High performance computing in the cloud

In recent years, the interest in both scientific and business workflows has increased. A workflow is composed of a series of tools, which should be executed in a predefined order to perform an analysis. Traditionally, these workflows were executed in a manual way, sending the output of one tool to the next one in the analysis process. Many applications to execute workflows automatically, appeared recently. These applications ease the work of the users while executing their analyses. In addition, from the computational point of view, some workflows require a significant amount of resources. Consequently, workflow execution moved from single workstations to distributed environments such as Grids or Clouds. Data management and tasks scheduling are required to execute workflows in an efficient way in such environments. In this thesis, we propose a cloud-based HPC environment, focusing on tasks scheduling, resources auto-scaling, data management and simplifying the access to the resources with software clients. First, the cloud computing infrastructure is devised, which includes the base software (i.e. OpenStack) plus several additional modules aimed at improving authentication (i.e. LDAP) and data management (i.e. GridFTP, Globus Online and CloudFuse). Second, built on top of the mentioned infrastructure, the TORQUE distributed resources manager and the Maui scheduler have been configured to schedule and distribute tasks to the cloud-based workers. To reduce the number of idle nodes and the incurred cost of the active cloud resources, we also propose a configurable auto-scaling technique, which is able to scale the execution cluster depending on the workload. Additionally, in order to simplify tasks submission to the TORQUE execution cluster, we have interconnected the Galaxy workflows management system with it, therefore users benefit from a simple way to execute their tasks. Finally, we conducted an experimental evaluation, composed by a number of different studies with synthetic and real-world applications, to show the behaviour of the auto-scaled execution cluster managed by TORQUE and Maui. All experiments have been performed by using an OpenStack cloud computing environment and the benchmarked applications correspond to the benchmarking suite, which is specially designed for workflows scheduling in the cloud computing environment. Cybershake, Ligo and Montage have been the selected synthetic applications from the benchmarking suite. GECKO and a GWAS pipeline represent the real-world test use cases, both having a diverse and heterogeneous set of tasks.The numerous technological advances in data acquisition techniques allow the massive production of enormous amounts of data in diverse fields such as astronomy, health and social networks. Nowadays, only a small part of this data can be analysed because of the lack of computational resources. High Performance Computing (HPC) strategies represent the single choice to analyse such overwhelming amount of data. However, in general, HPC techniques require the use of big and expensive computing and storage infrastructures, usually not affordable or available for most users. Cloud computing, where users pay for the resources they need and when they actually need them, appears as an interesting alternative. Besides the savings in hardware infrastructure, cloud computing offers further advantages such as the removal of installation, administration and supplying requirements. In addition, it enables users to use better hardware than the one they can usually afford, scale the resources depending on their needs, and a greater fault-tolerance, amongst others. The efficient utilisation of HPC resources becomes a fundamental task, particularly in cloud computing. We need to consider the cost of using HPC resources, specially in the case of cloud-based infrastructures, where users have to pay for storing, transferring and analysing data. Therefore, it is really important the usage of generic tasks scheduling and auto-scaling techniques to efficiently exploit the computational resources. It is equally important to make these tasks user-friendly through the development of tools/applications (software clients), which act as interface between the user and the infrastructure

Adaptive structured parallelism

Algorithmic skeletons abstract commonly-used patterns of parallel computation, communication, and interaction. Parallel programs are expressed by interweaving parameterised skeletons analogously to the way in which structured sequential programs are developed, using well-defined constructs. Skeletons provide top-down design composition and control inheritance throughout the program structure. Based on the algorithmic skeleton concept, structured parallelism provides a high-level parallel programming technique which allows the conceptual description of parallel programs whilst fostering platform independence and algorithm abstraction. By decoupling the algorithm specification from machine-dependent structural considerations, structured parallelism allows programmers to code programs regardless of how the computation and communications will be executed in the system platform.Meanwhile, large non-dedicated multiprocessing systems have long posed a challenge to known distributed systems programming techniques as a result of the inherent heterogeneity and dynamism of their resources. Scant research has been devoted to the use of structural information provided by skeletons in adaptively improving program performance, based on resource utilisation. This thesis presents a methodology to improve skeletal parallel programming in heterogeneous distributed systems by introducing adaptivity through resource awareness. As we hypothesise that a skeletal program should be able to adapt to the dynamic resource conditions over time using its structural forecasting information, we have developed ASPara: Adaptive Structured Parallelism. ASPara is a generic methodology to incorporate structural information at compilation into a parallel program, which will help it to adapt at execution

The Design and Implementation of Low-Latency Prediction Serving Systems

Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints

High-performance and fault-tolerant techniques for massive data distribution in online communities

