4,438 research outputs found
Performance analysis of knock detectors
This paper examines performance of the knock detection technique typically used in engine control systems, and the margin for possible improvement. We introduce a knock signal model and obtain an analytical result for the associated receiver operating characteristic of the standard knock detector. To show the improvement potential, we derive the theoretical upper bound of performance. A special case with unknown model parameters is also considered. Numerical results stimulate the research of improved detectors
Dynamic Performance Forecasting for Network-Enabled Servers in a Heterogeneous Environment
This paper presents a tool for dynamic forecasting of Network-Enabled Servers performance. FAST (Fast Agent's System Timer}) is a software package allowing client applications to get an accurate forecast of communicat- ion and computation times and memory use in a heterogeneous environment. It relies on low level software packages, i.e., network and host monitoring tools, and some of our developments in computation routines modeling. The FAST internals and user interface are presented and a comparison between the execution time predicted by FAST and the measured time of complex matrix multiplication executed on an heterogeneous platform is given
SimGrid: a Sustained Effort for the Versatile Simulation of Large Scale Distributed Systems
In this paper we present Simgrid, a toolkit for the versatile simulation of
large scale distributed systems, whose development effort has been sustained
for the last fifteen years. Over this time period SimGrid has evolved from a
one-laboratory project in the U.S. into a scientific instrument developed by an
international collaboration. The keys to making this evolution possible have
been securing of funding, improving the quality of the software, and increasing
the user base. In this paper we describe how we have been able to make advances
on all three fronts, on which we plan to intensify our efforts over the
upcoming years.Comment: 4 pages, submission to WSSSPE'1
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Persistence diagrams, the most common descriptors of Topological Data
Analysis, encode topological properties of data and have already proved pivotal
in many different applications of data science. However, since the (metric)
space of persistence diagrams is not Hilbert, they end up being difficult
inputs for most Machine Learning techniques. To address this concern, several
vectorization methods have been put forward that embed persistence diagrams
into either finite-dimensional Euclidean space or (implicit) infinite
dimensional Hilbert space with kernels. In this work, we focus on persistence
diagrams built on top of graphs. Relying on extended persistence theory and the
so-called heat kernel signature, we show how graphs can be encoded by
(extended) persistence diagrams in a provably stable way. We then propose a
general and versatile framework for learning vectorizations of persistence
diagrams, which encompasses most of the vectorization techniques used in the
literature. We finally showcase the experimental strength of our setup by
achieving competitive scores on classification tasks on real-life graph
datasets
Assessing the Performance of MPI Applications Through Time-Independent Trace Replay
International audienceSimulation is a popular approach to obtain objective performance indicators platforms that are not at one's disposal. It may help the dimensioning of compute clusters in large computing centers. In this work we present a framework for the off-line simulation of MPI applications. Its main originality with regard to the literature is to rely on time-independent execution traces. This allows us to completely decouple the acquisition process from the actual replay of the traces in a simulation context. Then we are able to acquire traces for large application instances without being limited to an execution on a single compute cluster. Finally our framework is built on top of a scalable, fast, and validated simulation kernel. In this paper, we present the used time-independent trace format, investigate several acquisition strategies, detail the developed trace replay tool, and assess the quality of our simulation framework in terms of accuracy, acquisition time, simulation time, and trace size.La simulation est une approche trĂšs populaire pour obtenir des indicateurs de performances objectifs sur des plates-formes qui ne sont pas disponibles. Cela peut permettre le dimensionnement de grappes de calculs au sein de grands centres de calcul. Dans cet article nous prĂ©sentons un outil de simulation post-mortem d'applications MPI. Sa principale originalitĂ© au regard de la littĂ©rature est d'utiliser des traces d'exĂ©cution indĂ©pendantes du temps. Cela permet de dĂ©coupler intĂ©gralement le processus d'acquisition des traces de celui de rejeu dans un contexte de simulation. Il est ainsi possible d'obtenir des traces pour de grandes instances de problĂšmes sans ĂȘtre limitĂ© Ă des exĂ©cutions au sein d'une unique grappe. Enfin notre outil est dĂ©veloppĂ© au dessus d'un noyau de simulation scalable, rapide et validĂ©. Cet article prĂ©sente le format de traces indĂ©pendantes du temps utilisĂ©, Ă©tudie plusieurs stratĂ©gies d'acquisition, dĂ©taille l'outil de rejeu que nous avons dĂ©velopĂ©, et evaluĂ© la qualitĂ© de nos simulations en termes de prĂ©cision, temps d'acuisition, temps de simulation et tailles de traces
STARRED: a two-channel deconvolution method with Starlet regularization
The spatial resolution of astronomical images is limited by atmospheric
turbulence and diffraction in the telescope optics, resulting in blurred
images. This makes it difficult to accurately measure the brightness of blended
objects because the contributions from adjacent objects are mixed in a
time-variable manner due to changes in the atmospheric conditions. However,
this effect can be corrected by characterizing the Point Spread Function (PSF),
which describes how a point source is blurred on a detector. This function can
be estimated from the stars in the field of view, which provides a natural
sampling of the PSF across the entire field of view.
Once the PSF is estimated, it can be removed from the data through the
so-called deconvolution process, leading to images of improved spatial
resolution. The deconvolution operation is an ill-posed inverse problem due to
noise and pixelization of the data. To solve this problem, regularization is
necessary to guarantee the robustness of the solution. Regularization can take
the form of a sparse prior, meaning that the recovered solution can be
represented with only a few basis eigenvectors.
STARRED is a Python package developed in the context of the COSMOGRAIL
collaboration and applies to a vast variety of astronomical problems. It
proposes to use an isotropic wavelet basis, called Starlets, to regularize the
solution of the deconvolution problem. This family of wavelets has been shown
to be well-suited to represent astronomical objects. STARRED provides two
modules to first reconstruct the PSF, and then perform the deconvolution. It is
based on two key concepts: i) the image is reconstructed in two separate
channels, one for the point sources and one for the extended sources, and ii)
the code relies on the deliberate choice of not completely removing the effect
of the PSF, but rather bringing the image to a higher resolution
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