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I am looking for ideas for my undergraduate project and I quite like the area of High Performance Computing , has got a lot of scope for research . Are there any ideas / already existing open source projects worth looking at ?
One hot field right now is in the area of algorithmic trading. You can sign up for $3000 (if you're under 21 -- it's $10k for over 21) at InteractiveBrokers.com and they will give you a free paper trading account (which is fake money traded using realtime data) of $10,000,000. They have API's in C#, C++, VB, Java and reasonable support... You could write your own stock pair trading algorithm. They have good documentation on how to get started.
You can scale this as high as you want, also a lot of people do high frequency trading which requires hpc and in-depth knowledge of Unix and C++.
Worth looking into, my 2 cents.
Perhaps massively parallel processing? Libraries like Cuda, OpenCL, and DirectCompute are just blossoming, and have a high likelihood of becoming commonplace. In my company, we are researching uses for OpenCL, and we're finding that it has the potential to revolutionize our industry.
Just a thought.
I would suggest looking at Sandia National Labs's SST (The Structural Simulation Toolkit). Its a highly parallel simulator framework used for HPC. It uses and incorporates other simulators from academia and industry. For instance, it currently integrates GEM5, QSim, MacSim, DRAMSim, Merlin, Portals, DRAMSim2, Iris, etc. Moreover, it is open source so you can contribute to the development.
You could work on integrating other academia components into SST, improve the interface of one of the components with SST, or just improve of the components themselves.
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If there is any PLC programmer or Twincat3 user out there. I would like to write my master thesis on Twincat3 in a company. Basically, They have different kinds of the Test bench, and they want someone to develop drivers for them. I have no experience with PLC or C++ or IEC61131 languages. Is it possible to learn any of these in 3 months, and then start writing the thesis? I have three months of Internship time before starting. I am having a bit of doubt. Even though it is daunting as an Electrical engineering student, I have no other options.
I thank you in Advance.
Also, The test benches are mainly Powerelectronics or Electrical machine test bench. I believe I need to automate the test processes in twincat3.
Best Regards
Good choice with TwinCAT 3. TwinCAT 3 is very capable, and quite easy to learn (of course dependent on your background, but generally a good platform to work on).
All I can support you with is a link to a TwinCAT 3 tutorial that I've created that is free of charge (available on YouTube):
https://www.youtube.com/playlist?list=PLimaF0nZKYHz3I3kFP4myaAYjmYk1SowO
There are also some other resources available both on YouTube and on the website. I've created a set of links here to help you find all the resources you might need.
To answer your question I would say it depends. Three months is not much time, especially considering you probably have a lot of other things that need to go in there (doing studies, writing the thesis, implementation, conclusions etc). It depends on the complexity of your project (it's not very specific what "writing drivers for them" means). If it's a simple project (including a very basic set of I/Os) it might be do-able. If it's anything more complex (like needing to add a front-end, doing motion control and maybe even safety) then it's most likely going to be hard to finish it in three months.
But again, I think more details on what you want to achieve is necessary.
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I'm a 2nd-year student doing my bachelor's degree in Computer Applications. I'm really interested in becoming a Game Programmer and my dream is to work at Rockstar Games😅(it sounds a bit crazy). I'm really worried and I don't know how to program or write code. I'm an absolute beginner. Do you have any tips or advice on how to get there and fulfill my dream? Thanks😀.
I work as a gameplay programmer in Canada so here's a few thing I can tell you.
Best place to start is doing the tutorials on the Unity website. Make a few that interests you and see how you like that. Unity is used in a lot of studios and it's very easy to find a ton of resources online for it. Once your more comfortable with it i'd experiment with unreal also to get an idea for more than 1 engine. While doing all that the idea is for you to build a portfolio of games/projects you can show an employer. Its ez to make ios/android builds to have portable visuals. Game jams are a great place to hone in some skills. Most game jam even lets you join in alone so teams missing people can add you in(programmers are usualy whats missing the most).
For the game industry more or less any programming degree can do but every studio will make you pass a programming test. Knowledge of the engine they use be it Unity/Unreal will be very important. For bigger studios that use their own they look for good c++/c# knowledge an often more experience. At the momment the industry is in dire need of people so its def a good time to join.
