27,294 research outputs found
A new tool for the performance analysis of massively parallel computer systems
We present a new tool, GPA, that can generate key performance measures for
very large systems. Based on solving systems of ordinary differential equations
(ODEs), this method of performance analysis is far more scalable than
stochastic simulation. The GPA tool is the first to produce higher moment
analysis from differential equation approximation, which is essential, in many
cases, to obtain an accurate performance prediction. We identify so-called
switch points as the source of error in the ODE approximation. We investigate
the switch point behaviour in several large models and observe that as the
scale of the model is increased, in general the ODE performance prediction
improves in accuracy. In the case of the variance measure, we are able to
justify theoretically that in the limit of model scale, the ODE approximation
can be expected to tend to the actual variance of the model
Web Services: A Process Algebra Approach
It is now well-admitted that formal methods are helpful for many issues
raised in the Web service area. In this paper we present a framework for the
design and verification of WSs using process algebras and their tools. We
define a two-way mapping between abstract specifications written using these
calculi and executable Web services written in BPEL4WS. Several choices are
available: design and correct errors in BPEL4WS, using process algebra
verification tools, or design and correct in process algebra and automatically
obtaining the corresponding BPEL4WS code. The approaches can be combined.
Process algebra are not useful only for temporal logic verification: we remark
the use of simulation/bisimulation both for verification and for the
hierarchical refinement design method. It is worth noting that our approach
allows the use of any process algebra depending on the needs of the user at
different levels (expressiveness, existence of reasoning tools, user
expertise)
Assessing the association between pre-course metrics of student preparation and student performance in introductory statistics: Results from early data on simulation-based inference vs. nonsimulation based inference
The recent simulation-based inference (SBI) movement in algebra-based
introductory statistics courses (Stat 101) has provided preliminary evidence of
improved student conceptual understanding and retention. However, little is
known about whether these positive effects are preferentially distributed
across types of students entering the course. We consider how two metrics of
Stat 101 student preparation (pre-course performance on concept inventory and
math ACT score) may or may not be associated with end of course student
performance on conceptual inventories. Students across all preparation levels
tended to show improvement in Stat 101, but more improvement was observed
across all student preparation levels in early versions of a SBI course.
Furthermore, students' gains tended to be similar regardless of whether
students entered the course with more preparation or less. Recent data on a
sample of students using a current version of an SBI course showed similar
results, though direct comparison with non-SBI students was not possible.
Overall, our analysis provides additional evidence that SBI curricula are
effective at improving students' conceptual understanding of statistical ideas
post-course regardless student preparation. Further work is needed to better
understand nuances of student improvement based on other student demographics,
prior coursework, as well as instructor and institutional variables.Comment: 16 page
Hybrid performance modelling of opportunistic networks
We demonstrate the modelling of opportunistic networks using the process
algebra stochastic HYPE. Network traffic is modelled as continuous flows,
contact between nodes in the network is modelled stochastically, and
instantaneous decisions are modelled as discrete events. Our model describes a
network of stationary video sensors with a mobile ferry which collects data
from the sensors and delivers it to the base station. We consider different
mobility models and different buffer sizes for the ferries. This case study
illustrates the flexibility and expressive power of stochastic HYPE. We also
discuss the software that enables us to describe stochastic HYPE models and
simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055
Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA
For a long time biologists have used visual representations of biochemical
networks to gain a quick overview of important structural properties. Recently
SBGN, the Systems Biology Graphical Notation, has been developed to standardise
the way in which such graphical maps are drawn in order to facilitate the
exchange of information. Its qualitative Process Diagrams (SBGN-PD) are based
on an implicit Process Flow Abstraction (PFA) that can also be used to
construct quantitative representations, which can be used for automated
analyses of the system. Here we explicitly describe the PFA that underpins
SBGN-PD and define attributes for SBGN-PD glyphs that make it possible to
capture the quantitative details of a biochemical reaction network. We
implemented SBGNtext2BioPEPA, a tool that demonstrates how such quantitative
details can be used to automatically generate working Bio-PEPA code from a
textual representation of SBGN-PD that we developed. Bio-PEPA is a process
algebra that was designed for implementing quantitative models of concurrent
biochemical reaction systems. We use this approach to compute the expected
delay between input and output using deterministic and stochastic simulations
of the MAPK signal transduction cascade. The scheme developed here is general
and can be easily adapted to other output formalisms
Computational Modeling for the Activation Cycle of G-proteins by G-protein-coupled Receptors
In this paper, we survey five different computational modeling methods. For
comparison, we use the activation cycle of G-proteins that regulate cellular
signaling events downstream of G-protein-coupled receptors (GPCRs) as a driving
example. Starting from an existing Ordinary Differential Equations (ODEs)
model, we implement the G-protein cycle in the stochastic Pi-calculus using
SPiM, as Petri-nets using Cell Illustrator, in the Kappa Language using
Cellucidate, and in Bio-PEPA using the Bio-PEPA eclipse plug in. We also
provide a high-level notation to abstract away from communication primitives
that may be unfamiliar to the average biologist, and we show how to translate
high-level programs into stochastic Pi-calculus processes and chemical
reactions.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
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