High-performance computing (HPC) systems generate huge amounts of metadata about different entities such as jobs, users, and files. Existing systems can efficiently record and manage part of these metadata, mainly the POSIX metadata of data files (e.g., file size, name, and permissions mode). But another important set of metadata, referred to as “rich” metadata in this study, which record not only wider range of entities (e.g., running processes and jobs) but also more complex relationships between them, are mostly missing in current HPC systems. Yet such rich metadata are critical for supporting many advanced data management functions such as identifying data sources and parameters behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. To uniformly and efficiently manage the rich metadata generated in HPC systems, We propose to utilize a graph model in this study. We identify the key challenges of implementing such a graph-based HPC rich metadata management system and present GraphMeta, a graph-based rich metadata management system designed and optimized for HPC platforms, to tackle these challenges. Extensive evaluations on both synthetic and real HPC metadata workloads show its advantages in both performance and scalability compared with existing solutions.

Today, computing on various Android devices is pervasive. However, growing security vulnerabilities and attacks in the Android ecosystem constitute various threats through user apps. Taint analysis is a common technique for defending against these threats, yet it suffers from challenges in attaining practical simultaneous scalability and effectiveness. This paper presents a novel approach to fast and precise taint checking, called incremental taint analysis, by exploiting the evolving nature of Android apps. The analysis narrows down the search space of taint checking from an entire app, as conventionally addressed, to the parts of the program that are different from its previous versions. This technique improves the overall efficiency of checking multiple versions of the app as it evolves. We have implemented the techniques as a tool prototype, EvoTaint, and evaluated our analysis by applying it to real-world evolving Android apps. Our preliminary results show that the incremental approach largely reduced the cost of taint analysis, by 78.6% on average, yet without sacrificing the analysis effectiveness, relative to a representative precise taint analysis as the baseline.

Provenance describes detailed information about the history of a piece of data, containing the relationships among elements such as users, processes, jobs, and workflows that contribute to the existence of data. Provenance is key to supporting many data management functionalities that are increasingly important in operations such as identifying data sources, parameters, or assumptions behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. Despite its importance, however, provenance support is largely underdeveloped in highly parallel architectures and systems. One major challenge is the demanding requirements of providing provenance service in situ. The need to remain lightweight and to be always on often conflicts with the need to be transparent and offer an accurate catalog of details regarding the applications and systems. To tackle this challenge, we introduce a lightweight provenance service, called LPS, for high-performance computing (HPC) systems. LPS leverages a kernel instrument mechanism to achieve transparency and introduces representative execution and flexible granularity to capture comprehensive provenance with controllable overhead. Extensive evaluations and use cases have confirmed its efficiency and usability. We believe that LPS can be integrated into current and future HPC systems to support a variety of data management needs.

Inter-component communication (ICC) serves as a key element of any Android app's implementation. Specifically, an Android app uses Intents as the main mechanism for ICC to complete tasks such as switching between different user interfaces, starting background services, communicating to other apps on the Android device, and saving or retrieving data from device storage. Thus, dissecting how an app uses ICCs to accomplish its tasks is fundamental to understanding the app's underlying behaviors. Existing works on Android ICCs focus on resolving Intents and/or mapping ICCs for security analysis purposes. While the ICC analysis result is potentially informative, it is difficult to digest on its own and has not been utilized for app/ICC comprehension. Also, the result is based on static analysis thus does not inform of run-time app behaviors due to ICCs. We propose the first approach to dissecting Android ICCs via interactive, dynamic visualizations empowered by static and dynamic ICC analysis combined. Through multiple semantically linked views and in-situ interaction features, our approach enables real-time visual explorations of ICCs as they are triggered by user inputs to the app under analysis. It conveys rich ICC information while managing limited visual space through various visualization design strategies. Our case studies with a number of most commonly used apps have showed promising merits of the approach for both deep ICC comprehension and security vulnerability inspection, as well as practical scalability. Our tool prototype of the approach has enabled quick revelation of inefficient, intrusive, and malicious behaviors in several popular apps that were normally hidden to users.

