Hardware Acceleration for Unstructured Big Data and Natural Language Processing.

The confluence of the rapid growth in electronic data in recent years, and the renewed interest in domain-specific hardware accelerators presents exciting technical opportunities. Traditional scale-out solutions for processing the vast amounts of text data have been shown to be energy- and cost-inefficient. In contrast, custom hardware accelerators can provide higher throughputs, lower latencies, and significant energy savings. In this thesis, I present a set of hardware accelerators for unstructured big-data processing and natural language processing. The first accelerator, called HAWK, aims to speed up the processing of ad hoc queries against large in-memory logs. HAWK is motivated by the observation that traditional software-based tools for processing large text corpora use memory bandwidth inefficiently due to software overheads, and, thus, fall far short of peak scan rates possible on modern memory systems. HAWK is designed to process data at a constant rate of 32 GB/s—faster than most extant memory systems. I demonstrate that HAWK outperforms state-of-the-art software solutions for text processing, almost by an order of magnitude in many cases. HAWK occupies an area of 45 sq-mm in its pareto-optimal configuration and consumes 22 W of power, well within the area and power envelopes of modern CPU chips. The second accelerator I propose aims to speed up similarity measurement calculations for semantic search in the natural language processing space. By leveraging the latency hiding concepts of multi-threading and simple scheduling mechanisms, my design maximizes functional unit utilization. This similarity measurement accelerator provides speedups of 36x-42x over optimized software running on server-class cores, while requiring 56x-58x lower energy, and only 1.3% of the area.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116712/1/prateekt_1.pd

Distributed computing practice for large-scale science and engineering applications

It is generally accepted that the ability to develop large-scale distributed applications has lagged seriously behind other developments in cyberinfrastructure. In this paper, we provide insight into how such applications have been developed and an understanding of why developing applications for distributed infrastructure is hard. Our approach is unique in the sense that it is centered around half a dozen existing scientific applications; we posit that these scientific applications are representative of the characteristics, requirements, as well as the challenges of the bulk of current distributed applications on production cyberinfrastructure (such as the US TeraGrid). We provide a novel and comprehensive analysis of such distributed scientific applications. Specifically, we survey existing models and methods for large-scale distributed applications and identify commonalities, recurring structures, patterns and abstractions. We find that there are many ad hoc solutions employed to develop and execute distributed applications, which result in a lack of generality and the inability of distributed applications to be extensible and independent of infrastructure details. In our analysis, we introduce the notion of application vectors: a novel way of understanding the structure of distributed applications. Important contributions of this paper include identifying patterns that are derived from a wide range of real distributed applications, as well as an integrated approach to analyzing applications, programming systems and patterns, resulting in the ability to provide a critical assessment of the current practice of developing, deploying and executing distributed applications. Gaps and omissions in the state of the art are identified, and directions for future research are outlined

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

High-Performance Computing Algorithms for Constructing Inverted Files on Emerging Multicore Processors

Current trends in processor architectures increasingly include more cores on a single chip and more complex memory hierarchies, and such a trend is likely to continue in the foreseeable future. These processors offer unprecedented opportunities for speeding up demanding computations if the available resources can be effectively utilized. Simultaneously, parallel programming languages such as OpenMP and MPI have been commonly used on clusters of multicore CPUs while newer programming languages such as OpenCL and CUDA have been widely adopted on recent heterogeneous systems and GPUs respectively. The main goal of this dissertation is to develop techniques and methodologies for exploiting these emerging parallel architectures and parallel programming languages to solve large scale irregular applications such as the construction of inverted files. The extraction of inverted files from large collections of documents forms a critical component of all information retrieval systems including web search engines. In this problem, the disk I/O throughput is the major performance bottleneck especially when intermediate results are written onto disks. In addition to the I/O bottleneck, a number of synchronization and consistency issues must be resolved in order to build the dictionary and postings lists efficiently. To address these issues, we introduce a dictionary data structure using a hybrid of trie and B-trees and a high-throughput pipeline strategy that completely avoids the use of disks as temporary storage for intermediate results, while ensuring the consumption of the input data at a high rate. The high-throughput pipelined strategy produces parallel parsed streams that are consumed at the same rate by parallel indexers. The pipelined strategy is implemented on a single multicore CPU as well as on a cluster of such nodes. We were able to achieve a throughput of more than 262MB/s on the ClueWeb09 dataset on a single node. On a cluster of 32 nodes, our experimental results show scalable performance using different metrics, significantly improving on prior published results. On the other hand, we develop a new approach for handling time-evolving documents using additional small temporal indexing structures. The lifetime of the collection is partitioned into multiple time windows, which guarantees a very fast temporal query response time at a small space overhead relative to the non-temporal case. Extensive experimental results indicate that the overhead in both indexing and querying is small in this more complicated case, and the query performance can indeed be improved using finer temporal partitioning of the collection. Finally, we employ GPUs to accelerate the indexing process for building inverted files and to develop a very fast algorithm for the highly irregular list ranking problem. For the indexing problem, the workload is split between CPUs and GPUs in such a way that the strengths of both architectures are exploited. For the list ranking problem involved in the decompression of inverted files, an optimized GPU algorithm is introduced by reducing the problem to a large number of fine grain computations in such a way that the processing cost per element is shown to be close to the best possible

