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After being exposed to scala's Actors and Clojure's Futures, I feel like both languages have excellent support for multi core data processing.
However, I still have not been able to determine the real engineering differences between the concurrency features and pros/cons of the two models. Are these languages complimentary, or opposed in terms of their treatment of concurrent process abstractions?
Secondarily, regarding big data issues, it's not clear wether the scala community continues to support Hadoop explicitly (whereas the clojure community clearly does ). How do Scala developers interface with the hadoop ecosystem?
Some solutions are well solved by agents/actors and some are not. This distinction is not really about languages more than how specific problems fit within general classes of solutions. This is a (very short) comparason of Actors/agents vs. References to try to clarify the point that the tool must fit the concurrency problem.
Actors excel in distributed situation where no data needs to be concurrently modified. If your problem can be expressed purely by passing messages then actors will do the trick. Actors work poorly where they need to modify several related data structures at the same time. The canonical example of this being moving money between bank accounts.
Clojure's refs are a great solution to the problem of many threads needing to modify the same thing at the same time. They excel at shared memory multi-processor systems like today's PCs and Servers. In addition to the Bank account example, Rich Hickey (the author of clojure) uses the example of a baseball game to explain why this is important. If you wanted to use actors to represent a baseball game then before you moved the ball, all the fans would have to send it a message asking it where it was... and if they wanted to watch a player catching the ball things get even more complex.
Clojure has cascalog which makes writing hadoop jobs look a lot like writing clojure.
Actors provide a way of handling the potential interleaving and synchronization control that inevitably comes when trying to get multiple threads to work together. Each actor has a queue of messages that it processes in order one at a time so as to avoid the need to include explicit locks. In this case a Future provides a way of waiting for a response from an actor.
As far as Hadoop is concerned, Twitter just released a library specifically for Hadoop called Scalding but as long as the library is written for the JVM, it should work with either language.
I'm doing some research on multicore processors; specifically I'm looking at writing code for multicore processors and also compiling code for multicore processors.
I'm curious about the major problems in this field that would currently prevent a widespread adoption of programming techniques and practices to fully leverage the power of multicore architectures.
I am aware of the following efforts (some of these don't seem directly related to multicore architectures, but seem to have more to do with parallel-programming models, multi-threading, and concurrency):
Erlang (I know that Erlang includes constructs to facilitate concurrency, but I am not sure how exactly it is being leveraged for multicore architectures)
OpenMP (seems mostly related to multiprocessing and leveraging the power of clusters)
Unified Parallel C
Cilk
Intel Threading Blocks (this seems to be directly related to multicore systems; makes sense as it comes from Intel. In addition to defining certain programming-constructs, it also seems have features that tell the compiler to optimize the code for multicore architectures)
In general, from what little experience I have with multithreaded programming, I know that programming with concurrency and parallelism in mind is definitely a difficult concept. I am also aware that multithreaded programming and multicore programming are two different things. in multithreaded programming you are ensuring that the CPU does not remain idle (on a single-CPU system. As James pointed out the OS can schedule different threads to run on different cores -- but I'm more interested in describing the parallel operations from the language itself, or via the compiler). As far as I know you cannot truly do parallel operations. In multicore systems, you should be able to perform truly-parallel operations.
So it seems to me that currently the problems facing multicore programming are:
Multicore programming is a difficult concept that requires significant skill
There are no native constructs in today's programming languages that provide a good abstraction to program for a multicore environment
Other than Intel's TBB library I haven't found efforts in other programming-languages to leverage the power of multicore architectures for compilation (for example, I don't know if the Java or C# compiler optimizes the bytecode for multicore systems or even if the JIT compiler does that)
I'm interested in knowing what other problems there might be, and if there are any solutions in the works to address these problems. Links to research papers (and things of that nature) would be helpful. Thanks!
EDIT
If I had to condense my question down to one sentence, it would be this: What are the problems that face multicore programming today and what research is going on in the field to solve these problems?
UPDATE
It also seems to me that there are three levels where multicore needs to be concerned:
Language level: Constructs/concepts/frameworks that abstract parallelization and concurrency and make it easy for programmers to express the same
Compiler level: If the compiler is aware of what architecture it is compiling for, it can optimize the compiled code for that architecture.