High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes but also from increasingly diverse metadata, which contains data provenance and user-defined attributes in addition to traditional POSIX metadata. This "rich" metadata is critical to support many advanced data management functionality such as data auditing and validation. In our prior work, we presented a graph-based model that could be a promising solution to uniformly manage such rich metadata because of its flexibility and generality. At the same time, however, graph-based rich metadata management introduces significant challenges. In this study, we first identify the challenges presented by the underlying infrastructure in supporting scalable, high-performance rich metadata management. To tackle these challenges, we then present GraphMeta, a graph-based engine designed for managing large-scale rich metadata. We also utilize a series of optimizations designed for rich metadata graphs. We evaluate GraphMeta with both synthetic and real HPC metadata workloads and compare it with other approaches. The results show that its advantages in terms of rich metadata management in HPC systems, including better performance and scalability compared with existing solutions.

Network contention between concurrently running jobs on HPC systems is a primary cause of performance variability. Optimizing job allocation and avoiding network sharing are hence crucial to alleviate the potential performance degradation. In order to do so effectively, an understanding of the interference among concurrently running jobs, their communication patterns, and contention in the network is required. In this work, we choose three representative HPC applications from the DOE Design Forward Project and conduct detailed simulations on a torus network model to analyze both intra-and interjob interference. By scrutinizing the communication behaviors of these applications, we identify relationships between these behaviors and the possible interference introduced by different job placement policies. Our analyses illuminate a path toward communication pattern awareness in job placement on HPC systems.

Distributed object storage architectures have become the de facto standard for high-performance storage in big data, cloud, and HPC computing. Object storage deployments using commodity hardware to reduce costs often employ object replication as a method to achieve data resilience. Repairing object replicas after failure is a daunting task for systems with thousands of servers and billions of objects, however, and it is increasingly difficult to evaluate such scenarios at scale on real-world systems. Resilience and availability are both compromised if objects are not repaired in a timely manner. In this work we leverage a high-fidelity discrete-event simulation model to investigate replica reconstruction on large-scale object storage systems with thousands of servers, billions of objects, and petabytes of data. We evaluate the behavior of CRUSH, a well-known object placement algorithm, and identify configuration scenarios in which aggregate rebuild performance is constrained by object placement policies. After determining the root cause of this bottleneck, we then propose enhancements to CRUSH and the usage policies atop it to enable scalable replica reconstruction. We use these methods to demonstrate a simulated aggregate rebuild rate of 410 GiB/s (within 5% of projected ideal linear scaling) on a 1,024-node commodity storage system. We also uncover an unexpected phenomenon in rebuild performance based on the characteristics of the data stored on the system.

As the cost of DNA sequencing has decreased, computational biology data processing platforms are experiencing an increasingly large volume of data analysis requests. The metagenomics analysis server MG-RAST at Argonne National Laboratory, a computational biology data processing platform, is receiving several terabytes of data submissions per month. However, MG-RAST currently relies on a central object-based data store, Shock, for data access and storage that can become a bottleneck under high data transfer loads, adversely affecting the job response time for end users. In this work, we use a discrete-event simulation approach to explore the use of data proxies and an enhanced, proxy-aware scheduling methodology designed to reduce the movement of the intermediate data generated during workflow processing. In this approach, Shock is supplemented with proxy storage servers, employing solid state drives, to decentralize the management and hence reduce the movement of intermediate workflow results. Discrete-event simulation provides a way to evaluate the performance of MG-RAST with increased workloads without disrupting the production system. For our case study, we extrapolate scientific workflows obtained from MG-RAST to represent future usage trends. We demonstrate that the addition of proxies and the proxy-aware scheduling methodology significantly reduces the data movement overhead by distributing the data plane, leading to substantial improvement in end-user job response time.

