Scalable Hash Tables

The term scalability with regards to this dissertation has two meanings: It means taking the best possible advantage of the provided resources (both computational and memory resources) and it also means scaling data structures in the literal sense, i.e., growing the capacity, by “rescaling” the table. Scaling well to computational resources implies constructing the fastest best per- forming algorithms and data structures. On today’s many-core machines the best performance is immediately associated with parallelism. Since CPU frequencies have stopped growing about 10-15 years ago, parallelism is the only way to take ad- vantage of growing computational resources. But for data structures in general and hash tables in particular performance is not only linked to faster computations. The most execution time is actually spent waiting for memory. Thus optimizing data structures to reduce the amount of memory accesses or to take better advantage of the memory hierarchy especially through predictable access patterns and prefetch- ing is just as important. In terms of scaling the size of hash tables we have identified three domains where scaling hash-based data structures have been lacking previously, i.e., space effi- cient growing, concurrent hash tables, and Approximate Membership Query data structures (AMQ-filter). Throughout this dissertation, we describe the problems in these areas and develop efficient solutions. We highlight three different libraries that we have developed over the course of this dissertation, each containing mul- tiple implementations that have shown throughout our testing to be among the best implementations in their respective domains. In this composition they offer a comprehensive toolbox that can be used to solve many kinds of hashing related problems or to develop individual solutions for further ones. DySECT is a library for space efficient hash tables specifically growing space effi- cient hash tables that scale with their input size. It contains the namesake DySECT data structure in addition to a number of different probing and cuckoo based im- plementations. Growt is a library for highly efficient concurrent hash tables. It contains a very fast base table and a number of extensions to adapt this table to match any purpose. All extension can be combined to create a variety of different interfaces. In our extensive experimental evaluation, each adaptation has shown to be among the best hash tables for their specific purpose. Lpqfilter is a library for concurrent approximate membership query (AMQ) data structures. It contains some original data structures, like the linear probing quotient filter, as well as some novel approaches to dynamically sized quotient filters