OS level: The OS optimizes the running process and perhaps schedules different threads/processes to run on different cores.
I've searched on ACM and IEEE and have found a few papers. Most of them talk about how difficult it is to think concurrently and also how current languages don't have a proper way to express concurrency. Some have gone so far as to claim that the current model of concurrency that we have (threads) is not a good way to handle concurrency (even on multiple cores). I'm interested in hearing other views.
I'm curious about the major problems in this field that would currently prevent a widespread adoption of programming techniques and practices to fully leverage the power of multicore architectures.
Inertia. (BTW: that's pretty much the answer to all "what does prevent the widespread adoption" questions, whether that be models of parallel programming, garbage collection, type safety or fuel-efficient automobiles.)
We have known since the 1960s that the threads+locks model is fundamentally broken. By ~1980, we had about a dozen better models. And yet, the vast majority of languages that are in use today (including languages that were newly created from scratch long after 1980), offer only threads+locks.
The major problems with multicore programming is the same as writing any other concurrent applications, but whereas before it was uncommon to have multiple cpus in a computer, now it is hard to find any modern computer with only one core in it, so, to take advantage of multicore, multiple cpu architectures there are new challenges.
But, this problem is an old problem, whenever computer architectures go beyond compilers then it seems the fallback solution is to move back toward functional programming, as that programming paradigm, if strictly followed, can make very parallelizable programs, as you don't have any global mutable variables, for example.
But, not all problems can be done easily using FP, so the goal then is how to easily get other programming paradigms to be easy to use on multicores.
The first thing is that many programmers have avoided writing good mulithreaded applications, so there isn't a strongly prepared number of developers, as they learned habits that will make their coding harder to do.
But, as with most changes to the cpu, you can look at how to change the compiler, and for that you can look at Scala, Haskell, Erlang and F#.
For libraries you can look at the parallel framework extension, by MS as a way to make it easier to do concurrent programming.
It is at work, but I recently either IEEE Spectrum or IEEE Computer had articles on multicore programming issues, so look at what IEEE and ACM articles have been written on these issues, to get more ideas as to what is being looked at.
I think the biggest impediment will be the difficulty to get programmers to change their language as FP is very different than OOP.
One place for research besides developing languages that will work well this way, is how to handle multiple threads accessing memory, but, as with much in this area, Haskell seems to be at the forefront in testing ideas for this, so you can look at what is going on with Haskell.
Ultimately there will be new languages, and it may be that we have DSLs to help abstract the developer more, but how to educate programmers on this will be a challenge.
UPDATE:
You may find Chapter 24. Concurrent and multicore programming of interest, http://book.realworldhaskell.org/read/concurrent-and-multicore-programming.html
One of the answers mentioned the Parallel Extensions for the .NET Framework and since you mentioned C#, it's definitely something I would investigate. Microsoft has done something interesting things there, though I have to think many of their efforts seem more suited for language enhancements in C# than a separate and distinct library for concurrent programming. But I think their efforts are worth applauding and respect that we're early here. (Disclaimer: I used to be the marketing director for Visual Studio about 3 years ago)
The Intel Thread Building Blocks are also quite interesting (Intel recently released a new version, and I'm excited to head down to Intel Developer Forum next week to learn more about how to use it properly).
Lastly, I work for Corensic, a software quality startup in Seattle. We've got a tool called Jinx that is designed to detect concurrency errors in your code. A 30-day trial edition is available for Windows and Linux, so you might want to check it out. (www.corensic.com)
In a nutshell, Jinx is a very thin hypervisor that, when activated, slips in between the processor and operating system. Jinx then intelligently takes slices of execution and runs simulations of various thread timings to look for bugs. When we find a particular thread timing that will cause a bug to happen, we make that timing "reality" on your machine (e.g., if you're using Visual Studio, the debugger will stop at that point). We then point out the area in your code where the bug was caused. There are no false positives with Jinx. When it detects a bug, it's definitely a bug.
Jinx works on Linux and Windows, and in both native and managed code. It is language and application platform agnostic and can work with all your existing tools.
