I am looking for a distributed simulation algorithm which allows me to couple multiple standalone systems. The systems I am targeting for interconnection use different formalisms, e.g. discrete time and continuous simulation paradigms. Now, the only algorithms I found were from the field of parallel discrete event simulation (PDES), such as the classical chandy/misra "null"-message protocol, which has some very undesirable problems. My question is now, what other approaches to interconnecting simulation systems besides PDES-algorithms are known i.e. can be used for interconnecting simulation systems?
Not an algorithm, but there are two IEEE standards out there that define protocols intended to address your issue: High-Level Architecture (HLA) and Distributed Interactive Simulation (DIS). HLA has a much greater presence in the analytic discrete-event simulation community where I hang out, DIS tends to get more use in training applications. If you'd like to check out some applications papers, go to the Winter Simulation Conference / INFORMS-sponsorted paper archive site and search for HLA, you'll get 448 hits.
Be forewarned, trying to make this stuff work in general requires some pretty weird plumbing, lots of kludges, and can be very fragile.
Related
In order to make my model simulation's in Modelica run faster am asking the following quesion :
What does impact simulation runtime in Modelica ?
i will aprecicate any help possible.
Edit: More details can be consulted from my book "Modelica by Application -- Power Systems" (URL)
What does impact the runtime performance?
I. Applied compilation techniques
Naturally, object-oriented Modelica models, even trivial ones, would correspond to a large-scale system of equations. Modelica simulation environments would usually optimize such generated models:
reduce the number of possible equations by removing trivial ones (i.e. alias equations)
decompose a large-block of equation system with so called BLT-transformation into smaller cascaded blocks of equation systems that can be solved faster in a sequential manner and not as a single block of equations,
solve s.c. large algebraic loops using tearing methods.
It can theoretically even go too far and attempt to solve blocks of equation system in an analytical manner if possible instead of conducting expensive numerical integration
Thus, the runtime performance would be influenced by the underlying Modelica compiler and how far does it exploit equation-based compiler methods. Usually some extra settings need to be activated to exploit all possible kind of such techniques. Digging the documentation to enable such settings is needed.
II. The nature of the model
The nature of the model would influence the runtime performance, particularly:
Is the model a large-scale system? or a small-scale one?
Is it strongly nonlinear or semi-linear one?
Is the resulting optimized equation system corresponding to the model sparse (i.e. large set of equations each with few number of variables, e.g. power system network models) or dense (e.g. multibody systems and biochemical networks)
Is it a stiff system? (e.g. a system with several subsystems some exhibiting very quick dynamics and others very slow dynamics)
Does the system exhibit large number of state events
...
III The choice of the solver
The mentioned characteristics of a given model would typically influence the ideal choice of the solver. The solver can largely influence the runtime performance (and accuracy). A strategy for solver choice could be made in the following order:
For a non-stiff weakly nonlinear model, the ideal choice would be an explicit method, e.g. Single-step Runga-Kutta or Multi-step Adam-Bashforth of higher order. If accuracy is less significant, one can attempt an explicit method of a lower order which would executes faster. Naturally, increasing the solver error tolerance would also speed-up the simulation.
However, it could happen, particularly for large-scale systems, that numerical stability could be more difficult to guarantee. Then, smaller solver step-sizes (and/or smaller error tolerance) for explicit solvers should be attempted. In this case, an implicit solver with larger error tolerance can be comparable with an explicit solver with a smaller tolerance.
Actually, it is wise to try both methods, comparing the accuracy of the results, and figuring out if explicit methods produce comparably accurate results. However, as a warning this would be just a heuristic, since the system does not necessarily have the same behavior over the entire space of admissible parameter values.
For increasing nonlinearity of the model, the choice would tend more towards modern solvers making use of variable step-size techniques. Here I would start with implicit variable-step Runga-Kutta (i.e. single-step) and/or the implicit variable-step multi-step methods, Adams–Moulton. For both of these classes, one can enlarge the solver tolerance and/or lower the solver error order and figure out if the simulation produces comparably accurate solutions (but with faster runtime).
