The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific f...
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The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.
We study exact recovery conditions for the linear programming relaxation of the k-median problem in the stochastic ball model (SBM). In Awasthi et al. (Relax, no need to round: integrality of clustering formulations. ...
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We study exact recovery conditions for the linear programming relaxation of the k-median problem in the stochastic ball model (SBM). In Awasthi et al. (Relax, no need to round: integrality of clustering formulations. arXiv:1408.4045, 2015;in: Proceedings of the 2015 conference on innovations in theoretical computerscience, pp 191-200, 2015), the authors give a tight result for the k-median LP in the SBM, saying that exact recovery can be achieved as long as the balls are pairwise disjoint. We give a counterexample to their result, thereby showing that the k-median LP is not tight in low dimension. Instead, we give a near optimal result showing that the k-median LP in the SBM is tight in high dimension. We also show that, if the probability measure satisfies some concentration assumptions, then the k-median LP in the SBM is tight in every dimension. Furthermore, we propose a new model of data called extended stochastic ball model (ESBM), which significantly generalizes the well-known SBM. We then show that exact recovery can still be achieved in the ESBM.
Knapsack problem is a classical optimization problem in computerscience and programming. Knapsack problem main objective is to solve how much the maximum profit can be carried with the knapsack maximum capacity. The ...
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In computerscience education, teaching and learning programming is difficult. Understanding and coding programmes are regarded as extremely difficult in computerscience education. This is because practical ability i...
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The Balitsky-Kovchegov (BK) evolution equation is an equation derived from perturbative Quantum Chromodynamics that allows one to evolve with collision energy the scattering amplitude of a pair of quark and antiquark ...
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The Balitsky-Kovchegov (BK) evolution equation is an equation derived from perturbative Quantum Chromodynamics that allows one to evolve with collision energy the scattering amplitude of a pair of quark and antiquark off a hadron target, called the dipole amplitude. The initial condition, being a non-perturbative object, usually has to be modeled separately. Typically, the model contains several tunable parameters that are determined by fitting to experimental data. In this contribution, we propose an implementation of the BK solver using differentiable programming. Automatic differentiation offers the possibility that the first and second derivatives of the amplitude with respect to the initial condition parameters are automatically calculated at all stages of the simulation. This fact should considerably facilitate and speed up the fitting step. Moreover, in the context of Transverse Momentum Distributions (TMD), we demonstrate that automatic differentiation can be used to obtain the first and second derivatives of the amplitude with respect to the quark-antiquark separation. These derivatives can be used to relate various TMD functions to the dipole amplitude. Our C ++ code for the solver, which is available in a public repository [1], includes the Balitsky one-loop running coupling prescription and the kinematic constraint. This version of the BK equation is widely used in the small-x evolution framework.
作者：
Schor, Nina F.NIH
Div Intramural Res Bethesda MD USA NIH
Intramural Res 1 Ctr Dr Bethesda MD 20892 USA
The road between a hypothesis about a disease or condition and its cure or palliation is never simply linear. There are many tantalizing tangents to be chased and many seemingly obvious truths with countless exception...
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The road between a hypothesis about a disease or condition and its cure or palliation is never simply linear. There are many tantalizing tangents to be chased and many seemingly obvious truths with countless exceptions;this is usually a feature, not a bug, as they say in computerprogramming. In the tangents and exceptions are clues and alternative roads to science and medicine that can provide cures and palliative measures, sometimes for diseases or conditions other than the one being studied. The narrative that follows uses the author's scientific experience in childhood nervous system cancer to illustrate the importance of a robust, bidirectional interaction between the laboratory bench and the clinic bedside in the quest for solutions to problems of health, longevity, and quality of life. Published by Elsevier Inc.
We are entering a new era in which software systems are becoming more and more complex and larger. So, the composition of such systems is becoming infeasible by manual means. To address this challenge, self-organising...
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ISBN:
(纸本)9783031646256;9783031646263
We are entering a new era in which software systems are becoming more and more complex and larger. So, the composition of such systems is becoming infeasible by manual means. To address this challenge, self-organising software models represent a promising direction since they allow the (bottom-up) emergence of complex computational structures from simple rules. In this paper, we propose an abstract machine, called the composition machine, which allows the definition and the execution of such models. Unlike typical abstract machines, our proposal does not compute individual programs but enables the emergence of multiple programs at once. We particularly present the machine's semantics and demonstrate its operation with well-known rules from the realm of Boolean logic and elementary cellular automata.
With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application programming Interfaces(APIs)that can b...
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With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.
We argue that there is a link between implicit computational complexity theory and reversible computation. We introduce inherently reversible programming languages which capture the complexity classes ETImE and p. Fur...
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We argue that there is a link between implicit computational complexity theory and reversible computation. We introduce inherently reversible programming languages which capture the complexity classes ETImE and p. Furthermore, we discuss and analyze higher order versions of our reversible programming languages. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
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