If you check it out, please send us feedback on what works and doesn't work. We've been running Jinx on some big open source projects and already are seeing situations where Jinx can find bugs 50-100 times faster than simply stress testing code.
The bottleneck of any high-performance application (written in C or C++) designed to make efficient use of more than one processor/core is the memory system (caches and RAM). A single core usually saturates the memory system with its reads and writes so it is easy to see why adding extra cores and threads causes an application to run slower. If a queue of people can pass through a door one a time, adding extra queues will not only clog the door but also make the passage of any one individual through the door less efficient.
The key to any multi-core application is optimization of and economizing on memory accesses. This means structuring data and code to work as much as possible inside their own caches where they don't disturb the other cores with acceses to the common cache (L3) or RAM. Once in a while a core needs to venture there but the trick is to reduce those situations as much as possible. In particular, data needs to be structured around and adapted to cache lines and their sizes (currently 64 bytes) and code needs to be compact and not call and jump all over the place which also disrupts pipelines.
My experience is that efficient solutions are unique to the application in question. The generic guidelines (above) are a basis on which to construct code but the tweak changes resulting from profiling conclusions will not be obvious to those who were not themselves involved in the optimizing work.
Look up fork/join frameworks and work-stealing runtimes. Two names for the same, or at least related, approaches, which is to recursively subdivide large tasks into lightweight units, such that all available parallelism is exploited, without having to know in advance how much parallelism there is. The idea is that it should run at serial speed on a uniprocessor, but get a linear speedup with multiple cores.
Sort of a horizontal analogue of cache-oblivious algorithms if you look at it right.
But i'd say the main problem facing multicore programming is that the great majority of computations remain stubbornly serial. There's just no way to throw multiple cores at those computations and make them stick.
i'm a computer systems engineering undergraduate student, i just want to know what advantages MATLAB has over SCILAB and vice versa other than that SCILAB is freeware.
i mean from a computer engineer point of view.
thanks
I can't get into the nitty-gritty details, as I haven't used SCILAB extensively.
But from a bird's eye view, MATLAB is a very polished software, with decades of development behind it. And a price to match. It has a huge array of specialized packages, good support, a reasonably well designed UI, and it's generally user-friendly enough for non-computer engineers to work with. It's also very common in the industry, so it's not a bad thing to have on your resume.
But if you don't have very complex needs (which I suspect, given the use I made of MATLAB during my undergrad years) and you don't need the robustness and polish of a professional package, SCILAB will probably meet your needs.
And since it's based on the MATLAB language, what you'll learn can be transferred later on if your needs change, or you find yourself working in an environment where MATLAB is the default.
Scilab is to MATLAB as OpenOffice is to MS Office. That is to say, it's a not-quite-a-clone, and it's not as polished. You do get most of the functionality of MATLAB, and the price is much more agreeable.
That said, if you want a free/open pretend MATLAB, I personally prefer Octave, since the syntax is closer to MATLAB's.
If you aren't bothered about MATLAB compatibility, then check out the statistics language/environment R, which is delightful.
Matlab is the de-facto industrial standard, is ready now and here, and has a big firm behind to push it.
Scilab has been for long time the open source alternative, but honestly it never appealed me. I think that or they never belived enough on the project, or that you need too much money to make a valid product of this kind.
And it is a real pity, since we desperately need a good open source alternative, because being open source is the only way to be very efficient on different platform: actually matlab is very good at prototyping small-medium programs, but since it is closed source, it's very difficult to scale it up, to supercomputers for example, requiring often a complete rewrite of the code.
Sage might be the third way, it has a lot of potential, and I would bet on it. Check it. It doesn't reinvent the wheel like Scilab did, but take existing software and merge it in a new program. It is based on python which gained a lot of momentum in the computing world, since it has shown to be both easy enough to quick prototype, and versatile enough to run on exotic platforma like supercomputers or GPGPU.
# MatlabDoug
It is feasible in small-medium environment, but on very big task the flexibility of open source is invaluable.
Starting from low-level tool like open-mpi that allows you to finely tune your applications, through higher-level framework like PETSc that lift a lot of work from your shoulders, to java and python implementations that let you concentrate on the algorithms forgetting about many of the headaches of the lower level languages.