If rockstar is your ultimate goal you can certainly try and apply there right away (there's no harm), but its often more difficult to get in via larger studios when you don't know anyone there (also knowing someone on the inside is a major plus anywhere). But even if you don't know anyone, smaller studios have a lot of opennings at the momment and will often give you a a chance. 3 to 5 years experience in is when most opportunities might open up. But again depending on the labour market place may be more desperate.
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I found it much better on visualisation but not many people using it. Why isn't mathematica not as popular as matlab or python?
MATLAB and Mathematica are different packages for different user groups. Both software tools have similar freeware versions including Octave (MATLAB), Maxima (Mathematica), and others.
MATLAB has realized its objectives as being a software package for prototyping, idea testing, and simulations. It is more popular with engineers and some scientists. This popularity is based, in part, on marketing to students and instructors, ease of use, and availability.
Mathematica is a difficult and yet powerful tool. It has a set of language options and the usage has been personally frustrating. I was excited about integration with R language, but I have found that integration to be lacking, so far. It is more popular with a few engineers, some scientists, and mathematicians. Mathematica's latest editions have desirable licensing for students and home users. While not free, expense is reasonable.
Both tools have limited use in some industries.
Licensing is much more expensive for industrial partners. Industry continues to tighten its belt. Therefore, if you plan to be in industry, it may be highly beneficial to experience the freeware options, which nowadays includes the packages mentioned above as well as R, Python, and others.
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We are a small team of developers and are currently in the process of drafting a business idea targeting scala developers as potential customers. In order to convince investors we obviously need some numbers on usage, some of which we were unable to find:
While there is a list of companies listed at typesafe.com we were not able to find out what they actually do with Scala and what the total market share of Scala is.
The TIOBE Index lists scala as the 29th most popular (?) language but the methods seem questionable and it seems hard to find out what that means for the real world. Are there any surveys where developers were actually asked what they use, which involve less guessing? (Possibly even in a commercial context)
For us it would be great to be able to print some (preferably exponential) curves on the development of Scala usage in the last years. While that is how we feel that scala is spreading we dont have anything to proove it.
Is there any evidence on scala beeing popular in the financial sector or other specific areas? For example, there seems to be some kind of a "Scala hotspot" in the financial district of London and it would be great to proove that.
Any hints on citeable surveys and studies would be awesome!
this won't tell you how many people are using it, but is a good indicator of trending technologies, as oposed to TIOBE, which rates any tech invented since the wheel
RedMonk uses github + stackoverflow
Scala is indeed popular in Finance in London. We are using it for our projects in a Front Office department in a major investment bank.
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I want to start a simulation project, which will be a descrete-time simulation. The purpose is simulating agent communication with some non-autonomous physical models involved, so it is not necessarily limited to a pure agent-based simulation. Before starting, I wanted to ask what software engineering practices specific to simulation do exist, for example test practices (TDD suited? Simulation tends to be hinghly non-deterministic), which problems from a software engineering point of view are common, often occurring problems, etc. I am not talking about the modelling process, but the process of the realization of a system that uses existing models. Related book recommendations are very welcome.
Thanks.
Marcin is right, this question is much too broad to have a correct answer apart from It Depends.â„¢
The main reason for this is that simulation software is, first and foremost, still 'just' software, and the engineering part very much depends on your requirements (programming language, purposes of the software, time budget, constraints on resources, etc.).
Of course, there might be additional steps involved (such as VV&A) and certain tasks need extra care, such as testing, but all this depends on the context.
Also, before you start hacking away, have you looked at existing tools - maybe there is a library or framework that you can rely on? If so, what approaches have worked there?
Except general introductions (like this), most books and papers are also focused on specific subsets of simulation software (e.g. simulation software in C++, or agent-based simulations, or parallel and distributed simulations). So without more context it is hard to even point you to relevant material.
One common problem in software engineering and development of (agent-based) simulation software is dealing with floating point numbers.
Since not all real numbers can be exactly represented by the floating point formats used in computers, small errors can build up in simulations with many operations over time and influence the final results.
Moreover, small differences in how floating point operations are implemented on various hardware and software platforms may result in different outcomes when a simulation is run on different systems.
See these links for some extensive studies into the effects of this on agent-based models:
http://www.macaulay.ac.uk/fearlus/floating-point/
http://jasss.soc.surrey.ac.uk/8/1/5.html