Implementation of an AMIDAR-based Java Processor

This thesis presents a Java processor based on the Adaptive Microinstruction Driven Architecture (AMIDAR). This processor is intended as a research platform for investigating adaptive processor architectures. Combined with a configurable accelerator, it is able to detect and speed up hot spots of arbitrary applications dynamically. In contrast to classical RISC processors, an AMIDAR-based processor consists of four main types of components: a token machine, functional units (FUs), a token distribution network and an FU interconnect structure. The token machine is a specialized functional unit and controls the other FUs by means of tokens. These tokens are delivered to the FUs over the token distribution network. The tokens inform the FUs about what to do with input data and where to send the results. Data is exchanged among the FUs over the FU interconnect structure. Based on the virtual machine architecture defined by the Java bytecode, a total of six FUs have been developed for the Java processor, namely a frame stack, a heap manager, a thread scheduler, a debugger, an integer ALU and a floating-point unit. Using these FUs, the processor can already execute the SPEC JVM98 benchmark suite properly. This indicates that it can be employed to run a broad variety of applications rather than embedded software only. Besides bytecode execution, several enhanced features have also been implemented in the processor to improve its performance and usability. First, the processor includes an object cache using a novel cache index generation scheme that provides a better average hit rate than the classical XOR-based scheme. Second, a hardware garbage collector has been integrated into the heap manager, which greatly reduces the overhead caused by the garbage collection process. Third, thread scheduling has been realized in hardware as well, which allows it to be performed concurrently with the running application. Furthermore, a complete debugging framework has been developed for the processor, which provides powerful debugging functionalities at both software and hardware levels

Analyse des performances de stockage, en mémoire et sur les périphériques d'entrée/sortie, à partir d'une trace d'exécution

Le stockage des données est vital pour l’industrie informatique. Les supports de stockage doivent être rapides et fiables pour répondre aux demandes croissantes des entreprises. Les technologies de stockage peuvent être classifiées en deux catégories principales : stockage de masse et stockage en mémoire. Le stockage de masse permet de sauvegarder une grande quantité de données à long terme. Les données sont enregistrées localement sur des périphériques d’entrée/sortie, comme les disques durs (HDD) et les Solid-State Drive (SSD), ou en ligne sur des systèmes de stockage distribué. Le stockage en mémoire permet de garder temporairement les données nécessaires pour les programmes en cours d’exécution. La mémoire vive est caractérisée par sa rapidité d’accès, indispensable pour fournir rapidement les données à l’unité de calcul du processeur. Les systèmes d’exploitation utilisent plusieurs mécanismes pour gérer les périphériques de stockage, par exemple les ordonnanceurs de disque et les allocateurs de mémoire. Le temps de traitement d’une requête de stockage est affecté par l’interaction entre plusieurs soussystèmes, ce qui complique la tâche de débogage. Les outils existants, comme les outils d’étalonnage, permettent de donner une vague idée sur la performance globale du système, mais ne permettent pas d’identifier précisément les causes d’une mauvaise performance. L’analyse dynamique par trace d’exécution est très utile pour l’étude de performance des systèmes. Le traçage permet de collecter des données précises sur le fonctionnement du système, ce qui permet de détecter des problèmes de performance difficilement identifiables. L’objectif de cette thèse est de fournir un outil permettant d’analyser les performances de stockage, en mémoire et sur les périphériques d’entrée/sortie, en se basant sur les traces d’exécution. Les défis relevés par cet outil sont : collecter les données nécessaires à l’analyse depuis le noyau et les programmes en mode utilisateur, limiter le surcoût du traçage et la taille des traces générées, synchroniser les différentes traces, fournir des analyses multiniveau couvrant plusieurs aspects de la performance et enfin proposer des abstractions permettant aux utilisateurs de facilement comprendre les traces.----------ABSTRACT: Data storage is an essential resource for the computer industry. Storage devices must be fast and reliable to meet the growing demands of the data-driven economy. Storage technologies can be classified into two main categories: mass storage and main memory storage. Mass storage can store large amounts of data persistently. Data is saved locally on input/output devices, such as Hard Disk Drives (HDD) and Solid-State Drives (SSD), or remotely on distributed storage systems. Main memory storage temporarily holds the necessary data for running programs. Main memory is characterized by its high access speed, essential to quickly provide data to the Central Processing Unit (CPU). Operating systems use several mechanisms to manage storage devices, such as disk schedulers and memory allocators. The processing time of a storage request is affected by the interaction between several subsystems, which complicates the debugging task. Existing tools, such as benchmarking tools, provide a general idea of the overall system performance, but do not accurately identify the causes of poor performance. Dynamic analysis through execution tracing is a solution for the detailed runtime analysis of storage systems. Tracing collects precise data about the internal behavior of the system, which helps in detecting performance problems that are difficult to identify. The goal of this thesis is to provide a tool to analyze storage performance based on lowlevel trace events. The main challenges addressed by this tool are: collecting the required data using kernel and userspace tracing, limiting the overhead of tracing and the size of the generated traces, synchronizing the traces collected from different sources, providing multi-level analyses covering several aspects of storage performance, and lastly proposing abstractions allowing users to easily understand the traces. We carefully designed and inserted the instrumentation needed for the analyses. The tracepoints provide full visibility into the system and track the lifecycle of storage requests, from creation to processing. The Linux Trace Toolkit Next Generation (LTTng), a free and low-overhead tracer, is used for data collection. This tracer is characterized by its stability, and efficiency with highly parallel applications, thanks to the lock-free synchronization mechanisms used to update the content of the trace buffers. We also contributed to the creation of a patch that allows LTTng to capture the call stacks of userspace events