But the real proof is that an astonishing majority of the top500 supercompunters prefers open source alternatives.
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This is marked as a subjective question, I hope I won't get too many down votes though.
LV seems to offer a nice graphic alternative to traditional text based programming. As I understand, it's not a just-virtualization/data acquisition programming language. Nonetheless, it seems to have that paradigm pegged to its creator's name.
My question comes up because it doesn't seem to be widely used for multi-purpose applications. I'm not a LV-expert of any kind, I'm more like a learner. I'm still getting used to LV.
Labview is fantastic if you have National Instruments hardware, and want to do something like acquire, plot and log the data.
When you start interfacing to custom devices the wiring between modules gets complicated having to do all the string manipulation work for input and output to a device.
At my place of work, we found that we got annoyed with having to make massive, complicated VI's to interface to devices and started writing them in .NET and interfacing them to Labview.
In the end we ended up scrapping Labview all together and using the NI Measurement Studio for Visual Studio to give us all the lovely looking NI controls (waveform plot, tank, gauges, switches etc) with the flexibility of C#.
In summary, even with a couple of 24" screens, sometimes the wiring for Labview code can get too complex and becomes impossible to comment, debug, and make extensible for any future changes. I suggest taking a look at Measurement Studio for Visual Studio and using your favourite .NET language with the pretty NI controls.
My two experiences with "graphic alternative[s] to traditional text based programming" have been dreadful. I find such languages to be slow to use, hard to edit, and inexpressive. Debugging them is a nightmare. And they offer no real advantages.
To be sure, it has been quite a long time since I looked at one, but the opinions of others I've asked about them have been only luke warm, so I have never taken the time to look again. Reasons to look again are welcome and will be taken on board...
Labview can be used to author large, complex software projects. Labview is unquestionably much more fun to use than a syntax based language. I have programmed mathematically dense, dynamic simulations using labview. Newer versions of Labview include alot of exciting features, especially for utilizing multiple processors. I like Labview very much. But I don't recommend it to anyone.
Unfortunately, it's an absolute nightmare for anything other than simple acquisition and display. It may one day be sufficiently developed to be considered as a viable alternative to text based languages. However, the developers at NI have consistently opted to ignore the three fundamental problems that plague labview.
1) It is unstable and riddled with bugs. There are thousands of bugs that have been posted to the labview support forums that are yet to be fixed. Some of these are quite serious, such as memory leaks, or mathematical errors in basic functions.
2) The documentation is atrocious. More often than not, when you look for help with a labview function in the local help file you'll find a sentence that merely restates the name of the item you are trying to find some detail on. e.g. A user looks up the help file on the texture filter mode setting and the only thing written in the help file is "Texture Filter Mode- selects the mode used for texture filtering." Gee, thanks. That clears things right up, doesn't it? The problem goes much deeper in that; quite often, when you ask a technical representative from national instruments to provide critical details about labview functionality or the specific behavior of mathematical functions, they simply don't know how the functions in their own library work. This may sound like an exaggeration, but trust me, it's not.
3) While it's not impossible to keep graphical code clean and well documented, Labview is designed to make these tasks both difficult and inefficient. In order to keep your code from becoming a tangled, confusing mess, you must routinely (every few operations) employ structures like clusters, and sub-vis and giant type defined controls (which can stretch over multiple screens in a large project). These structures eat memory and destroy performance by forcing labview to make multiple copies of data in memory and perform gratuitous operations- all for the sake of keeping the graphical diagram from looking like rainbow colored spaghetti with no comments or text anywhere in sight. Programming in labview is like playing pictionary with the devil. Imagine your giant software project written as a wall sized flowchart with no words on it at all. Now imagine that all the lines cross each other a thousand times so that tracing the data flow is completely impossible. You have just envisioned the most natural and most efficient way to program in labview.
Labview is cool. Labview is getting better with each new release. If National Instruments keeps improving it, it will be great one day as a general programming language. Right now, it's an extremely bad choice as a software development platform for large or logically complex projects.
I **have been writing in LabVIEW for almost 20 years now. I develop automated test systems. I have developed, RF, Vison, high speed digital and many different flavors of mixed signal test systems. I was a "C" programmer before I switched to LabVIEW.