Implementations of the previous classes of methods are usually less conservative with error control, and therefore, for increasing stiffness of the model or badly scalable models, the choice would tend more towards modern solvers implementing so-called numerically more stable backward differentiation formula (BDF), s.a. DASSL, CVODE, IDA. These solvers (can) also make use of the s.c. Jacobian of the system for adaptive step-size control.
A modern solver like LSODAR that switches between explicit and implicit solvers and also perform automatic error order control (switching between different orders) is a good choice if one does not know that much information about the behavior of the model. May be some Modelica environments have an advanced solver making use of automatic switching. However, if one knows the behavior of the model in advance, it is also wise to use other suggested methods since LSODAR may not perform the most optimal switching when needed.
x. ...
The comparisons between solvers from classes 3,4 and 5 are not straightforward to judge and it depends also on whether the system is continuous or hybrid, i.e. the underlying root-finding algorithms.
Usually DASSL could be slower as it is more conservative with step-size/error control. So it seems that IDA and others are faster. Some published works exist that can give some intuitions regarding such comparisons. It would be nice to have a Modelica library including all possible types of models and running all possible benchmarks w.r.t. accuracy and runtime to draw some more solver/model specific conclusions. A library that could be used and extended for such a purpose is the ScalableTestSuite Modelica library.
IV. Advanced aspects
There have been some published works in the Modelica community regarding making use of sparse solvers to exploit the expected sparsity of the Jacobian. If such a feature is provided by the simulation environment, this would usually significantly improve the runtime performance of large-scale models.
For models with massive number of events, numerical integration in the standard way can be extremely inefficient. Particularly challenging is when an event is triggered, other sets of state-events could be further triggered and a queue of state-events should be evaluated. The root-finding algorithm could further trigger other events and the solver could be hanging on in a s.c. chattering situation. There are advanced strategies for such situations, s.c. sliding mode, however I am not sure how far Modelica simulation environments are handing this issue.
One set of suggested solutions (also for systems with high degree of stiffness) is to employ so called QSS (quantized state system) methods. This would be significantly beneficial particularly for models that can not be solved using explicit solvers. There are both explicit and implicit QSS methods. There have been also other worth-to-try numerical integration strategies where only subsets of the entire equation system is evaluated when approximating a state event. Here I am not sure about availability of such solvers.
Some simulation environments differentiate between two simulation modes which can influence the simulation runtime: the ODE Mode and DAE Mode. In the first mode, the system is reduced to an ODE system with potentially additional cascaded blocks of nonlinear equation systems. In the DAE mode, the system is reduced to a DAE system of index one. The former mode would be beneficial for dense systems exhibiting such large cascaded blocks of nonlinear equations to be solved using s.c. Tearing methods instead of numerical integration. The DAE mode would be beneficial for large-scale sparse systems solved using sparse solvers. I think the ODE mode is usually activated by choosing CVODE or LSODAR while DAE mode is activated by choosing IDA or DASSL. But digging the documentation here and there is also recommended.
There are also some published works regarding so called multirate numerical integration solvers. Here, in each numerical integration step, only the numerically-significant portion of the equation system and not the entire equation system is integrated. Hence, this is significantly beneficial for large-scale stiff systems.
x. ...
V. Parallelization
Obviously, making use of multicore / GPUs for executing numerical integration in parallel, among other approaches for applying parallelization can speed-up computations.
VI. quite very advanced topics
In order to pay attention at some excellent research attempts some of which can be exploited for speeding up the simulation runtime performance of large-scale (loosely-coupled) hybrid networked models, I am listing this here as well. Speed-up can be obtained by making use of hybrid paradigms, agent-based modeling paradigm and/or multimode paradigm. The idea behind is that it is possible to describe a loosely coupled system in several smaller subsystems and conduct the communication among subsystems only when necessary. This can be beneficial and the reasons can be traced by searching for relevant publications. There have been some excellent work in some of the mentioned directions, and it is worth to continue them where they have stopped if this is the case.
