402 research outputs found
GPGPU microbenchmarking for irregular application optimization
Irregular applications, such as unstructured mesh operations, do not easily map onto the typical GPU programming paradigms endorsed by GPU manufacturers, which mostly focus on maximizing concurrency for latency hiding. In this work, we show how alternative techniques focused on latency amortization can be used to control overall latency while requiring less concurrency. We used a custom-built microbenchmarking framework to test several GPU kernels and show how the GPU behaves under relevant workloads. We demonstrate that coalescing is not required for efficacious performance; an uncoalesced access pattern can achieve high bandwidth - even over 80% of the theoretical global memory bandwidth in certain circumstances. We also make other further observations on specific relevant behaviors of GPUs. We hope that this study opens the door for further investigation into techniques that can exploit latency amortization when latency hiding does not achieve sufficient performance
A Formal Account of the Open Provenance Model
On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The Open Provenance Model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations, and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present paper extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The paper concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20
Pages, 1 Figur
Context-Aware Personalized Activity Modeling in Concurrent Environment
Activity recognition, having endemic impact on smart homes, faces one of the biggest challenges in learning a personalized activity model completely by using a generic model especially for parallel and interleaved activities. Furthermore, inhabitant’s mistaken object interaction may entail in another spurious activity at smart homes. Identifying and removing such spurious activities is another challenging task. Knowledge driven techniques used for recognizing activity models are static in nature, lack contextual representation and may not comprehend spurious actions for parallel/interleaved activities. In this paper, a novel approach for completing the personalized model specific to each inhabitant at smart homes using generic model (incomplete) is presented that can recognize the sequential, parallel, and interleaved activities dynamically while removing the spurious activities semantically. A comprehensive set of experiments and results based upon number of correct (true positivity) or incorrect (false negativity) recognition of activities assert effectiveness of presented approach within a smart hom
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of
entities and relations of a given graph by mapping them into a vector space.
Although the majority of these methods focus on static knowledge graphs, a
large number of publicly available KGs contain temporal information stating the
time instant/period over which a certain fact has been true. Such graphs are
often known as temporal knowledge graphs. Furthermore, knowledge graphs may
also contain textual descriptions of entities and relations. Both temporal
information and textual descriptions are not taken into account during
representation learning by static KGC methods, and only structural information
of the graph is leveraged. Recently, some studies have used temporal
information to improve link prediction, yet they do not exploit textual
descriptions and do not support inductive inference (prediction on entities
that have not been seen in training).
We propose a novel framework called TEMT that exploits the power of
pre-trained language models (PLMs) for text-enhanced temporal knowledge graph
completion. The knowledge stored in the parameters of a PLM allows TEMT to
produce rich semantic representations of facts and to generalize on previously
unseen entities. TEMT leverages textual and temporal information available in a
KG, treats them separately, and fuses them to get plausibility scores of facts.
Unlike previous approaches, TEMT effectively captures dependencies across
different time points and enables predictions on unseen entities. To assess the
performance of TEMT, we carried out several experiments including time interval
prediction, both in transductive and inductive settings, and triple
classification. The experimental results show that TEMT is competitive with the
state-of-the-art.Comment: 10 pages, 3 figure
Assise: Performance and Availability via NVM Colocation in a Distributed File System
The adoption of very low latency persistent memory modules (PMMs) upends the
long-established model of disaggregated file system access. Instead, by
colocating computation and PMM storage, we can provide applications much higher
I/O performance, sub-second application failover, and strong consistency. To
demonstrate this, we built the Assise distributed file system, based on a
persistent, replicated coherence protocol for managing a set of
server-colocated PMMs as a fast, crash-recoverable cache between applications
and slower disaggregated storage, such as SSDs. Unlike disaggregated file
systems, Assise maximizes locality for all file IO by carrying out IO on
colocated PMM whenever possible and minimizes coherence overhead by maintaining
consistency at IO operation granularity, rather than at fixed block sizes.
We compare Assise to Ceph/Bluestore, NFS, and Octopus on a cluster with Intel
Optane DC PMMs and SSDs for common cloud applications and benchmarks, such as
LevelDB, Postfix, and FileBench. We find that Assise improves write latency up
to 22x, throughput up to 56x, fail-over time up to 103x, and scales up to 6x
better than its counterparts, while providing stronger consistency semantics.