Random hypergraphs for hashing-based data structures

This thesis concerns dictionaries and related data structures that rely on providing several random possibilities for storing each key. Imagine information on a set S of m = |S| keys should be stored in n memory locations, indexed by [n] = {1,…,n}. Each object x [ELEMENT OF] S is assigned a small set e(x) [SUBSET OF OR EQUAL TO] [n] of locations by a random hash function, independent of other objects. Information on x must then be stored in the locations from e(x) only. It is possible that too many objects compete for the same locations, in particular if the load c = m/n is high. Successfully storing all information may then be impossible. For most distributions of e(x), however, success or failure can be predicted very reliably, since the success probability is close to 1 for loads c less than a certain load threshold c^* and close to 0 for loads greater than this load threshold. We mainly consider two types of data structures: • A cuckoo hash table is a dictionary data structure where each key x [ELEMENT OF] S is stored together with an associated value f(x) in one of the memory locations with an index from e(x). The distribution of e(x) is controlled by the hashing scheme. We analyse three known hashing schemes, and determine their exact load thresholds. The schemes are unaligned blocks, double hashing and a scheme for dynamically growing key sets. • A retrieval data structure also stores a value f(x) for each x [ELEMENT OF] S. This time, the values stored in the memory locations from e(x) must satisfy a linear equation that characterises the value f(x). The resulting data structure is extremely compact, but unusual. It cannot answer questions of the form “is y [ELEMENT OF] S?”. Given a key y it returns a value z. If y [ELEMENT OF] S, then z = f(y) is guaranteed, otherwise z may be an arbitrary value. We consider two new hashing schemes, where the elements of e(x) are contained in one or two contiguous blocks. This yields good access times on a word RAM and high cache efficiency. An important question is whether these types of data structures can be constructed in linear time. The success probability of a natural linear time greedy algorithm exhibits, once again, threshold behaviour with respect to the load c. We identify a hashing scheme that leads to a particularly high threshold value in this regard. In the mathematical model, the memory locations [n] correspond to vertices, and the sets e(x) for x [ELEMENT OF] S correspond to hyperedges. Three properties of the resulting hypergraphs turn out to be important: peelability, solvability and orientability. Therefore, large parts of this thesis examine how hyperedge distribution and load affects the probabilities with which these properties hold and derive corresponding thresholds. Translated back into the world of data structures, we achieve low access times, high memory efficiency and low construction times. We complement and support the theoretical results by experiments.Diese Arbeit behandelt Wörterbücher und verwandte Datenstrukturen, die darauf aufbauen, mehrere zufällige Möglichkeiten zur Speicherung jedes Schlüssels vorzusehen. Man stelle sich vor, Information über eine Menge S von m = |S| Schlüsseln soll in n Speicherplätzen abgelegt werden, die durch [n] = {1,…,n} indiziert sind. Jeder Schlüssel x [ELEMENT OF] S bekommt eine kleine Menge e(x) [SUBSET OF OR EQUAL TO] [n] von Speicherplätzen durch eine zufällige Hashfunktion unabhängig von anderen Schlüsseln zugewiesen. Die Information über x darf nun ausschließlich in den Plätzen aus e(x) untergebracht werden. Es kann hierbei passieren, dass zu viele Schlüssel um dieselben Speicherplätze konkurrieren, insbesondere bei hoher Auslastung c = m/n. Eine erfolgreiche Speicherung der Gesamtinformation ist dann eventuell unmöglich. Für die meisten Verteilungen von e(x) lässt sich Erfolg oder Misserfolg allerdings sehr zuverlässig vorhersagen, da für Auslastung c unterhalb eines gewissen Auslastungsschwellwertes c* die Erfolgswahrscheinlichkeit nahezu 1 ist und für c jenseits dieses Auslastungsschwellwertes nahezu 0 ist. Hauptsächlich werden wir zwei Arten von Datenstrukturen betrachten: • Eine Kuckucks-Hashtabelle ist eine Wörterbuchdatenstruktur, bei der jeder Schlüssel x [ELEMENT OF] S zusammen mit einem assoziierten Wert f(x) in einem der Speicherplätze mit Index aus e(x) gespeichert wird. Die Verteilung von e(x) wird hierbei vom Hashing-Schema festgelegt. Wir analysieren drei bekannte Hashing-Schemata und bestimmen erstmals deren exakte Auslastungsschwellwerte im obigen Sinne. Die Schemata sind unausgerichtete Blöcke, Doppel-Hashing sowie ein Schema für dynamisch wachsenden Schlüsselmengen. • Auch eine Retrieval-Datenstruktur speichert einen Wert f(x) für alle x [ELEMENT OF] S. Diesmal sollen die Werte in den Speicherplätzen aus e(x) eine lineare Gleichung erfüllen, die den Wert f(x) charakterisiert. Die entstehende Datenstruktur ist extrem platzsparend, aber ungewöhnlich: Sie ist ungeeignet um Fragen der Form „ist y [ELEMENT OF] S?“ zu beantworten. Bei Anfrage eines Schlüssels y wird ein Ergebnis z zurückgegeben. Falls y [ELEMENT OF] S ist, so ist z = f(y) garantiert, andernfalls darf z ein beliebiger Wert sein. Wir betrachten zwei neue Hashing-Schemata, bei denen die Elemente von e(x) in einem oder in zwei zusammenhängenden Blöcken liegen. So werden gute Zugriffszeiten auf Word-RAMs und eine hohe Cache-Effizienz erzielt. Eine wichtige Frage ist, ob Datenstrukturen obiger Art in Linearzeit konstruiert werden können. Die Erfolgswahrscheinlichkeit eines naheliegenden Greedy-Algorithmus weist abermals ein Schwellwertverhalten in Bezug auf die Auslastung c auf. Wir identifizieren ein Hashing-Schema, das diesbezüglich einen besonders hohen Schwellwert mit sich bringt. In der mathematischen Modellierung werden die Speicherpositionen [n] als Knoten und die Mengen e(x) für x [ELEMENT OF] S als Hyperkanten aufgefasst. Drei Eigenschaften der entstehenden Hypergraphen stellen sich dann als zentral heraus: Schälbarkeit, Lösbarkeit und Orientierbarkeit. Weite Teile dieser Arbeit beschäftigen sich daher mit den Wahrscheinlichkeiten für das Vorliegen dieser Eigenschaften abhängig von Hashing Schema und Auslastung, sowie mit entsprechenden Schwellwerten. Eine Rückübersetzung der Ergebnisse liefert dann Datenstrukturen mit geringen Anfragezeiten, hoher Speichereffizienz und geringen Konstruktionszeiten. Die theoretischen Überlegungen werden dabei durch experimentelle Ergebnisse ergänzt und gestützt