Remark: Any of the mentioned solvers is not necessarily present in all possible Modelica simulation environments. If a solver is not provided as a choice, one would still be able to produce an FMU-ME (Functional mockup unit for model exchange) and write code that numerically integrate this FMU with a desired solver.
Warning: Some of the above aspects are based on personal experiences for a particular type of models and are not necessarily true for all model types.
Few suggested reading and I am definitely missing a lot of key publications:
F. Casella, Simulation of Large-Scale Models in Modelica: State of the Art and Future Perspectives, Modelica 2016
Liu Liu, Felix Felgner and Georg Frey, Comparison of 4 numerical solvers for stiff and hybrid systems simulation, Conference 2010
Willi Braun, Francesco Casella and Bernhard Bachmann, Solving large-scale Modelica models: new approaches and experimental results using OpenModelica, Modelica 2017
Erik Henningsson and Hans Olsson and Luigi Vanfretti, DAE Solvers for Large-Scale Hybrid Models, Modelica 2019
Tamara Beltrame and François Cellier, Quantised state system simulation in Dymola/Modelica using the DEVS formalism, Modelica 2006
Victorino Sanz and Federico Bergero and Alfonso Urquia, An approach to agent-based modeling with Modelica, Simpra 2010
Is there a machine learning concept (algorithm or multi-classifier system) that can detect the variance of network attacks(or try to).
One of the biggest problems for signature based intrusion detection systems is the inability to detect new or variant attacks.
Reading up, anomaly detection seems to still be a statistical based en-devour it refers to detecting patterns in a given data set which isn't the same as detecting variation in packet payloads. Anomaly based NIDS monitors network traffic and compares it against an established baseline of a normal traffic profile. The baseline characterizes what is "normal" for the network - such as the normal bandwidth usage, the common protocols used, correct combinations of ports numbers and devices etc
Say some one uses Virus A to propagate through a network then some one writes a rule to stop Virus A but another person writes a "variation" of Virus A called Virus B purely for the purposes of evading that initial rule but still using most if not all of the same tactics/code. Is there not a way to detect variance?
If there is whats the umbrella term it would come under, as ive been under the illusion that anomaly detection was it.
Could machine learning be used for pattern recognition(rather than pattern matching) at the packet payload level?
i think your intution to look at machine learning techniques is correct, or will turn out to be correct (One of the biggest problems for signature based intrusion detection systems is the inability to detect new or variant attacks.) The superior performance of ML techiques is in general due to the ability of these algorithms to generalize (a multiplicity of soft constraints rather than a few hard constraints). and to adapt (updates based on new training instances to frustrate simple countermeasures)--two attributes that i would imagine are crucial for identifying network attacks.
The theoretical promise aside, there are practical difficulties with applying ML techniques to problems like the one recited in the OP. By far the most significant is the difficultly in gathering data to train the classifier. In particular, reliably labeling data points as "intrusion" is probably not easy; likewise, my guess is that these instances are sparsely distributed in the raw data."
I suppose it's this limitation that has led to the increased interest (as evidenced at least by the published literature) in applying unsupervised ML techniques to problems like network intrusion detection.
Unsupervised techniques differ from supervised techniques in that the data is fed to the algorithms without a response variable (i.e., without the class labels). In these cases you are relying on the algorithm to discern structure in the data--i.e., some inherent ordering in the data into reasonably stable groups or clusters (possibly what you the OP had in mind by "variance." So with an unsupervised technique, there is no need to explicitly show the algorithm instances of each class, nor is it necessary to establish baseline measurements, etc.
The most frequently used unsupervised ML technique applied to problems of this type is probably the Kohonen Map (also sometimes called self-organizing map or SOM.)
i use Kohonen Maps frequently, but so far not for this purpose. There are however, numerous published reports of their successful application in your domain of interest, e.g.,
Dynamic Intrusion Detection Using Self-Organizing Maps
Multiple Self-Organizing Maps for Intrusion Detection
I know MATLAB has at least one available implementation of Kohonen Map--the SOM Toolbox. The homepage for this Toolbox also contains a brief introduction to Kohonen Maps.