Latent Semantic Indexing (LSI) Based Distributed System and Search On Encrypted Data

Latent semantic indexing (LSI) was initially introduced to overcome the issues of synonymy and polysemy of the traditional vector space model (VSM). LSI, however, has challenges of its own, mainly scalability. Despite being introduced in 1990, there are few attempts that provide an efficient solution for LSI, most of the literature is focuses on LSI’s applications rather than improving the original algorithm. In this work we analyze the first framework to provide scalable implementation of LSI and report its performance on the distributed environment of RAAD. The possibility of adopting LSI in the field of searching over encrypted data is also investigated. The importance of that field is stemmed from the need for cloud computing as an effective computing paradigm that provides an affordable access to high computational power. Encryption is usually applied to prevent unauthorized access to the data (the host is assumed to be curious), however this limits accessibility to the data given that search over encryption is yet to catch with the latest techniques adopted by the Information Retrieval (IR) community. In this work we propose a system that uses LSI for indexing and free-query text for retrieving. The results show that the available LSI framework does scale on large datasets, however it had some limitations with respect to factors like dictionary size and memory limit. When replicating the exact settings of the baseline on RAAD, it performed relatively slower. This could be resulted by the fact that RAAD uses a distributed file system or because of network latency. The results also show that the proposed system for applying LSI on encrypted data retrieved documents in the same order as the baseline (unencrypted data)

Automatic skeleton-driven performance optimizations for transactional memory

The recent shift toward multi -core chips has pushed the burden of extracting performance to the programmer. In fact, programmers now have to be able to uncover more coarse -grain parallelism with every new generation of processors, or the performance of their applications will remain roughly the same or even degrade. Unfortunately, parallel programming is still hard and error prone. This has driven the development of many new parallel programming models that aim to make this process efficient.This thesis first combines the skeleton -based and transactional memory programming models in a new framework, called OpenSkel, in order to improve performance and programmability of parallel applications. This framework provides a single skeleton that allows the implementation of transactional worklist applications. Skeleton or pattern-based programming allows parallel programs to be expressed as specialized instances of generic communication and computation patterns. This leaves the programmer with only the implementation of the particular operations required to solve the problem at hand. Thus, this programming approach simplifies parallel programming by eliminating some of the major challenges of parallel programming, namely thread communication, scheduling and orchestration. However, the application programmer has still to correctly synchronize threads on data races. This commonly requires the use of locks to guarantee atomic access to shared data. In particular, lock programming is vulnerable to deadlocks and also limits coarse grain parallelism by blocking threads that could be potentially executed in parallel.Transactional Memory (TM) thus emerges as an attractive alternative model to simplify parallel programming by removing this burden of handling data races explicitly. This model allows programmers to write parallel code as transactions, which are then guaranteed by the runtime system to execute atomically and in isolation regardless of eventual data races. TM programming thus frees the application from deadlocks and enables the exploitation of coarse grain parallelism when transactions do not conflict very often. Nevertheless, thread management and orchestration are left for the application programmer. Fortunately, this can be naturally handled by a skeleton framework. This fact makes the combination of skeleton -based and transactional programming a natural step to improve programmability since these models complement each other. In fact, this combination releases the application programmer from dealing with thread management and data races, and also inherits the performance improvements of both models. In addition to it, a skeleton framework is also amenable to skeleton - driven iii performance optimizations that exploits the application pattern and system information.This thesis thus also presents a set of pattern- oriented optimizations that are automatically selected and applied in a significant subset of transactional memory applications that shares a common pattern called worklist. These optimizations exploit the knowledge about the worklist pattern and the TM nature of the applications to avoid transaction conflicts, to prefetch data, to reduce contention etc. Using a novel autotuning mechanism, OpenSkel dynamically selects the most suitable set of these patternoriented performance optimizations for each application and adjusts them accordingly. Experimental results on a subset of five applications from the STAMP benchmark suite show that the proposed autotuning mechanism can achieve performance improvements within 2 %, on average, of a static oracle for a 16 -core UMA (Uniform Memory Access) platform and surpasses it by 7% on average for a 32 -core NUMA (Non -Uniform Memory Access) platform.Finally, this thesis also investigates skeleton -driven system- oriented performance optimizations such as thread mapping and memory page allocation. In order to do it, the OpenSkel system and also the autotuning mechanism are extended to accommodate these optimizations. The conducted experimental results on a subset of five applications from the STAMP benchmark show that the OpenSkel framework with the extended autotuning mechanism driving both pattern and system- oriented optimizations can achieve performance improvements of up to 88 %, with an average of 46 %, over a baseline version for a 16 -core UMA platform and up to 162 %, with an average of 91 %, for a 32 -core NUMA platform