It's true that you can build some programs quickly in LabVIEW, but just like any other language it takes a lot of training to learn to build a large application that is clean easy to maintain with reusable code. In 20 years I have never had a LabVIEW bug stop me from finishing a project.
Back in the day, NIWEEK would have a software shootout every year. LabVIEW and LabWINDOWS (NI's version of "C") programmers would both be given the same problem and have a race to see which group finished first. Each and every year all the LabVIEW programmers were done way before the 1st LabWINDOWs person finished. I have challenged many of my dedicated text based programming friends to shootouts and they all admit they don't stand a chance, even if I let them define the software problem.
So, I feel LabVIEW is a great programming tool. It's definitely the way to go if you’re interfacing with any type of NI hardware. It's not the answer for everything but I’m sure there are many people not using it just because they don’t consider LabVIEW a “real programming language”. After all, we just wire a bunch of blocks together right? I do find it funny how many text based programmers snub there noses at it as they are so proud of the mess of text code they have created that only they can understand. A good programmer in any language should write code that others can easily read. Writing overly complex code that is impossible to follow does not make the programmer a genius. It means the programmer is a “compliator”(someone who can take a simple problem and complicate it). I believe in the KISS principle (KEEP IT SIMPLE STUPID).
Anyway, there’s my two cents worth!**
I thought LabVIEW was a dream for FPGA programming. Independent executable blocks just... work. In general, I use LabVIEW for various tasks interfacing with my DAQ and FPGA hardware, but that's about it. It seems (again to me) that this is LabVIEW's strong point and the reason it was built, but outside that arena it feels "cumbersome." As far as getting things done, it's like any other language with a learning curve - once you figure it out it's not too bad for getting work done. I've seen several people give up before that thinking the learning curve was permanent or something.
Picking up a 30" monitor made a huge difference.
I know one thing that people dislike is the version control integration.
Edit: LabVIEW/hardware is hella expensive for "just for fun" use. I dropped $10K on their hardware (student prices) and got the software for free from school for making toys around the house.
Our company is using LabVIEW for the last 10 years for measuring, monitoring and reporting of our subject (trains).
Recently we have started using LabVIEW as GUI for databases with lots of data, the powers of LabVIEW with the recent new features (Classes, XControls) allows use to create these kinds of GUIs for a fraction of development costs at other platforms. While we don't need external programmers at consultancy rate.
Ton
I first started using Labview in a college physics lab. Initially, I thought it was slow and cumbersome when compared to other text-based languages. It was too difficult to create complex logic and code became sloppy real fast (wires everywhere).
Then, a few years later, I learned about using sub-vi's and bundles. What a difference! At this point, I was using labview for very high level functions. I was taking raw input from a camera, using all kinds of image filters and processing to ultimately parse out the lines in a road so that a vehicle could drive itself down this road with no driver - it was for the DARPA URBAN CHALLENGE. I was also generating maps from text waypoint data, making high-level parsing functions, and a slew of other applications that had nothing to do with processing data from input devices. It was really a lot of fun. and FAST.
After leaving college, I am now back to using text-based languages. I've been using: PHP, Javascript, VBA, C#, VBscript, VB.net, Matlab, Epson RC+, Codeigniter, various API's, and I'm sure some others. I often get very frustrated in the amount of syntax I have to memorize in order to program with any significant speed. I find it annoying to have to switch schools of thought based on the language I am using... when all programming languages essentially do the same thing! I need a second monitor just to have the help up at all times so i can find the syntax for the same functions in different languages. I miss Labview very much, it's too bad it's so expensive otherwise I would use it for everything.
Graphical based programming I think has a huge potential. By not being constrained by syntax, you can focus on logic instead of code. Labview itself may still be in its infancy in terms of support and debugging, but I believe conceptually it beats out the competition. It's simple a more intuitive way to program.
We use LabVIEW for running our end of line test equipment and it is ideal for data acquisition and control. Typically measuring 15 to 80 differential voltages and controlling environmental chambers, mass flow controllers and various serial devices LabVIEW is more than capable.