High-radix, low-diameter dragonfly networks will be a common choice in next-generation supercomputers. Preliminary studies show that random job placement with adaptive routing should be the rule of thumb to utilize such networks, since it uniformly distributes traffic and alleviates congestion. Nevertheless, in this work we find that while random job placement coupled with adaptive routing is good at load balancing network traffic, it cannot guarantee the best performance for every job. The performance improvement of communication-intensive applications comes at the expense of performance degradation of less intensive ones. We identify this bully behavior and validate its underlying causes with the help of detailed network simulation and real application traces. We further investigate a hybrid contiguous-noncontiguous job placement policy as an alternative. Initial experimentation shows that hybrid job placement aids in reducing the worst-case performance degradation for less communication-intensive applications while retaining the performance of communication-intensive ones.

Property graphs are a promising data model for rich metadata management in high-performance computing (HPC) systems because of their ability to represent not only metadata attributes but also the relationships between them. A property graph can be used to record the relationships between users, jobs, and data, for example, with unique annotations for each entity. This high-volume, power-law distributed use case is a natural fit for an out-of-core distributed property graph database. Such a system must support live updates (to ingest production information in real time), low-latency point queries (for frequent metadata operations such as permission checking), and large-scale traversals (for provenance data mining). Large-scale property graph traversals are particularly challenging for distributed graph databases, however. Most existing graph databases implement a "level-synchronous" breadth-first search algorithm that relies on global synchronization in each traversal step. This traversal model performs well in many problem domains, but a rich metadata management system is characterized by imbalanced graphs, long traversal lengths, and concurrent workloads, each of which has the potential to introduce or exacerbate stragglers. We define stragglers as abnormally slow steps (or servers) in a graph traversal that lead to low overall throughput for synchronous traversal algorithms. The straggler problem can be mitigated by the use of asynchronous traversal algorithms. Asynchronous traversal has been successfully demonstrated in graph processing frameworks, but such systems require the graph to be loaded into a separate batch-processing framework. In this work, we propose GraphTrek, a general asynchronous graph traversal engine working with graph databases for processing rich metadata management in their native format. We also outline a traversal-aware query language and key optimizations (traversal-affiliate caching and execution merging) necessary for efficient performance. Our experiments show that the asynchronous graph traversal engine is more efficient than its synchronous counterpart in the case of HPC rich metadata processing, where more servers are involved and larger traversals are needed.

Participatory sensing (PS) is a distributed data collection and analysis approach where individuals, acting alone or in groups, use their personal mobile devices to systematically explore interesting aspects of their lives and communities [Burke et al. 2006]. These mobile devices can be used to capture diverse spatiotemporal data through both intermittent self-report and continuous recording from on-board sensors and applications. Ohmage (http://ohmage.org) is a modular and extensible open-source, mobile to Web PS platform that records, stores, analyzes, and visualizes data from both prompted self-report and continuous data streams. These data streams are authorable and can dynamically be deployed in diverse settings. Feedback from hundreds of behavioral and technology researchers, focus group participants, and end users has been integrated into ohmage through an iterative participatory design process. Ohmage has been used as an enabling platform in more than 20 independent projects in many disciplines. We summarize the PS requirements, challenges and key design objectives learned through our design process, and ohmage system architecture to achieve those objectives. The flexibility, modularity, and extensibility of ohmage in supporting diverse deployment settings are presented through three distinct case studies in education, health, and clinical research.

Though an abundance of novel "data transformation" technologies have been developed (such as compression, level-of-detail, layout optimization, and indexing), there remains a notable gap in the adoption of such services by scientific applications. In response, we develop an in situ data transformation framework in the ADIOS I/O middleware with a "plug in" interface, thus greatly simplifying both the deployment and use of data transform services in scientific applications. Our approach ensures user-transparency, runtime-configurability, compatibility with existing I/O optimizations, and the potential for exploiting read-optimizing transforms (such as level-of-detail) to achieve I/O reduction. We demonstrate use of our framework with the QLG simulation at up to 8,192 cores on the leadership-class Titan supercomputer, showing negligible overhead. We also explore the read performance implications of data transforms with respect to parameters such as chunk size, access pattern, and the "opacity" of different transform methods including compression and level-of-detail.