Architectural Principles for Database Systems on Storage-Class Memory

Database systems have long been optimized to hide the higher latency of storage media, yielding complex persistence mechanisms. With the advent of large DRAM capacities, it became possible to keep a full copy of the data in DRAM. Systems that leverage this possibility, such as main-memory databases, keep two copies of the data in two different formats: one in main memory and the other one in storage. The two copies are kept synchronized using snapshotting and logging. This main-memory-centric architecture yields nearly two orders of magnitude faster analytical processing than traditional, disk-centric ones. The rise of Big Data emphasized the importance of such systems with an ever-increasing need for more main memory. However, DRAM is hitting its scalability limits: It is intrinsically hard to further increase its density. Storage-Class Memory (SCM) is a group of novel memory technologies that promise to alleviate DRAM’s scalability limits. They combine the non-volatility, density, and economic characteristics of storage media with the byte-addressability and a latency close to that of DRAM. Therefore, SCM can serve as persistent main memory, thereby bridging the gap between main memory and storage. In this dissertation, we explore the impact of SCM as persistent main memory on database systems. Assuming a hybrid SCM-DRAM hardware architecture, we propose a novel software architecture for database systems that places primary data in SCM and directly operates on it, eliminating the need for explicit IO. This architecture yields many benefits: First, it obviates the need to reload data from storage to main memory during recovery, as data is discovered and accessed directly in SCM. Second, it allows replacing the traditional logging infrastructure by fine-grained, cheap micro-logging at data-structure level. Third, secondary data can be stored in DRAM and reconstructed during recovery. Fourth, system runtime information can be stored in SCM to improve recovery time. Finally, the system may retain and continue in-flight transactions in case of system failures. However, SCM is no panacea as it raises unprecedented programming challenges. Given its byte-addressability and low latency, processors can access, read, modify, and persist data in SCM using load/store instructions at a CPU cache line granularity. The path from CPU registers to SCM is long and mostly volatile, including store buffers and CPU caches, leaving the programmer with little control over when data is persisted. Therefore, there is a need to enforce the order and durability of SCM writes using persistence primitives, such as cache line flushing instructions. This in turn creates new failure scenarios, such as missing or misplaced persistence primitives. We devise several building blocks to overcome these challenges. First, we identify the programming challenges of SCM and present a sound programming model that solves them. Then, we tackle memory management, as the first required building block to build a database system, by designing a highly scalable SCM allocator, named PAllocator, that fulfills the versatile needs of database systems. Thereafter, we propose the FPTree, a highly scalable hybrid SCM-DRAM persistent B+-Tree that bridges the gap between the performance of transient and persistent B+-Trees. Using these building blocks, we realize our envisioned database architecture in SOFORT, a hybrid SCM-DRAM columnar transactional engine. We propose an SCM-optimized MVCC scheme that eliminates write-ahead logging from the critical path of transactions. Since SCM -resident data is near-instantly available upon recovery, the new recovery bottleneck is rebuilding DRAM-based data. To alleviate this bottleneck, we propose a novel recovery technique that achieves nearly instant responsiveness of the database by accepting queries right after recovering SCM -based data, while rebuilding DRAM -based data in the background. Additionally, SCM brings new failure scenarios that existing testing tools cannot detect. Hence, we propose an online testing framework that is able to automatically simulate power failures and detect missing or misplaced persistence primitives. Finally, our proposed building blocks can serve to build more complex systems, paving the way for future database systems on SCM