Productive Programming Systems for Heterogeneous Supercomputers

The majority of today's scientific and data analytics workloads are still run on relatively energy inefficient, heavyweight, general-purpose processing cores, often referred to in the literature as latency-oriented architectures. The flexibility of these architectures and the programmer aids included (e.g. large and deep cache hierarchies, branch prediction logic, pre-fetch logic) makes them flexible enough to run a wide range of applications fast. However, we have started to see growth in the use of lightweight, simpler, energy-efficient, and functionally constrained cores. These architectures are commonly referred to as throughput-oriented. Within each shared memory node, the computational backbone of future throughput-oriented HPC machines will consist of large pools of lightweight cores. The first wave of throughput-oriented computing came in the mid 2000's with the use of GPUs for general-purpose and scientific computing. Today we are entering the second wave of throughput-oriented computing, with the introduction of NVIDIA Pascal GPUs, Intel Knights Landing Xeon Phi processors, the Epiphany Co-Processor, the Sunway MPP, and other throughput-oriented architectures that enable pre-exascale computing. However, while the majority of the FLOPS in designs for future HPC systems come from throughput-oriented architectures, they are still commonly paired with latency-oriented cores which handle management functions and lightweight/un-parallelizable computational kernels. Hence, most future HPC machines will be heterogeneous in their processing cores. However, the heterogeneity of future machines will not be limited to the processing elements. Indeed, heterogeneity will also exist in the storage, networking, memory, and software stacks of future supercomputers. As a result, it will be necessary to combine many different programming models and libraries in a single application. How to do so in a programmable and well-performing manner is an open research question. This thesis addresses this question using two approaches. First, we explore using managed runtimes on HPC platforms. As a result of their high-level programming models, these managed runtimes have a long history of supporting data analytics workloads on commodity hardware, but often come with overheads which make them less common in the HPC domain. Managed runtimes are also not supported natively on throughput-oriented architectures. Second, we explore the use of a modular programming model and work-stealing runtime to compose the programming and scheduling of multiple third-party HPC libraries. This approach leverages existing investment in HPC libraries, unifies the scheduling of work on a platform, and is designed to quickly support new programming model and runtime extensions. In support of these two approaches, this thesis also makes novel contributions in tooling for future supercomputers. We demonstrate the value of checkpoints as a software development tool on current and future HPC machines, and present novel techniques in performance prediction across heterogeneous cores