On-premise containerized, light-weight software solutions for Biomedicine

Bioinformatics software systems are critical tools for analysing large-scale biological data, but their design and implementation can be challenging due to the need for reliability, scalability, and performance. This thesis investigates the impact of several software approaches on the design and implementation of bioinformatics software systems. These approaches include software patterns, microservices, distributed computing, containerisation and container orchestration. The research focuses on understanding how these techniques affect bioinformatics software systems’ reliability, scalability, performance, and efficiency. Furthermore, this research highlights the challenges and considerations involved in their implementation. This study also examines potential solutions for implementing container orchestration in bioinformatics research teams with limited resources and the challenges of using container orchestration. Additionally, the thesis considers microservices and distributed computing and how these can be optimised in the design and implementation process to enhance the productivity and performance of bioinformatics software systems. The research was conducted using a combination of software development, experimentation, and evaluation. The results show that implementing software patterns can significantly improve the code accessibility and structure of bioinformatics software systems. Specifically, microservices and containerisation also enhanced system reliability, scalability, and performance. Additionally, the study indicates that adopting advanced software engineering practices, such as model-driven design and container orchestration, can facilitate efficient and productive deployment and management of bioinformatics software systems, even for researchers with limited resources. Overall, we develop a software system integrating all our findings. Our proposed system demonstrated the ability to address challenges in bioinformatics. The thesis makes several key contributions in addressing the research questions surrounding the design, implementation, and optimisation of bioinformatics software systems using software patterns, microservices, containerisation, and advanced software engineering principles and practices. Our findings suggest that incorporating these technologies can significantly improve bioinformatics software systems’ reliability, scalability, performance, efficiency, and productivity.Bioinformatische Software-Systeme stellen bedeutende Werkzeuge für die Analyse umfangreicher biologischer Daten dar. Ihre Entwicklung und Implementierung kann jedoch aufgrund der erforderlichen Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit eine Herausforderung darstellen. Das Ziel dieser Arbeit ist es, die Auswirkungen von Software-Mustern, Microservices, verteilten Systemen, Containerisierung und Container-Orchestrierung auf die Architektur und Implementierung von bioinformatischen Software-Systemen zu untersuchen. Die Forschung konzentriert sich darauf, zu verstehen, wie sich diese Techniken auf die Zuverlässigkeit, Skalierbarkeit, Leistungsfähigkeit und Effizienz von bioinformatischen Software-Systemen auswirken und welche Herausforderungen mit ihrer Konzeptualisierungen und Implementierung verbunden sind. Diese Arbeit untersucht auch potenzielle Lösungen zur Implementierung von Container-Orchestrierung in bioinformatischen Forschungsteams mit begrenzten Ressourcen und die Einschränkungen bei deren Verwendung in diesem Kontext. Des Weiteren werden die Schlüsselfaktoren, die den Erfolg von bioinformatischen Software-Systemen mit Containerisierung, Microservices und verteiltem Computing beeinflussen, untersucht und wie diese im Design- und Implementierungsprozess optimiert werden können, um die Produktivität und Leistung bioinformatischer Software-Systeme zu steigern. Die vorliegende Arbeit wurde mittels einer Kombination aus Software-Entwicklung, Experimenten und Evaluation durchgeführt. Die erzielten Ergebnisse zeigen, dass die Implementierung von Software-Mustern, die Zuverlässigkeit und Skalierbarkeit von bioinformatischen Software-Systemen erheblich verbessern kann. Der Einsatz von Microservices und Containerisierung trug ebenfalls zur Steigerung der Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit des Systems bei. Darüber hinaus legt die Arbeit dar, dass die Anwendung von SoftwareEngineering-Praktiken, wie modellgesteuertem Design und Container-Orchestrierung, die effiziente und produktive Bereitstellung und Verwaltung von bioinformatischen Software-Systemen erleichtern kann. Zudem löst die Implementierung dieses SoftwareSystems, Herausforderungen für Forschungsgruppen mit begrenzten Ressourcen. Insgesamt hat das System gezeigt, dass es in der Lage ist, Herausforderungen im Bereich der Bioinformatik zu bewältigen und stellt somit ein wertvolles Werkzeug für Forscher in diesem Bereich dar. Die vorliegende Arbeit leistet mehrere wichtige Beiträge zur Beantwortung von Forschungsfragen im Zusammenhang mit dem Entwurf, der Implementierung und der Optimierung von Software-Systemen für die Bioinformatik unter Verwendung von Prinzipien und Praktiken der Softwaretechnik. Unsere Ergebnisse deuten darauf hin, dass die Einbindung dieser Technologien die Zuverlässigkeit, Skalierbarkeit, Leistungsfähigkeit, Effizienz und Produktivität bioinformatischer Software-Systeme erheblich verbessern kann