Do you know if anyone has tried to compile high level programming languages (java, c#, etc') into a recurrent neural network and then evolve them?
I mean that the whole process including memory usage is stored in a graph of a neural net, and I'm talking about complex programs (thinking about natural language processing problems).
When I say neural net I mean a directed weighted graphs that spreads activation, and the nodes are functions of their inputs (linear, sigmoid and multiplicative to keep it simple).
Furthermore, is that what people mean in genetic programming or is there a difference?
Neural networks are not particularly well suited for evolving programs; their strength tends to be in classification. If anyone has tried, I haven't heard about it (which considering I barely touch neural networks is not a surprise, but I am active in the general AI field at the moment).
The main reason why neural networks aren't useful for generating programs is that they basically represent a mathematical equation (numeric, rather than functional). Given some numeric input, you get a numeric output. It is difficult to interpret these in the context of a program any more complicated than simple arithmetic.
Genetic Programming traditionally uses Lisp, which is a pure functional language, and often programs are often shown as tree diagrams (which occasionally look similar to some neural network diagrams - is this the source of your confusion?). The programs are evolved by exchanging entire branches of a tree (a function and all its parameters) between programs or regenerating an entire branch randomly.
There are certainly a lot of good (and a lot of bad) references on both of these topics out there - I refrain from listing them because it isn't clear what you are actually interested in. Wikipedia covers each of these techniques, and is a good starting point.
Genetic programming is very different from Neural networks. What you are suggesting is more along the lines of genetic programming - making small random changes to a program, possibly "breeding" successful programs. It is not easy, and I have my doubts that it can be done successfully across a large program.
You may have more luck extracting a small but critical part of your program, one which has a few particular "aspects" (such as parameter values) that you can try to evolve.
Google is your friend.
Some sophisticated anti-virus programs as well as sophisticated malware use formal grammar and genetic operators to evolve against each other using neural networks.
Here is an example paper on the topic: http://nexginrc.org/nexginrcAdmin/PublicationsFiles/raid09-sadia.pdf
Sources: A class on Artificial Intelligence I took a couple years ago.
With regards to your main question, no one has ever tried that on programming languages to the best of my knowledge, but there is some research in the field of evolutionary computation that could be compared to something like that (but it's obviously a far-fetched comparison). As a matter of possible interest, I asked a similar question about sel-improving compilers a while ago.
For a difference between genetic algorithms and genetic programming, have a look at this question.
Neural networks have nothing to do with genetic algorithms or genetic programming, but you can obviously use either to evolve neural nets (as any other thing for that matters).
You could have look at genetic-programming.org where they claim that they have found some near human competitive results produced by genetic programming.
I have not heard of self-evolving and self-imrpvoing programs before. They may exist as special research tools like genetic-programming.org have but nothing solid for generic use. And even if they exist they are very limited to special purpose operations like malware detection as Alain mentioned.
I am writing a report, and I would like to know, in your opinion, which open source physical simulation methods (like Molecular Dynamics, Brownian Dynamics, etc) and not ported yet, would be worth to port to GPU or another special hardware that can potentially speedup the calculation.
Links to the projects would be really appreciated.
Thanks in advance
Any physical simulation technique, be it finite difference, finite element, or boundary element, could benefit from a port to GPU. Same for Monte Carlo simulations of financial models. Anything that could use that smoking floating point processing, really.
I am currently working on quantum chemistry application on GPU. as far as I am aware, quantum chemistry is one of most demanding areas, in terms of total cpu time. there has been a number of papers regarding GPU and quantum chemistry, you can research those.
As far as methods, all of them are open source. are you asking about particular program? Then you can look at pyquante or mpqc. for molecular dynamics, look at hoomd. you can also Google QCD on GPU.
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A professor asked me to help making a specification for a college project.
By the time the students should know the basics of programming.
The professor is a mathematician and has little experience in other programming languages, so it should really be in MATLAB.
