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Register a new userHow Flexible is Your Computing System
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Added by
Luc Waeijen
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In literature computer architectures are frequently claimed to be highly flexible, typically implying there exist trade-offs between flexibility and performance or energy efficiency. Processor flexibility, however, is not very sharply defined, and as such these claims can not be validated, nor can such hypothetical relations be fully understood and exploited in the design of computing systems. This paper is an attempt to introduce scientific rigour to the notion of flexibility in computing systems.
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The proliferation of IoT devices in smart homes, hospitals, and enterprise networks is widespread and continuing to increase in a superlinear manner. With this unprecedented growth, how can one assess the security of an IoT network holistically? In this article, we explore two dimensions of security assessment, using vulnerability information of IoT devices and their underlying components ($textit{compositional security scores}$) and SIEM logs captured from the communications and operations of such devices in a network ($textit{dynamic activity metrics}$) to propose the notion of an $textit{attack circuit}$. These measures are used to evaluate the security of IoT devices and the overall IoT network, demonstrating the effectiveness of attack circuits as practical tools for computing security metrics (exploitability, impact, and risk to confidentiality, integrity, and availability) of heterogeneous networks. We propose methods for generating attack circuits with input/output pairs constructed from CVEs using natural language processing (NLP) and with weights computed using standard security scoring procedures, as well as efficient optimization methods for evaluating attack circuits. Our system provides insight into possible attack paths an adversary may utilize based on their exploitability, impact, or overall risk. We have performed experiments on IoT networks to demonstrate the efficacy of the proposed techniques.
This paper describes JANUS, a modular massively parallel and reconfigurable FPGA-based computing system. Each JANUS module has a computational core and a host. The computational core is a 4x4 array of FPGA-based processing elements with nearest-neighbor data links. Processors are also directly connected to an I/O node attached to the JANUS host, a conventional PC. JANUS is tailored for, but not limited to, the requirements of a class of hard scientific applications characterized by regular code structure, unconventional data manipulation instructions and not too large data-base size. We discuss the architecture of this configurable machine, and focus on its use on Monte Carlo simulations of statistical mechanics. On this class of application JANUS achieves impressive performances: in some cases one JANUS processing element outperfoms high-end PCs by a factor ~ 1000. We also discuss the role of JANUS on other classes of scientific applications.
Given the fact that Earth is so far the only place in the Milky Way galaxy known to harbor life, the question arises of whether the solar system is in any way special. To address this question, I compare the solar system to the many recently discovered exoplanetary systems. I identify two main features that appear to distinguish the solar system from the majority of other systems: (i) the lack of super-Earths, (ii) the absence of close-in planets. I examine models for the formation of super-Earths, as well as models for the evolution of asteroid belts, the rate of asteroid impacts on Earth, and of snow lines, all of which may have some implications for the emergence and evolution of life on a terrestrial planet. Finally, I revisit an argument by Brandon Carter on the rarity of intelligent civilizations, and I review a few of the criticisms of this argument.
Imagine a large graph that is being processed by a cluster of computers, e.g., described by the $k$-machine model or the Massively Parallel Computation Model. The graph, however, is not static; instead it is receiving a constant stream of updates. How fast can the cluster process the stream of updates? The fundamental question we want to ask in this paper is whether we can update the graph fast enough to keep up with the stream. We focus specifically on the problem of maintaining a minimum spanning tree (MST), and we give an algorithm for the $k$-machine model that can process $O(k)$ graph updates per $O(1)$ rounds with high probability. (And these results carry over to the Massively Parallel Computation (MPC) model.) We also show a lower bound, i.e., it is impossible to process $k^{1+epsilon}$ updates in $O(1)$ rounds. Thus we provide a nearly tight answer to the question of how fast a cluster can respond to a stream of graph modifications while maintaining an MST.
We explore the recoverability of gas physical conditions with the Large Velocity Gradient (LVG) model, using the public code RADEX and the molecules HCN and CO. Examining a wide parameter range with a series of models of increasing complexity we use both grid and Monte Carlo Markov Chain (MCMC) methods to recover the input conditions, and quantify the inherent and noise induced uncertainties in the model results. We find that even with the benefit of generous assumptions the LVG models struggle to recover any parameter better than to within half a dex, although we find no evidence of systemic offsets. Examining isotopologue lines we demonstrate that it is always preferable to model the isotopologue abundance ratio as a free parameter, due to large biases introduced in all other parameters when an incorrect ratio is assumed. Finally, we explore the effects of the background radiation temperature on CO and HCN line ratios, with an emphasis on the effect of the CMB at $z>4$, and show that while the effect on the line ratios is minor, the effect on the SLED peak is significant and that the CO$(1-0)$ line luminosity to H$_2$ mass conversion factor ($alpha_{rm CO}$) needs to be altered to account for the loss of contrast against the hotter CMB as redshift increases.
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