Interfacing with custom devices can be simplified greatly by using the NI instrument driver wizard to create reusable VI's, interfacing with custom dll's if needed. On a number of projects we have created such drivers for custom hardware and once created there are reusable in future projects with no modification.
Using event driven structures user interfaces are responsive and we regularly use LabVIEW applications to interface with a database.
Whatever programming environment you choose it's the process of designing the application that matters most. I agree that you can create some really horrible and unreadable block diagrams in LabVIEW but then you can also create unreadable code in Visual studio. With just a little thought and planning a LabVIEW block diagram can be made to fit on a single 24" monitor with plenty of space to add comments.
I would use LabVIEW over Visual Studio for most projects.
But people do use LabView for purposes other than data acquisition and virtualization. Of course LabVIEW is mainly used in labs and production environments because it is (or was) one of the main NI's customer target.
However you can do a lots of various things with LabVIEW, like programming a robot that would perform a lot of image analysis, and then tweet the results. Have a look at videos from NI Week 2009 on you-tube, and you'll see how powerful this tool is. For instance, there is possibility to write code and deploy it to ARM MCUs (see this Dev Monkey article from 2009.08.10).
And finally check this LabVIEW DIY group
I have been using LabVIEW for about two years for developing automation. If given due care and proper design we sure can develop maintainable and really good looking application in LabVIEW.I think this is the same for all the other languages out there. I have seen equally bad code in LabVIEW primarily from people who use it only to develop quick and dirty working automation. IMHO Graphical programming is a lot easier to code and understand if rightly done. But that said I feel text based programming 'feels' more powerful!
LabVIEW is primarily marketed for industrial automation, has inherent support for lot of NI hardware and you can get the third party hardwares working with it pretty quickly. I think that is the reason you see it only in automation field. Moreover it is pretty costly and you are locked down with NI as you do cannot even open your code if you do not buy the software from them!
I've been thinking about this question for decades (yes, since 1989...)
Like all programming languages, LabVIEW is a high-level tool used to manipulate the flow of electrons. Unless you are a purist and refuse to use anything other than a breadboard and wires; transistors, integrated circuits and programming languages are probably a good thing if you wish to build something of any consequence.
But like all high-level tools, just wielding one does not make you a professional craftsman. Back in the day of soldering irons, op-amps and UARTs it required a large amount of careful study before you could create a system that actually functioned. The modern realm of text-based languages is so overly dominated by syntax that the programmer must get it just right before it will compile and run. In order to write code that works, the programmer must increase their skill level to create systems much larger than "Hello World".
LabVIEW is not dominated by syntax, but by Data Flow. Back in the day, reaching for your flow charting template and developing the diagram of a well-balanced information system was the art and beauty part of the job. Only after you had the reviewed flowchart in hand would you even consider slogging through the drudgery of punching out the code. (yes... punch cards)
LabVIEW is a development system that allows the programmer to use flow charting tools to diagram the complete information system and press "run"..... LabVIEW "punches out the code" and compiles it for you. No need to fight through the syntax of text language A or language B.
With such a powerful tool, novices can build large, working programs rapidly -- implying some level of professional craftsmanship since it runs at all. However, if the system does not perform elegantly, or the source code diagram is a mess, it is not the fault of LabVIEW.
People often point to "LabVIEW is only good for developing large data acquisition systems." Perhaps those people should consider the professionalism of the scientists and engineers that are working in data acquisition. If they know enough to get the actual wires right for the sensors and transducers, it may be a good bet that they are expert at developing LabVIEW wiring diagrams as well.
I do use LabView at home, as it is part of Lego Mindstorms, which my son loves. And I really like the way to compose systems like this.
However, in my work (embedded systems), it is generally to restrictive. But also here, I'm trying to move up in abstraction:
- control and state behavior: Model based design (i.e. Rhapsody)
- data algorithms etc. Simulink
Sometimes a graphical model can require more clicks than a piece of code. But this also includes the work a good programmer need to do in design & documentation; not just the code typing. The graphical notation takes many hassles away and is generally much faster if the tool is powerful enough for the complexity at hand. So I expect these kinds of tools will gain more popularity in the next years as they mature and people get familiar with them.