Assise promises to beat the MinuteSort world record by 1.5x
Distinguishing Provenance Equivalence of Earth Science Data
Reproducibility of scientific research relies on accurate and precise citation of data and the provenance of that data. Earth science data are often the result of applying complex data transformation and analysis workflows to vast quantities of data. Provenance information of data processing is used for a variety of purposes, including understanding the process and auditing as well as reproducibility. Certain provenance information is essential for producing scientifically equivalent data. Capturing and representing that provenance information and assigning identifiers suitable for precisely distinguishing data granules and datasets is needed for accurate comparisons. This paper discusses scientific equivalence and essential provenance for scientific reproducibility. We use the example of an operational earth science data processing system to illustrate the application of the technique of cascading digital signatures or hash chains to precisely identify sets of granules and as provenance equivalence identifiers to distinguish data made in an an equivalent manner
Indexing methods for web archives
There have been numerous efforts recently to digitize previously published content and preserving born-digital content leading to the widespread growth of large text reposi- tories. Web archives are such continuously growing text collections which contain ver- sions of documents spanning over long time periods. Web archives present many op- portunities for historical, cultural and political analyses. Consequently there is a grow- ing need for tools which can efficiently access and search them.
In this work, we are interested in indexing methods for supporting text-search work- loads over web archives like time-travel queries and phrase queries. To this end we make the following contributions:
• Time-travel queries are keyword queries with a temporal predicate, e.g., “mpii saarland” @ [06/2009], which return versions of documents in the past. We in- troduce a novel index organization strategy, called index sharding, for efficiently supporting time-travel queries without incurring additional index-size blowup. We also propose index-maintenance approaches which scale to such continuously growing collections.
• We develop query-optimization techniques for time-travel queries called partition selection which maximizes recall at any given query-execution stage.
• We propose indexing methods to support phrase queries, e.g., “to be or not to be that is the question”. We index multi-word sequences and devise novel query- optimization methods over the indexed sequences to efficiently answer phrase queries.
We demonstrate the superior performance of our approaches over existing methods by extensive experimentation on real-world web archives.In der jüngsten Vergangenheit gab es zahlreiche Bemühungen zuvor veröffentlichte Inhalte zu digitalisieren und elektronisch erstellte Inhalte zu erhalten. Dies führte zu einem weit verbreitenden Anstieg großer Textdatenbestände. Webarchive sind eine solche Art konstant ansteigender Textdatensammlung. Sie enthalten mehrere Versionen von Dokumenten, welche sich über längere Zeiträume erstrecken. Darüber hinaus bieten sie viele Möglichkeiten für historische, kulturelle und politische Analysen. Infolgedessen gibt es einen wachsenden Bedarf an Werkzeugen, die eine effiziente Suche in Webarchiven und einen effizienten Zugriff auf die Daten erlauben.
Der Fokus dieser Arbeit liegt auf Indexierungsverfahren, um die Arbeitslast von Textsuche auf Webarchiven zu unterstützen, wie zum Beispiel time-travel queries oder phrase queries. Zu diesem Zweck leisten wir folgende Beiträge:
• Time-travel queries sind Suchwortanfragen mit einem temporalen Prädikat. Zum Beispiel liefert die Anfrage “mpii saarland” @ [06/2009] Versionen des Dokuments aus der Vergangenheit als Ergebnis. Zur effizienten Unterstützung solcher Anfragen ohne die Indexgröße aufzublasen, stellen wir eine neue Strategie zur Organisation von Indizes dar, so genanntes index sharding. Des Weiteren schlagen wir Wartungsverfahren für Indizes vor, die für solch konstant wachsende Datensätze skalieren.
• WirentwickelnTechnikenzurAnfrageoptimierungvontime-travelqueries, nachstehend partition selection genannt. Diese maximieren den Recall in jeder Phase der Anfrageverarbeitung.
• Wir stellen Indexierungsmethoden vor, die phrase queries unterstützen, z. B. “Sein oder Nichtsein, das ist hier die Frage”. Wir indexieren Sequenzen bestehend aus mehreren Wörtern und entwerfen neue Optimierungsverfahren für die indexierten Sequenzen, um phrase queries effizient zu beantworten. Die Performanz dieser Verfahren wird anhand von ausführlichen Experimenten auf realen Webarchiven demonstriert
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