Optymalizacja zapytań w środowisku heterogenicznym CPU/GPU dla baz danych szeregów czasowych

In recent years, processing and exploration of time series has experienced a noticeable interest. Growing volumes of data and needs of efficient processing pushed the research in new directions, including hardware based solutions. Graphics Processing Units (GPU) have significantly more applications than just rendering images. They are also used in general purpose computing to solve problems that can benefit from massive parallel processing. There are numerous reports confirming the effectiveness of GPU in science and industrial applications. However, there are several issues related with GPU usage as a databases coprocessor that must be considered. First, all computations on the GPU are preceded by time consuming memory transfers. In this thesis we present a study on lossless lightweight compression algorithms in the context of GPU computations and time series database systems. We discuss the algorithms, their application and implementation details on GPU. We analyse their influence on the data processing efficiency, taking into account both the data transfer time and decompression time. Moreover, we propose a data adaptive compression planner based on those algorithms, which uses hierarchy of multiple compression algorithms in order to further reduce the data size. Secondly, there are tasks that either hardly suit GPU or fit GPU only partially. This may be related to the size or type of the task. We elaborate on heterogeneous CPU/GPU computation environment and optimization method that seeks equilibrium between these two computation platforms. This method is based on heuristic search for bi-objective optimal execution plans. The underlying model mimics the commodity market, where devices are producers and queries are consumers. The value of resources of computing devices is controlled by supply-and-demand laws. Our model of the optimization criteria allows finding solutions for heterogeneous query processing problems where existing methods have been ineffective. Furthermore, it also offers lower time complexity and higher accuracy than other methods. The dissertation also discusses an exemplary application of time series databases: the analysis of zebra mussel (Dreissena polymorpha) behaviour based on observations of the change of the gap between the valves, collected as a time series. We propose a new algorithm based on wavelets and kernel methods that detects relevant events in the collected data. This algorithm allows us to extract elementary behaviour events from the observations. Moreover, we propose an efficient framework for automatic classification to separate the control and stressful conditions. Since zebra mussels are well-known bioindicators this is an important step towards the creation of an advanced environmental biomonitoring system.W ostatnich latach przetwarzanie i badanie szeregów czasowych zyskało spore zainteresowanie. Rosnące ilości danych i potrzeba ich sprawnego przetwarzania nadały nowe kierunki prowadzonym badaniom, które uwzględniają również wykorzystanie rozwiązań sprzętowych. Procesory graficzne (GPU) mają znacznie więcej zastosowań niż tylko wyświetlanie obrazów. Coraz częściej są wykorzystywane przy rozwiązywaniu problemów obliczeniowych ogólnego zastosowania, które mogą spożytkować możliwości przetwarzania masywnie równoległego. Wiele źródeł potwierdza skuteczność GPU zarówno w nauce, jak i w zastosowaniach w przemyśle. Jest jednak kilka kwestii związanych z użyciem GPU jako koprocesora w bazach danych, które trzeba mieć na uwadze. Po pierwsze, wszystkie obliczenia na GPU są poprzedzone czasochłonnym transferem danych. W pracy zaprezentowano rezultaty badań dotyczących lekkich i bezstratnych algorytmów kompresji w kontekście obliczeń GPU i systemów baz danych dla szeregów czasowych. Omówione zostały algorytmy, ich zastosowanie oraz szczegóły implementacyjne na GPU. Rozważono wpływ algorytmów na wydajność przetwarzania danych z uwzględnieniem czasu transferu i dekompresji danych. Ponadto, zaproponowany został adaptacyjny planer kompresji danych, który wykorzystuje różne algorytmy lekkiej kompresji w celu dalszego zmniejszenia rozmiaru skompresowanych danych. Kolejnym problemem są zadania, które źle (lub tylko częściowo) wpisują się w architekturę GPU. Może być to związane z rozmiarem lub rodzajem zadania. W pracy zaproponowany został model heterogenicznych obliczeń na CPU/GPU. Przedstawiono metody optymalizacji, poszukujące równowagi między różnymi platformami obliczeniowymi. Opierają się one na heurystycznym poszukiwaniu planów wykonania uwzględniających wiele celów optymalizacyjnych. Model leżący u podstaw tego podejścia naśladuje rynki towarowe, gdzie urządzenia są traktowane jako producenci, konsumentami są natomiast plany zapytań. Wartość zasobów urządzeń komputerowych jest kontrolowana przez prawa popytu i podaży. Zastosowanie różnych kryteriów optymalizacji pozwala rozwiązać problemy z zakresu heterogenicznego przetwarzania zapytań, dla których dotychczasowe metody były nieskuteczne. Ponadto proponowane rozwiązania wyróżnia mniejsza złożoność czasowa i lepsza dokładność. W rozprawie omówiono przykładowe zastosowanie baz danych szeregów czasowych: analizę zachowań racicznicy zmiennej (Dreissena polymorpha) opartą na obserwacji rozchyleń muszli zapisanej w postaci szeregów czasowych. Proponowany jest nowy algorytm oparty na falkach i funkcjach jądrowych (ang. kernel functions), który wykrywa odpowiednie zdarzenia w zebranych danych. Algorytm ten pozwala wyodrębnić zdarzenia elementarne z zapisanych obserwacji. Ponadto proponowany jest zarys systemu do automatycznego oddzielenia pomiarów kontrolnych i tych dokonanych w stresujących warunkach. Jako że małże z gatunku Dreissena polymorpha są znanymi wskaźnikami biologicznymi, jest to istotny krok w kierunku biologicznych systemów wczesnego ostrzegania