The amount of digital information produced and consumed is increasing each day. This rapid growth is motivated by the advances in computing power, hardware technologies, and the popularization of user generated content networks. New hardware is able to process larger quantities of data, which permits to obtain finer results, and as a consequence more data is generated. In this respect, scientific applications have evolved benefiting from the new hardware capabilities. This type of application is characterized by requiring large amounts of information as input, generating a significant amount of intermediate data resulting in large files. This increase not only appears in terms of volume, but also in terms of size, we need to provide methods that permit a efficient and reliable data access mechanism. Producing such a method is a challenging task due to the amount of aspects involved. However, we can leverage the knowledge found in social networks to improve the distribution process. In this respect, the advent of the Web 2.0 has popularized the concept of social network, which provides valuable knowledge about the relationships among users, and the users with the data. However, extracting the knowledge and defining ways to actively use it to increase the performance of a system remains an open research direction. Additionally, we must also take into account other existing limitations. In particular, the interconnection between different elements of the system is one of the key aspects. The availability of new technologies such as the mass-production of multicore chips, large storage media, better sensors, etc. contributed to the increase of data being produced. However, the underlying interconnection technologies have not improved with the same speed as the others. This leads to a situation where vast amounts of data can be produced and need to be consumed by a large number of geographically distributed users, but the interconnection between both ends does not match the required needs. In this thesis, we address the problem of efficient and reliable data distribution in a geographically distributed systems. In this respect, we focus on providing a solution that 1) optimizes the use of existing resources, 2) does not requires changes in the underlying interconnection, and 3) provides fault-tolerant capabilities. In order to achieve this objectives, we define a generic data distribution architecture composed of three main components: community detection module, transfer scheduling module, and distribution controller. The community detection module leverages the information found in the social network formed by the users requesting files and produces a set of virtual communities grouping entities with similar interests. The transfer scheduling module permits to produce a plan to efficiently distribute all requested files improving resource utilization. For this purpose, we model the distribution problem using linear programming and offer a method to permit a distributed solving of the problem. Finally, the distribution controller manages the distribution process using the aforementioned schedule, controls the available server infrastructure, and launches new on-demand resources when necessary

Programming and parallelising applications for distributed infrastructures

The last decade has witnessed unprecedented changes in parallel and distributed infrastructures. Due to the diminished gains in processor performance from increasing clock frequency, manufacturers have moved from uniprocessor architectures to multicores; as a result, clusters of computers have incorporated such new CPU designs. Furthermore, the ever-growing need of scienti c applications for computing and storage capabilities has motivated the appearance of grids: geographically-distributed, multi-domain infrastructures based on sharing of resources to accomplish large and complex tasks. More recently, clouds have emerged by combining virtualisation technologies, service-orientation and business models to deliver IT resources on demand over the Internet. The size and complexity of these new infrastructures poses a challenge for programmers to exploit them. On the one hand, some of the di culties are inherent to concurrent and distributed programming themselves, e.g. dealing with thread creation and synchronisation, messaging, data partitioning and transfer, etc. On the other hand, other issues are related to the singularities of each scenario, like the heterogeneity of Grid middleware and resources or the risk of vendor lock-in when writing an application for a particular Cloud provider. In the face of such a challenge, programming productivity - understood as a tradeo between programmability and performance - has become crucial for software developers. There is a strong need for high-productivity programming models and languages, which should provide simple means for writing parallel and distributed applications that can run on current infrastructures without sacri cing performance. In that sense, this thesis contributes with Java StarSs, a programming model and runtime system for developing and parallelising Java applications on distributed infrastructures. The model has two key features: first, the user programs in a fully-sequential standard-Java fashion - no parallel construct, API call or pragma must be included in the application code; second, it is completely infrastructure-unaware, i.e. programs do not contain any details about deployment or resource management, so that the same application can run in di erent infrastructures with no changes. The only requirement for the user is to select the application tasks, which are the model's unit of parallelism. Tasks can be either regular Java methods or web service operations, and they can handle any data type supported by the Java language, namely les, objects, arrays and primitives. For the sake of simplicity of the model, Java StarSs shifts the burden of parallelisation from the programmer to the runtime system. The runtime is responsible from modifying the original application to make it create asynchronous tasks and synchronise data accesses from the main program. Moreover, the implicit inter-task concurrency is automatically found as the application executes, thanks to a data dependency detection mechanism that integrates all the Java data types. This thesis provides a fairly comprehensive evaluation of Java StarSs on three di erent distributed scenarios: Grid, Cluster and Cloud. For each of them, a runtime system was designed and implemented to exploit their particular characteristics as well as to address their issues, while keeping the infrastructure unawareness of the programming model. The evaluation compares Java StarSs against state-of-the-art solutions, both in terms of programmability and performance, and demonstrates how the model can bring remarkable productivity to programmers of parallel distributed applications

Undergraduate Course Catalog of the University of San Diego 2021-2022

845 pages. Includes information about academics, expenses, campus and the college, the 2021-2022 academic calendar, and school policies.https://digital.sandiego.edu/coursecatalogs-undergrad/1029/thumbnail.jp