Driven by the goals of efficient and generic communication of noncontiguous data layouts in GPU memory, for which solutions do not currently exist, we present a parallel, noncontiguous data-processing methodology through the MPI datatypes specification. Our processing algorithm utilizes a kernel on the GPU to pack arbitrary noncontiguous GPU data by enriching the datatypes encoding to expose a fine-grained, data-point level of parallelism. Additionally, the typically tree-based datatype encoding is preprocessed to enable efficient, cached access across GPU threads. Using CUDA, we show that the computational method outperforms DMA-based alternatives for several common data layouts as well as more complex data layouts for which reasonable DMA-based processing does not exist. Our method incurs low overhead for data layouts that closely match best-case DMA usage or that can be processed by layout-specific implementations. We additionally investigate usage scenarios for data packing that incur resource contention, identifying potential pitfalls for various packing strategies. We also demonstrate the efficacy of kernel-based packing in various communication scenarios, showing multifold improvement in point-to-point communication and evaluating packing within the context of the SHOC stencil benchmark and HACC mesh analysis.

Real-world dynamic systems such as physical and atmosphere-ocean systems often exhibit a hierarchical system-subsystem structure. However, the paradigm of making this hierarchical/modular structure and the rich properties they encode a “first-class citizen” of machine learning algorithms is largely absent from the literature. Furthermore, traditional data mining approaches focus on designing new classifiers or ensembles of classifiers, while there is a lack of study on detecting and correcting prediction errors of existing forecasting (or classification) algorithms. In this paper, we propose DETECTOR, a hierarchical method for detecting and correcting forecast errors by employing the whole-part relationships between the target system and non-target systems. Experimental results show that DETECTOR can successfully detect and correct forecasting errors made by state-of-art classifier ensemble techniques and traditional single classifier methods at an average rate of 22%, corresponding to a 11% average forecasting accuracy increase, in seasonal forecasting of hurricanes and landfalling hurricanes in North Atlantic and North African rainfall.

Smartphones can capture diverse spatio-temporal data about an individual; including both intermittent self-report, and continuous passive data collection from onboard sensors and applications. The resulting personal data streams can support powerful inference about the user's state, behavior, well-being and environment. However making sense and acting on these multi-dimensional, heterogeneous data streams requires iterative and intensive exploration of the datasets, and development of customized analysis techniques that are appropriate for a particular health domain.

Lack of efficient and transparent interaction with GPU data in hybrid MPI+GPU environments challenges GPU acceleration of large-scale scientific computations. A particular challenge is the transfer of noncontiguous data to and from GPU memory. MPI implementations currently do not provide an efficient means of utilizing data types for noncontiguous communication of data in GPU memory. To address this gap, we present an MPI data type-processing system capable of efficiently processing arbitrary data types directly on the GPU. We present a means for converting conventional data type representations into a GPU-amenable format. Fine-grained, element-level parallelism is then utilized by a GPU kernel to perform in-device packing and unpacking of noncontiguous elements. We demonstrate a several-fold performance improvement for noncontiguous column vectors, 3D array slices, and 4D array sub volumes over CUDA-based alternatives. Compared with optimized, layout-specific implementations, our approach incurs low overhead, while enabling the packing of data types that do not have a direct CUDA equivalent. These improvements are demonstrated to translate to significant improvements in end-to-end, GPU-to-GPU communication time. In addition, we identify and evaluate communication patterns that may cause resource contention with packing operations, providing a baseline for adaptively selecting data-processing strategies.