Greenhouse gas mitigation strategies for the oil industry - bottom-up system analysis on the transition of the Colombian oil production and refining sector

The oil and gas industry is responsible for 6% of global CO2 emissions, from exploration and production to refining and downstream petrochemical production and rises to 50% when final use of products is included. Previous studies considered technology options to reduce CO2 emissions but are constrained by details and options covered and the lack of involving further integration with the surrounding energy systems and alternative energy sources. This research aimed to assess the techno-economic and CO2 mitigation potential for decarbonization pathways of the crude oil industry in Colombia on a detailed level for existing facilities. Several options were considered namely energy efficiency measurements (EEM), carbon capture and storage (CCS), CO2 enhanced-oil-recovery (CO2-EOR), electrification, blue and green hydrogen, and biomass-based alternatives. The key research questions answered are: 1)What are the promising technological options, their potential and mitigation cost for decarbonizing the oil industry? 2) In which way potential deployment pathways can be developed for a decarbonization strategy of the oil industry? 3)What is an effective design for a methodological approach to assess and quantify mitigation options and decarbonization pathways for existing industrial facilities? An improved methodological approach to assess decarbonization pathways for a complex-industry was developed and based on a) bottom-up approach, b) comprehensive mitigation options and analysis of interactions during deployment, c) full value-chain perspective, d) building deployment pathways for decarbonization. Future assessments at sector level can utilize insights from this work to propose deployment pathways of decarbonization strategies from an integrated and comprehensive inventory of mitigation options

Voodoo - a vector algebra for portable database performance on modern hardware

In-memory databases require careful tuning and many engineering tricks to achieve good performance. Such database performance engineering is hard: a plethora of data and hardware-dependent optimization techniques form a design space that is difficult to navigate for a skilled engineer --- even more so for a query compiler. To facilitate performance-oriented design exploration and query plan compilation, we present Voodoo, a declarative intermediate algebra that abstracts the detailed architectural properties of the hardware, such as multi- or many-core architectures, caches and SIMD registers, without losing the ability to generate highly tuned code. Because it consists of a collection of declarative, vector-oriented operations, Voodoo is easier to reason about and tune than low-level C and related hardware-focused extensions (Intrinsics, OpenCL, CUDA, etc.). This enables our Voodoo compiler to produce (OpenCL) code that rivals and even outperforms the fastest state-of-the-art in memory databases for both GPUs and CPUs. In addition, Voodoo makes it possible to express techniques as diverse as cache-conscious processing, predication and vectorization (again on both GPUs and CPUs) with just a few lines of code. Central to our approach is a novel idea we termed control vectors, which allows a code generating frontend to expose parallelism to the Voodoo compiler in a abstract manner, enabling portable performance across hardware platforms. We used Voodoo to build an alternative backend for MonetDB, a popular open-source in-memory database. Our backend allows MonetDB to perform at the same level as highly tuned in-memory databases, including HyPeR and Ocelot. We also demonstrate Voodoo's usefulness when investigating hardware conscious tuning techniques, assessing their performance on different queries, devices and data

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Efficient Memory Management for GPU-based Deep Learning Systems

GPU (graphics processing unit) has been used for many data-intensive applications. Among them, deep learning systems are one of the most important consumer systems for GPU nowadays. As deep learning applications impose deeper and larger models in order to achieve higher accuracy, memory management becomes an important research topic for deep learning systems, given that GPU has limited memory size. Many approaches have been proposed towards this issue, e.g., model compression and memory swapping. However, they either degrade the model accuracy or require a lot of manual intervention. In this paper, we propose two orthogonal approaches to reduce the memory cost from the system perspective. Our approaches are transparent to the models, and thus do not affect the model accuracy. They are achieved by exploiting the iterative nature of the training algorithm of deep learning to derive the lifetime and read/write order of all variables. With the lifetime semantics, we are able to implement a memory pool with minimal fragments. However, the optimization problem is NP-complete. We propose a heuristic algorithm that reduces up to 13.3% of memory compared with Nvidia's default memory pool with equal time complexity. With the read/write semantics, the variables that are not in use can be swapped out from GPU to CPU to reduce the memory footprint. We propose multiple swapping strategies to automatically decide which variable to swap and when to swap out (in), which reduces the memory cost by up to 34.2% without communication overhead

Efficient Memory Management for GPU-based Deep Learning Systems

GPU (graphics processing unit) has been used for many data-intensive applications. Among them, deep learning systems are one of the most important consumer systems for GPU nowadays. As deep learning applications impose deeper and larger models in order to achieve higher accuracy, memory management becomes an important research topic for deep learning systems, given that GPU has limited memory size. Many approaches have been proposed towards this issue, e.g., model compression and memory swapping. However, they either degrade the model accuracy or require a lot of manual intervention. In this paper, we propose two orthogonal approaches to reduce the memory cost from the system perspective. Our approaches are transparent to the models, and thus do not affect the model accuracy. They are achieved by exploiting the iterative nature of the training algorithm of deep learning to derive the lifetime and read/write order of all variables. With the lifetime semantics, we are able to implement a memory pool with minimal fragments. However, the optimization problem is NP-complete. We propose a heuristic algorithm that reduces up to 13.3% of memory compared with Nvidia's default memory pool with equal time complexity. With the read/write semantics, the variables that are not in use can be swapped out from GPU to CPU to reduce the memory footprint. We propose multiple swapping strategies to automatically decide which variable to swap and when to swap out (in), which reduces the memory cost by up to 34.2% without communication overhead