SoS: self-organizing substrates

Large-scale networked systems often, both by design or chance exhibit self-organizing properties. Understanding self-organization using tools from cybernetics, particularly modeling them as Markov processes is a first step towards a formal framework which can be used in (decentralized) systems research and design.Interesting aspects to look for include the time evolution of a system and to investigate if and when a system converges to some absorbing states or stabilizes into a dynamic (and stable) equilibrium and how it performs under such an equilibrium state. Such a formal framework brings in objectivity in systems research, helping discern facts from artefacts as well as providing tools for quantitative evaluation of such systems. This thesis introduces such formalism in analyzing and evaluating peer-to-peer (P2P) systems in order to better understand the dynamics of such systems which in turn helps in better designs. In particular this thesis develops and studies the fundamental building blocks for a P2P storage system. In the process the design and evaluation methodology we pursue illustrate the typical methodological approaches in studying and designing self-organizing systems, and how the analysis methodology influences the design of the algorithms themselves to meet system design goals (preferably with quantifiable guarantees). These goals include efficiency, availability and durability, load-balance, high fault-tolerance and self-maintenance even in adversarial conditions like arbitrarily skewed and dynamic load and high membership dynamics (churn), apart of-course the specific functionalities that the system is supposed to provide. The functionalities we study here are some of the fundamental building blocks for various P2P applications and systems including P2P storage systems, and hence we call them substrates or base infrastructure. These elemental functionalities include: (i) Reliable and efficient discovery of resources distributed over the network in a decentralized manner; (ii) Communication among participants in an address independent manner, i.e., even when peers change their physical addresses; (iii) Availability and persistence of stored objects in the network, irrespective of availability or departure of individual participants from the system at any time; and (iv) Freshness of the objects/resources' (up-to-date replicas). Internet-scale distributed index structures (often termed as structured overlays) are used for discovery and access of resources in a decentralized setting. We propose a rapid construction from scratch and maintenance of the P-Grid overlay network in a self-organized manner so as to provide efficient search of both individual keys as well as a whole range of keys, doing so providing good load-balancing characteristics for diverse kind of arbitrarily skewed loads - storage and replication, query forwarding and query answering loads. For fast overlay construction we employ recursive partitioning of the key-space so that the resulting partitions are balanced with respect to storage load and replication. The proper algorithmic parameters for such partitioning is derived from a transient analysis of the partitioning process which has Markov property. Preservation of ordering information in P-Grid such that queries other than exact queries, like range queries can be efficiently and rather trivially handled makes P-Grid suitable for data-oriented applications. Fast overlay construction is analogous to building an index on a new set of keys making P-Grid suitable as the underlying indexing mechanism for peer-to-peer information retrieval applications among other potential applications which may require frequent indexing of new attributes apart regular updates to an existing index. In order to deal with membership dynamics, in particular changing physical address of peers across sessions, the overlay itself is used as a (self-referential) directory service for maintaining the participating peers' physical addresses across sessions. Exploiting this self-referential directory, a family of overlay maintenance scheme has been designed with lower communication overhead than other overlay maintenance strategies. The notion of dynamic equilibrium study for overlays under continuous churn and repairs, modeled as a Markov process, was introduced in order to evaluate and compare the overlay maintenance schemes. While the self-referential directory was originally invented to realize overlay maintenance schemes with lower overheads than existing overlay maintenance schemes, the self-referential directory is generic in nature and can be used for various other purposes, e.g., as a decentralized public key infrastructure. Persistence of peer identity across sessions, in spite of changes in physical address, provides a logical independence of the overlay network from the underlying physical network. This has many other potential usages, for example, efficient maintenance mechanisms for P2P storage systems and P2P trust and reputation management. We specifically look into the dynamics of maintaining redundancy for storage systems and design a novel lazy maintenance strategy. This strategy is algorithmically a simple variant of existing maintenance strategies which adapts to the system dynamics. This randomized lazy maintenance strategy thus explores the cost-performance trade-offs of the storage maintenance operations in a self-organizing manner. We model the storage system (redundancy), under churn and maintenance, as a Markov process. We perform an equilibrium study to show that the system operates in a more stable dynamic equilibrium with our strategy than for the existing maintenance scheme for comparable overheads. Particularly, we show that our maintenance scheme provides substantial performance gains in terms of maintenance overhead and system's resilience in presence of churn and correlated failures. Finally, we propose a gossip mechanism which works with lower communication overhead than existing approaches for communication among a relatively large set of unreliable peers without assuming any specific structure for their mutual connectivity. We use such a communication primitive for propagating replica updates in P2P systems, facilitating management of mutable content in P2P systems. The peer population affected by a gossip can be modeled as a Markov process. Studying the transient spread of gossips help in choosing proper algorithm parameters to reduce communication overhead while guaranteeing coverage of online peers. Each of these substrates in themselves were developed to find practical solutions for real problems. Put together, these can be used in other applications, including a P2P storage system with support for efficient lookup and inserts, membership dynamics, content mutation and updates, persistence and availability. Many of the ideas have already been implemented in real systems and several others are in the way to be integrated into the implementations. There are two principal contributions of this dissertation. It provides design of the P2P systems which are useful for end-users as well as other application developers who can build upon these existing systems. Secondly, it adapts and introduces the methodology of analysis of a system's time-evolution (tools typically used in diverse domains including physics and cybernetics) to study the long run behavior of P2P systems, and uses this methodology to (re-)design appropriate algorithms and evaluate them. We observed that studying P2P systems from the perspective of complex systems reveals their inner dynamics and hence ways to exploit such dynamics for suitable or better algorithms. In other words, the analysis methodology in itself strongly influences and inspires the way we design such systems. We believe that such an approach of orchestrating self-organization in internet-scale systems, where the algorithms and the analysis methodology have strong mutual influence will significantly change the way future such systems are developed and evaluated. We envision that such an approach will particularly serve as an important tool for the nascent but fast moving P2P systems research and development community

Development of a real-time business intelligence (BI) framework based on hex-elementization of data points for accurate business decision-making