Current peta-scale data analytics frameworks suffer from a significant performance bottleneck due to an imbalance between their enormous computational power and limited I/O bandwidth. Using data compression schemes to reduce the amount of I/O activity is a promising approach to addressing this problem. In this paper, we propose a hybrid framework for interleaving I/O with data compression to achieve improved I/O throughput side-by-side with reduced dataset size. We evaluate several interleaving strategies, present theoretical models, and evaluate the efficiency and scalability of our approach through comparative analysis. With our theoretical model, considering 19 real-world scientific datasets both from the public domain and peta-scale simulations, we estimate that the hybrid method can result in a 12 to 46 increase in throughput on hard-to-compress scientific datasets. At the reported peak bandwidth of 60 GB/s of uncompressed data for a current, leadership-class parallel I/O system, this translates into an effective gain of 7 to 28 GB/s in aggregate throughput.

The size and scope of cutting-edge scientific simulations are growing much faster than the I/O and storage capabilities of their runtime environments. The growing gap gets exacerbated by exploratory dataâ"intensive analytics, such as querying simulation data for regions of interest with multivariate, spatio-temporal constraints. Query-driven data exploration induces heterogeneous access patterns that further stress the performance of the underlying storage system. To partially alleviate the problem, data reduction via compression and multi-resolution data extraction are becoming an integral part of I/O systems. While addressing the data size issue, these techniques introduce yet another mix of access patterns to a heterogeneous set of possibilities. Moreover, how extreme-scale datasets are partitioned into multiple files and organized on a parallel file systems augments to an already combinatorial space of possible access patterns. To address this challenge, we present MLOC, a parallel Multilevel Layout Optimization framework for Compressed scientific spatio-temporal data at extreme scale. MLOC proposes multiple fine-grained data layout optimization kernels that form a generic core from which a broader constellation of such kernels can be organically consolidated to enable an effective data exploration with various combinations of access patterns. Specifically, the kernels are optimized for access patterns induced by (a) queryâ"driven multivariate, spatio-temporal constraints, (b) precisionâ"driven data analytics, (c) compressionâ"driven data reduction, (d) multi-resolution data sampling, and (e) multiâ"file data partitioning and organization on a parallel file system. MLOC organizes these optimization kernels within a multiâ"level architecture, on which all the levels can be flexibly re-ordered by userâ"defined priorities. When tested on queryâ"driven exploration of compressed data, MLOC demonstrates a superior performance compared to any state-of-the-art scientific database management technologies.

The size and scope of cutting-edge scientific simulations are growing much faster than the I/O subsystems of their runtime environments, not only making I/O the primary bottleneck, but also consuming space that pushes the storage capacities of many computing facilities. These problems are exacerbated by the need to perform data-intensive analytics applications, such as querying the dataset by variable and spatio-temporal constraints, for what current database technologies commonly build query indices of size greater than that of the raw data. To help solve these problems, we present a parallel query-processing engine that can handle both range queries and queries with spatio-temporal constraints, on B-spline compressed data with user-controlled accuracy. Our method adapts to widening gaps between computation and I/O performance by querying on compressed metadata separated into bins by variable values, utilizing Hilbert space-filling curves to optimize for spatial constraints and aggregating data access to improve locality of per-bin stored data, reducing the false positive rate and latency bound I/O operations (such as seek) substantially. We show our method to be efficient with respect to storage, computation, and I/O compared to existing database technologies optimized for query processing on scientific data.

I/O bottlenecks in HPC applications are becoming a more pressing problem as compute capabilities continue to outpace I/O capabilities. While double-precision simulation data often must be stored losslessly, the loss of some of the fractional component may introduce acceptably small errors to many types of scientific analyses. Given this observation, we develop a precision level of detail (APLOD) library, which partitions double-precision datasets along user-defined byte boundaries. APLOD parameterizes the analysis accuracy-I/O performance tradeoff, bounds maximum relative error, maintains I/O access patterns compared to full precision, and operates with low overhead. Using ADIOS as an I/O use-case, we show proportional reduction in disk access time to the degree of precision. Finally, we show the effects of partial precision analysis on accuracy for operations such as k-means and Fourier analysis, finding a strong applicability for the use of varying degrees of precision to reduce the cost of analyzing extreme-scale data.

