7,649 research outputs found
A Concurrency-Agnostic Protocol for Multi-Paradigm Concurrent Debugging Tools
Today's complex software systems combine high-level concurrency models. Each
model is used to solve a specific set of problems. Unfortunately, debuggers
support only the low-level notions of threads and shared memory, forcing
developers to reason about these notions instead of the high-level concurrency
models they chose.
This paper proposes a concurrency-agnostic debugger protocol that decouples
the debugger from the concurrency models employed by the target application. As
a result, the underlying language runtime can define custom breakpoints,
stepping operations, and execution events for each concurrency model it
supports, and a debugger can expose them without having to be specifically
adapted.
We evaluated the generality of the protocol by applying it to SOMns, a
Newspeak implementation, which supports a diversity of concurrency models
including communicating sequential processes, communicating event loops,
threads and locks, fork/join parallelism, and software transactional memory. We
implemented 21 breakpoints and 20 stepping operations for these concurrency
models. For none of these, the debugger needed to be changed. Furthermore, we
visualize all concurrent interactions independently of a specific concurrency
model. To show that tooling for a specific concurrency model is possible, we
visualize actor turns and message sends separately.Comment: International Symposium on Dynamic Language
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
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Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization
In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with
a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved
DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives
We present a new parallel algorithm for probabilistic graphical model
optimization. The algorithm relies on data-parallel primitives (DPPs), which
provide portable performance over hardware architecture. We evaluate results on
CPUs and GPUs for an image segmentation problem. Compared to a serial baseline,
we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare
our performance to a reference, OpenMP-based algorithm, and find speedups of up
to 7X (CPU).Comment: LDAV 2018, October 201
Mining structured Petri nets for the visualization of process behavior
Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.Peer ReviewedPostprint (author's final draft
Using High-Rising Cities to Visualize Performance in Real-Time
For developers concerned with a performance drop or improvement in their
software, a profiler allows a developer to quickly search and identify
bottlenecks and leaks that consume much execution time. Non real-time profilers
analyze the history of already executed stack traces, while a real-time
profiler outputs the results concurrently with the execution of software, so
users can know the results instantaneously. However, a real-time profiler risks
providing overly large and complex outputs, which is difficult for developers
to quickly analyze. In this paper, we visualize the performance data from a
real-time profiler. We visualize program execution as a three-dimensional (3D)
city, representing the structure of the program as artifacts in a city (i.e.,
classes and packages expressed as buildings and districts) and their program
executions expressed as the fluctuating height of artifacts. Through two case
studies and using a prototype of our proposed visualization, we demonstrate how
our visualization can easily identify performance issues such as a memory leak
and compare performance changes between versions of a program. A demonstration
of the interactive features of our prototype is available at
https://youtu.be/eleVo19Hp4k.Comment: 10 pages, VISSOFT 2017, Artifact:
https://github.com/sefield/high-rising-city-artifac
Benefits of Session Types for software Development
Session types are a formalism used to specify and check the correctness of communication based systems. Within their scope, they can guarantee the absence of communication errors such as deadlock, sending an unexpected message or failing to handle an incoming message. Introduced over two decades ago, they have developed into a significant theme in programming languages. In this paper we examine the beliefs that drive research into this area and make it popular. We look at the claims and motivation behind session types throughout the literature. We identify the hypotheses upon which session types have been designed and implemented, and attempt to clarify and formulate them in a more suitable manner for testing
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A survey of simulation techniques in commerce and defence
Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches
TAPAs: A Tool for the Analysis of Process Algebras
Process algebras are formalisms for modelling concurrent systems that permit mathematical reasoning with respect to a set of desired properties. TAPAs is a tool that can be used to support the use of process algebras to specify and analyze concurrent systems. It does not aim at guaranteeing high performances, but has been developed as a support to teaching. Systems are described as process algebras terms that are then mapped to labelled transition systems (LTSs). Properties are verified either by checking equivalence of concrete and abstract systems descriptions, or by model checking temporal formulae over the obtained LTS. A key feature of TAPAs, that makes it particularly suitable for teaching, is that it maintains a consistent double representation of each system both as a term and as a graph. Another useful didactical feature is the exhibition of counterexamples in case equivalences are not verified or the proposed formulae are not satisfied
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