The desire to use business intelligence (BI) to enhance efficiency and effectiveness of business decisions is neither new nor revolutionary. The promise of BI is to provide the ability to capture interrelationship from data and information to guide action towards a business goal. Although BI has been around since the 1960s, businesses still cannot get competitive information in the form they want, when they want and how they want. Business decisions are already full of challenges. The challenges in business decision-making include the use of a vast amount of data, adopting new technologies, and making decisions on a real-time basis. To address these challenges, businesses spend valuable time and resources on data, technologies and business processes. Integration of data in decision-making is crucial for modern businesses. This research aims to propose and validate a framework for organic integration of data into business decision-making. This proposed framework enables efficient business decisions in real-time. The core of this research is to understand and modularise the pre-established set of data points into intelligent and granular “hex-elements” (stated simply, hex-element is a data point with six properties). These intelligent hex-elements build semi-automatic relationships using their six properties between the large volume and high-velocity data points in a dynamic, automated and integrated manner. The proposed business intelligence framework is called “Hex-Elementization” (or “Hex-E” for short). Evolution of technology presents ongoing challenges to BI. These challenges emanate from the challenging nature of the underlying new-age data characterised by large volume, high velocity and wide variety. Efficient and effective analysis of such data depends on the business context and the corresponding technical capabilities of the organisation. Technologies like Big Data, Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), play a key role in capitalising on the variety, volume and veracity of data. Extricating the “value” from data in its various forms, depth and scale require synchronizing technologies with analytics and business processes. Transforming data into useful and actionable intelligence is the discipline of data scientists. Data scientists and data analysts use sophisticated tools to crunch data into information which, in turn, are converted into intelligence. The transformation of data into information and its final consumption as actionable business intelligence is an end-to-end journey. This end-to-end transformation of data to intelligence is complex, time-consuming and resource-intensive. This research explores approaches to ease the challenges the of end-to-end transformation of data into intelligence. This research presents Hex-E as a simplified and semi-automated framework to integrate, unify, correlate and coalesce data (from diverse sources and disparate formats) into intelligence. Furthermore, this framework aims to unify data from diverse sources and disparate formats to help businesses make accurate and timely decisions

Tracing the Compositional Process. Sound art that rewrites its own past: formation, praxis and a computer framework

The domain of this thesis is electroacoustic computer-based music and sound art. It investigates a facet of composition which is often neglected or ill-defined: the process of composing itself and its embedding in time. Previous research mostly focused on instrumental composition or, when electronic music was included, the computer was treated as a tool which would eventually be subtracted from the equation. The aim was either to explain a resultant piece of music by reconstructing the intention of the composer, or to explain human creativity by building a model of the mind. Our aim instead is to understand composition as an irreducible unfolding of material traces which takes place in its own temporality. This understanding is formalised as a software framework that traces creation time as a version graph of transactions. The instantiation and manipulation of any musical structure implemented within this framework is thereby automatically stored in a database. Not only can it be queried ex post by an external researcher—providing a new quality for the empirical analysis of the activity of composing—but it is an integral part of the composition environment. Therefore it can recursively become a source for the ongoing composition and introduce new ways of aesthetic expression. The framework aims to unify creation and performance time, fixed and generative composition, human and algorithmic “writing”, a writing that includes indeterminate elements which condense as concurrent vertices in the version graph. The second major contribution is a critical epistemological discourse on the question of ob- servability and the function of observation. Our goal is to explore a new direction of artistic research which is characterised by a mixed methodology of theoretical writing, technological development and artistic practice. The form of the thesis is an exercise in becoming process-like itself, wherein the epistemic thing is generated by translating the gaps between these three levels. This is my idea of the new aesthetics: That through the operation of a re-entry one may establish a sort of process “form”, yielding works which go beyond a categorical either “sound-in-itself” or “conceptualism”. Exemplary processes are revealed by deconstructing a series of existing pieces, as well as through the successful application of the new framework in the creation of new pieces

Constitutions of Value

Gathering an interdisciplinary range of cutting-edge scholars, this book addresses legal constitutions of value. Global value production and transnational value practices that rely on exploitation and extraction have left us with toxic commons and a damaged planet. Against this situation, the book examines law’s fundamental role in institutions of value production and valuation. Utilising pathbreaking theoretical approaches, it problematizes mainstream efforts to redeem institutions of value production by recoupling them with progressive values. Aiming beyond radical critique, the book opens up the possibility of imagining and enacting new and different value practices. This wide-ranging and accessible book will appeal to international lawyers, socio-legal scholars, those working at the intersections of law and economy and others, in politics, economics, environmental studies and elsewhere, who are concerned with rethinking our current ideas of what has value, what does not, and whether and how value may be revalued