I would like some projects ideas. The project should
last about 1 to 2 months
be done individually
have web interface would be great
doesn't necessary have to go deep in maths, but some would be great
use a database (or store data in files)
What kind of project would make the students excited?
If you have any other tips I'll appreciate.
UPDATE: The students are sophomores and have already studied vector calculus. This project is for an one year Discrete Mathematics course.
UPDATE 2: The topics covered in the course are
Formal Logic
Proofs, Recursion, and Analysis of Algorithms
Sets and Combinatorics
Relations, Functions, and Matrices
Graphs and Trees
Graph Algorithms
Boolean Algebra and Computer Logic
Modeling Arithmetic, Computation, and Languages
And it'll be based on this book Mathematical Structures for Computer Science: A Modern Approach to Discrete Mathematics by Judith L. Gersting
General Suggestions:
There are many teaching resources at The MathWorks that may give you some ideas for course projects. Some sample links:
The MATLAB Central blogs, specifically some posts by Loren that include using LEGO Mindstorms in teaching and a webinar about MATLAB for teaching (note: you will have to sign up to see the webinar)
The Curriculum Exchange: a repository of course materials
Teaching with MATLAB and Simulink: a number of other links you may find useful
Specific Suggestions:
One of my grad school projects in non-linear dynamics that I found interesting dealt with Lorenz oscillators. A Lorenz oscillator is a non-linear system of three variables that can exhibit chaotic behavior. Such a system would provide an opportunity to introduce the students to numerical computation (iterative methods for simulating systems of differential equations, stability and convergence, etc.).
The most interesting thing about this project was that we were using Lorenz oscillators to encode and decode signals. This "encrypted communication" aspect was really cool, and was based on the following journal article:
Kevin M. Cuomo and Alan V. Oppenheim,
Circuit Implementation of Synchronized Chaos with Applications
to Communications, Physical Review
Letters 71(1), 65-68 (1993)
The article addresses hardware implementations of a chaotic communication system, but the equivalent software implementation should be simple enough to derive (and much easier for the students to implement!).
Some other useful aspects of such a project:
The behavior of the system can be visualized in 2-D and 3-D plots, thus exposing the students to a number of graphing utilities in MATLAB (PLOT, PLOT3, COMET, COMET3, etc.).
Audio signals can be read from files, encrypted using the Lorenz equations, written out to a new file, and then decrypted once again. You could even have the students each encrypt a signal with their Lorenz oscillator code and give it to another student to decrypt. This would introduce them to various file operations (FREAD, FWRITE, SAVE, LOAD, etc.), and you could even introduce them to working with audio data file formats.
You can introduce the students to the use of the PUBLISH command in MATLAB, which allows you to format M-files and publish them to various output types (like HTML or Word documents). This will teach them techniques for making useful help documentation for their MATLAB code.
I have found that implementing and visualizing Dynamical systems is great
for giving an introduction to programming and to an interesting branch of
applied mathematics. Because one can see the 'life' in these systems,
our students really enjoy this practical module.
We usually start off by visualizing a 1D attractor, so that we can
overlay the evolution rule/rate of change with the current state of
the system. That way you can teach computational aspects (integrating the system) and
visualization, and the separation of both in implementation (on a simple level, refreshing
graphics at every n-th computation step, but in C++ leading to threads, unsure about MATLAB capabilities here).
Next we add noise, and then add a sigmoidal nonlinearity to the linear attractor. We combine this extension with an introduction to version control (we use a sandbox SVN repository for this): The
students first have to create branches, modify the evolution rule and then merge
it back into HEAD.
When going 2D you can simply start with a rotation and modify it to become a Hopf oscillator, and visualize either by morphing a grid over time or by going 3D when starting with a distinct point. You can also visualize the bifurcation diagram in 3D. So you again combine generic MATLAB skills like 3D plotting with the maths.
To link in other topics, browse around in wikipedia: you can bring in hunter/predator models, chaotic systems, physical systems, etc.etc.