Raphtory: Modelling, Maintenance and Analysis of Distributed Temporal Graphs.

PhD ThesesTemporal graphs capture the development of relationships within data throughout time. This model ts naturally within a streaming architecture, where new events can be inserted directly into the graph upon arrival from a data source and be compared to related entities or historical state. However, the majority of graph processing systems only consider traditional graph analysis on static data, whilst those which do expand past this often only support batched updating and delta analysis across graph snapshots. In this work we de ne a temporal property graph model and the semantics for updating it in both a distributed and non-distributed context. We have built Raphtory, a distributed temporal graph analytics platform which maintains the full graph history in memory, leveraging the de ned update semantics to insert streamed events directly into the model without batching or centralised ordering. In parallel with the ingestion, traditional and time-aware analytics may be performed on the most up-to-date version of the graph, as well as any point throughout its history. The depth of history viewed from the perspective of a time point may also be varied to explore both short and long term patterns within the data. Through this we extract novel insights over a variety of use cases, including phenomena never seen before in social networks. Finally, we demonstrate Raphtory's ability to scale both vertically and horizontally, handling consistent throughput in excess of 100,000 updates a second alongside the ingestion and maintenance of graphs built from billions of events

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers

African Handbook of Climate Change Adaptation

This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced

The First Global Integrated Marine Assessment: World Ocean Assessment I

We used satellite-derived sea-surface-temperature (SST) data along with in-situ data collected along a meridional transect between 18.85 and 20.25°N along 69.2°E to describe the evolution of an SST filament and front during 25 November to 1 December in the northeastern Arabian Sea (NEAS). Both features were ∼ 100 km long, lasted about a week and were associated with weak temperature gradients (∼ 0.07°C km⁻¹). The in-situ data were collected first using a suite of surface sensors during a north–south mapping of this transect and showed the existence of a chlorophyll maximum within the filament. This surface data acquisition was followed by a high-resolution south–north CTD (conductivity–temperature–depth) sampling along the transect. In the two days that elapsed between the two in-situ measurements, the filament had shrunk in size and moved northward. In general, the current direction was northwestward and advected these mesoscale features. The CTD data also showed an SST front towards the northern end of the transect. In both these features, the chlorophyll concentration was higher than in the surrounding waters. The temperature and salinity data from the CTD suggest upward mixing or pumping of water from the base of the mixed layer, where a chlorophyll maximum was present, into the mixed layer that was about 60 m thick. A striking diurnal cycle was evident in the chlorophyll concentration, with higher values tending to occur closer to the surface during the night. The in-situ data from both surface sensors and CTD, and so also satellite-derived chlorophyll data, showed higher chlorophyll concentration, particularly at sub-surface levels, between the filament and the front, but there was no corresponding signature in the temperature and salinity data. Analysis of the SST fronts in the satellite data shows that fronts weaker than those associated with the filament and the front had crossed the transect in this region a day or two preceding the sampling of the front