Efficient analytics of scientific data from extreme-scale simulations is quickly becoming a top-notch priority. The increasing simulation output data sizes demand for a paradigm shift in how analytics is conducted. In this paper, we argue that query-driven analytics over compressed---rather than original, full-size---data is a promising strategy in order to meet storage-and-I/O-bound application challenges. As a proof-of-principle, we propose a parallel query processing engine, called ISABELA-QA that is designed and optimized for knowledge priors driven analytical processing of spatio-temporal, multivariate scientific data that is initially compressed, in situ, by our ISABELA technology. With ISABELA-QA, the total data storage requirement is less than 23%-30% of the original data, which is upto eight-fold less than what the existing state-of-the-art data management technologies that require storing both the original data and the index could offer. Since ISABELA-QA operates on the metadata generated by our compression technology, its underlying indexing technology for efficient query processing is light-weight; it requires less than 3% of the original data, unlike existing database indexing approaches that require 30%-300% of the original data. Moreover, ISABELA-QA is specifically optimized to retrieve the actual values rather than spatial regions for the variables that satisfy user-specified range queries---a functionality that is critical for high-accuracy data analytics. To the best of our knowledge, this is the first techology that enables query-driven analytics over the compressed spatio-temporal floating-point double-or single-precision data, while offering a light-weight memory and disk storage footprint solution with parallel, scalable, multi-node, multi-core, GPU-based query processing.

Systems failures cause havoc everywhere. Failures may be total, partial or temporary; and examination of the subject has to consider: implemented systems, systems being enhanced and totally new projects. When failing situations arise, the aftermath is highly complicated because there are so many possible contributing factors. For many years, students of Computer Science and Software Engineering – and indeed software practitioners in general – have been able to learn about software failures from the media, at conferences and by hearing and reading general anecdotal evidence. Comparatively little research attention has been specifically devoted to the subject of systems failures and what should be done about such situations. The workshop will examine the current situation regarding what is known about failures, review current research into the subject and consider what contribution can be made to the available knowledge on the subject through the auspices of SWEBOK. In doing this account will be taken of the radical changes in thinking that have resulted from the changes to computer and telecommunications technology, the advent of the Internet as a feature of everyday life and the new economy that is emerging as e-commerce grips society. Having discussed the prevailing issues surrounding systems failures, the focus of the workshop will shift to consider how those involved in the Education and Training of software engineering, can teach and provide advice on the subject. University graduates and software practitioners are taught the rudimentary elements of “best practice” in their field, but very little is done to prepare them for the realities of life in the hot-room of software technology. The proposers have integrated course units on systems failures into their assisted-learning Global Campus which is run from Middlesex University and have introduced it into their mainstream teaching courses within the University. In doing this they are becoming able to use this as a means of assessing the best way of introducing this very complex subject in a constructive way.

When systems fail, they can cause havoc everywhere. They affect the organisations involved in creating, maintaining and using them and they can have a profound effect on the people involved, directly or indirectly. The causes of systems and project failures, vary considerably. Each case has to be taken in isolation and examined, to see where it has gone wrong in the past, or is starting to go wrong at present. In a true-life scenario, it is essential to be able to predict likely problems that may arise or accurately recognise failure symptoms when they occur. To achieve this it is important to be able to identify what is really going on and when these facts have been established, to be able to select a suitable means of handling the situation. Two European Esprit research projects examined software and multimedia quality practices and provided a framework for addressing these issues. These frameworks did not just promote best practices within software engineering, but sought to address some of the wider issues of systems and their role within the business. Middlesex University has taken this theme forward with a specific remit to address the subject of systems failures.