We usually do not teach object-oriented-programming from within MATLAB, although it is possible and you can easily make up your own use cases in the dynamical systems setting.
When introducing inheritance, we will already have moved on to C++, and I'm again unaware of MATLAB's capabilities here.
Coming back to your five points:
Duration is easily adjusted, because the simple 1D attractor can be
done quickly and from then on, extensions are ample and modular.
We assign this as an individual task, but allow and encourage discussion among students.
About the web interface I'm at a loss: what exactly do you have in mind, why is it
important, what would it add to the assignment, how does it relate to learning MATLAB.
I would recommend dropping this.
Complexity: A simple attractor is easily understood, but the sky's the limit :)
Using a database really is a lot different from config files. As to the first, there
is a database toolbox for accessing databases from MATLAB. Few institutes have the license though, and apart from that: this IMHO does not belong into such a course. I suggest introducing to the concept of config files, e.g. for the location and strength of the attractor, and later for the system's respective properties.
All this said, I would at least also tell your professor (and your students!) that Python is rising up against MATLAB. We are in the progress of going Python with our tutorials, but I understand if someone wants to stick with what's familiar.
Also, we actually need the scientific content later on, so the usefulness for you will probably depend on which department your course will be related to.
A lot of things are possible.
The first example that comes in mind is to model a public transportation network (the network of your city, with underground, buses, tramways, ...). It is represented by a weighted directed graph (you can use sparse matrix to represent it, for example).
You may, for example, ask them to compute the shortest path from one station to another one (Moore-dijkistra algorithm, for example) and display it.
So, for the students, the several steps to do are:
choose an appropriate representation for the network (it could be some objects to represent the properties of the stations and the lines, and a sparse matrix for the network)
load all the data (you can provide them the data in an XML file)
be able to draw the network (since you will put the coordinates of the stations)
calculate the shortest path from one point to another and display it in a pretty way
create a fronted (with GUI)
Of course, this could be complicated by adding connection times (when you change from one line to another), asking for several options (shortest path with minimum connections, take in considerations the time you loose by waiting for a train/bus, ...)
The level of details will depend on the level of the students and the time they could spend on it (it could be very simple, or very realist)
You want to do a project with a web interface and a database, but not any serious math... and you're doing it in MATLAB? Do you understand that MATLAB is especially designed to be used for "deep math", and not for web interfaces or databases?
I think if this is an intro to a Discrete Mathematics course, you should probably do something involving Discrete Mathematics, and not waste the students' time as they learn a bunch of things in that language that they'll never actually use.
Why not do something involving audio? I did an undergraduate project in which we used MATLAB to automatically beat-match different tunes and DJ mix between them. The full program took all semester, but you could do a subset of it. wavread() and the like are built in and easy to use.
Or do some simple image processing like finding Waldo using cross-correlation.
Maybe do something involving cryptography, have them crack a simple encryption scheme and feel like hackers.
MATLAB started life as a MATrix LAB, so maybe concentrating on problems in linear algebra would be a natural fit.
Discrete math problems using matricies include:
Spanning trees and shortest paths
The marriage problem (bipartite graphs)
Matching algorithms
Maximal flow in a network
The transportation problem
See Gil Strang's "Intro to Applied Math" or Knuth's "Concrete Math" for ideas.
You might look here: http://www.mathworks.com/academia/student_center/tutorials/launchpad.html
on the MathWorks website. The interactive tutorial (second link) is quite popular.
--Loren
I always thought the one I was assigned in grad school was a good choice-a magnetic lens simulator. The math isn't completely overwhelming so you can focus more on learning the language, and it's a good intro to the graphical capabilities (e.g., animating the path of an off-axis electron going through the lens).
db I/O and fancy interfaces are out of place in a discrete math course.
my matlab labs were typically algorithm implementations, with charts as output, and simple file input.
how hard is the material? image processing is really easy in matlab, can you do some discrete 2D filtering? blurs and stuff. http://homepages.inf.ed.ac.uk/rbf/HIPR2/filtops.htm