penalizes the energy efficiency of a GPU, especially in the mobile arena, is the so-called overshading, which happens when a portion of an object is shaded and rendered but finally occluded by another object. This useless work results in a waste of energy, however, the conventional Z-Test only eliminates a fraction of it. In this paper we present a novel microarchitectural technique, the ¿-Test, to drastically reduce overshading on a Tile-Based Rendering (TBR) architecture. The proposed approach leverages frame-to-frame coherence by taking advantage of the costly and valuable calculations made in previous frames. In particular, we propose to reuse information from the Z-Buffer of the previous frame, which is currently discarded. We make the observation that due to the existing frame-to-frame coherence, the Z-Buffer of a frame will have a high similarity in many areas with that of the previous frame. As a result, the proposed technique avoids many costly computations and off-chip memory accesses. Our experimental evaluation shows that ¿-Test reduces the average energy consumption of the overall GPU/Memory system by 15.7 % and the runtime of the evaluated benchmarks by 10.6 % on average.This work has been supported by the the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency under grant TIN2016-75344-R (AEI/-FEDER, EU) and the ICREA Academia program. D. Corbal´an-Navarro has been supported by a PhD research fellowship from the University of Murcia.Peer ReviewedPostprint (author's final draft

Rendering Elimination: Early Discard of Redundant Tiles in the Graphics Pipeline

GPUs are one of the most energy-consuming components for real-time rendering applications, since a large number of fragment shading computations and memory accesses are involved. Main memory bandwidth is especially taxing battery-operated devices such as smartphones. Tile-Based Rendering GPUs divide the screen space into multiple tiles that are independently rendered in on-chip buffers, thus reducing memory bandwidth and energy consumption. We have observed that, in many animated graphics workloads, a large number of screen tiles have the same color across adjacent frames. In this paper, we propose Rendering Elimination (RE), a novel micro-architectural technique that accurately determines if a tile will be identical to the same tile in the preceding frame before rasterization by means of comparing signatures. Since RE identifies redundant tiles early in the graphics pipeline, it completely avoids the computation and memory accesses of the most power consuming stages of the pipeline, which substantially reduces the execution time and the energy consumption of the GPU. For widely used Android applications, we show that RE achieves an average speedup of 1.74x and energy reduction of 43% for the GPU/Memory system, surpassing by far the benefits of Transaction Elimination, a state-of-the-art memory bandwidth reduction technique available in some commercial Tile-Based Rendering GPUs

Advanced Methods for Real-time Metagenomic Analysis of Nanopore Sequencing Data

Whole shotgun metagenomics sequencing allows researchers to retrieve information about all organisms in a complex sample. This method enables microbiologists to detect pathogens in clinical samples, study the microbial diversity in various environments, and detect abundance differences of certain microbes under different living conditions. The emergence of nanopore sequencing has offered many new possibilities for clinical and environmental microbiologists. In particular, the portability of the small nanopore sequencing devices and the ability to selectively sequence only DNA from interesting organisms are expected to make a significant contribution to the field. However, both options require memory-efficient methods that perform real-time data analysis on commodity hardware like usual laptops. In this thesis, I present new methods for real-time analysis of nanopore sequencing data in a metagenomic context. These methods are based on optimized algorithmic approaches querying the sequenced data against large sets of reference sequences. The main goal of those contributions is to improve the sequencing and analysis of underrepresented organisms in complex metagenomic samples and enable this analysis in low-resource settings in the field. First, I introduce ReadBouncer as a new tool for nanopore adaptive sampling, which can reject uninteresting DNA molecules during the sequencing process. ReadBouncer improves read classification compared to other adaptive sampling tools and has fewer memory requirements. These improvements enable a higher enrichment of underrepresented sequences while performing adaptive sampling in the field. I further show that, besides host sequence removal and enrichment of low-abundant microbes, adaptive sampling can enrich underrepresented plasmid sequences in bacterial samples. These plasmids play a crucial role in the dissemination of antibiotic resistance genes. However, their characterization requires expensive and time-consuming lab protocols. I describe how adaptive sampling can be used as a cheap method for the enrichment of plasmids, which can make a significant contribution to the point-of-care sequencing of bacterial pathogens. Finally, I introduce a novel memory- and space-efficient algorithm for real-time taxonomic profiling of nanopore reads that was implemented in Taxor. It improves the taxonomic classification of nanopore reads compared to other taxonomic profiling tools and tremendously reduces the memory footprint. The resulting database index for thousands of microbial species is small enough to fit into the memory of a small laptop, enabling real-time metagenomics analysis of nanopore sequencing data with large reference databases in the field