I have used LabView for some 10 years. It's brilliant for Scientific prorgamming ie like Matlab or Simulink but 10 times better. If you are having problems then you are doing something wrong. It takes time to learn like any language. As for using .Net instead - are these people even on the same planet? Why would you go to the trouble of writing eveything from scratch when you can say pull up an FFT etc and use alread written code. .NET is fine for simple programs but not so good for Scientific processing. yes you can do it but not without oodles of add-ons for graphics etc. Prorgamming in G is far easier than text based for Scientific problems. You can of course program in c if you are interfacing and use the dll. Now there are things that I would not use LabView for - speech recognition for example may be a bit messy at present. More to the point though, why do people like programming in outdated text form when there is an easy alternative. It is as if people want to make things complicated so as to justify their job in some way. Simplify Simplify!
Somebody said that LabView is only sued in the Automation field. Simply not write at all. It has applications in Digital Signal Processing,Control Systems,Communications, Web Based,Mathematics,Image Processing and so on. It started as a data aquisition method and they invented the name Virtual Instrumentation but it has gone far beyond that now. It is a Scientific programming language with a second to none graphical interface. It is way beyond Simulink and if you like Matlab then it has a type of Matlab scripting built in for those that like such ways of programming. It is evolving all the time. The one thing I found difficult was writing code for the Compact Rio - tricky but far easier than the alternative. It's expensive but you get a quality product. I personally have not found any bugs in ordinary programming. It is an engineers language but anybody could use it to program.
I'm taking an introductory course (3 months) about real time systems design, but any implementation.
I would like to build something that let me understand better what I'll learn in theory, but since I have never done any real time system I can't estimate how long will take any project. It would be a concept proof project, or something like that, given my available time and knowledge.
Please, could you give me some idea? Thank you in advance.
I programm in TSQL, Delphi and C#, but I'll not have any problem in learning another language.
Suggest you consider exploring the Real-Time Specification for Java (RTSJ). While it is not a traditional environment for constructing real-time software, it is an up-and-coming technology with a lot of interest. Even better, you can witness some of the ongoing debate about what matters and what doesn't in real-time systems.
Sun's JavaRTS is freely available for download, and has some interesting demonstrations available to show deterministic behavior, and show off their RT garbage collector.
In terms of a specific project, I suggest you start simple: 1) Build a work-generator that you can tune to consume a given amount of CPU time; 2) Put this into a framework that can produce a distribution of work-generator tasks (as threads, or as chunks of work executed in a thread) and a mechanism for logging the work produced; 3) Produce charts of the execution time, sojourn time, deadline, slack/overrun of these tasks versus their priority; 4) demonstrate that tasks running in the context of real-time threads (vice timesharing) behave differently.
Bonus points if you can measure the overhead in the scheduler by determining at what supplied load (total CPU time produced by your work generator tasks divided by wall-clock time) your tasks begin missing deadlines.
Try to think of real-time tasks that are time-critical, for instance video-playing, which fails if tasks are not finished (e.g. calculating the next frame) in time.
You can also think of some industrial solutions, but they are probably more difficult to study in your local environment.
You should definitely consider building your system using a hardware development board equipped with a small processor (ARM, PIC, AVR, any one will do). This really helped remove my fear of the low-level when I started developing. You'll have to use C or C++ though.
You will then have two alternatives : either go bare-metal, or use a real-time OS.
Going bare-metal, you can learn :
How to initalize your processor from scratch and most importantly how to use interrupts, which are the fastest way you have to respond to an externel event
How to implement lightweight threads with fast context switching, something every real-time OS implements
In order to ease this a bit, look for a dev kit which comes with lots of documentation and source code. I used Embedded Artists ARM boards and they give you a lot of material.
Going with the RT OS :
You'll fast-track your project, and will be able to learn how to fine-tune a RT OS
You may try your hand at an open-source OS, such as Linux or the BSDs, and learn a lot from the source code
Either choice is good, you will get a really cool hands-on project to show off and hopefully better understand your course material. Good luck!
As most realtime systems are still implemented in C or C++ it may be good to brush up your knowledge of these programming languages. Many realtime systems are also embedded systems, so you might want to play around with a cheap open source one like BeagleBoard (http://beagleboard.org/). This will also give you a chance to learn about cross compiling etc.