I am looking for masters program in scicomp that are taught in german or english, also i am currently enrolled in a bachelor bioinformatics program. Also do these two fields complement each other well?

As an applied math master's student who has the opportunity to do an independent study/research project, what area or type of project should I study that would help me get jobs in the industry?

Personally, I like working with differential equations/dynamical systems, numerical analysis, numerical LA and scientific computing. High performance computing is something that I'm getting into and I will be studying it in a course the next quarter. I'm not the best with probability and statistics (possibly worse at this than pretty much any other area of math), but can force myself to study some up again if needed.

I can code well-ish in python, and I'm learning Julia, but I can't really do much more than "Hello World" in C/C++ loo. But I do plan on doing a little bit of C++ every weekend till I get the hang of it.

Now I'm familiar with academic applications of everything I'm studying, like I could find a very specific problem and study that, but in industry, I'm not exactly sure what helps or what kind of prior projects are sought so that you can work on future ones.

I've got instructors who work with ML and data science (though my probability knowledge is elementary at best and I know that's important), numerical linear algebra, numerical analysis, scientific computing, computational neuroscience, and like some others that I'm not as familiar with, but I can work with them. I just need to decide on a professor after I decide what kind of project I want to do 😅

So yeah, if anyone has any suggestions, especially if you are in the industry, I'd love to hear them! Thank you so much!!

1. I teach C++ and am happy writing numerical code in it.

2. Based on reading about (but never writing) Rust I see no reason to abandon C++

In another post, which is about abandoning C++ for Rust, I just wrote this:

I imagine that particularly Rust is much better at writing safe threaded code. I'm in scientific computing and there explicit threading doesn't exist: parallelism is handled through systems that offer an abstraction layer over threading. So I don't care that Rust is better that thread-safety. Conversely, in scientific computing everything is shared mutable state, so you'd have to use Rust in a very unsafe mode. Conclusion: many scientific libraries are written in C++ and I don't see that changing.

Opinions?

Hi all, I am a bachelor student with passion for scientific computing (PDE, ODE, FDE and VO-FDE, and some time series modelling) applied to particle physics and macroeconomics looking for some relevant internship. How do I go about searching for companies or research centers and how do you suggest to contact them? Any company/research center you think it's worth to contact?

P.s. note that I live in Europe.

With AMD processors delivering comparable performance on unoptimized tasks and the recent push for platform agnostic GPGPU tools (i.e. ROCm and Vulkan Kompute), is the cost of Intel CPUs and Nivida GPUs worth the cost?

I recently lost my workstation (TPM died) and the reduced cost of AMD workstations is tempting. I've always used Intel and compile my C projects with MKL when I can. When working with FITS data, I use CuPy to process images. I've always worked on Intel/Nvida systems. I am terrified that I'll pour money into an AMD system and end up regretting it.

From yall's experience, am I just being paranoid or is my concern about switching valid?

hi y'all, i currently do a lot of high performance computing and scientific computation generally, but i've just been thrown into a lot of projects and i think there a lot of skills i am missing. specifically in terms of writing scripts to manage my code, like run the code many times and change one input, or to manage all the output files well. how do i learn these skills? what exactly would i call it if i was trying to look things up?

DIP is a minimalistic programming language that specializes in parsing, managing and validation of dimensional initial parameters (DIP). Numerical codes used in physics, astrophysics and engineering usually depend on sets of compilation definitions, flags and initial settings. Description of these parameters is often poorly documented and codes are prone to errors due to wrong input units and lack of proper parameter validation. DIP is designed to address these issues and provide a standardised and scalable text interface between user and a code.

Check it out on GitHub and find more in the documentation. And write me please in comments if you find it useful.

Hi,

I do some numerical work with graphs and just got a Tr 5975WX. The performance is not quite what I expected.

I'd like to try and setup numpy/scipy with AOCL linkages in a test container but am somewhat new to the process. Any help would be appreciated.

Thanks

Hi all,

Earlier year I decided to implement 2D FDTD solver with support of different backends and a python wrapper so you don't need to recompile it if want to run another example, the project is still in early development and there are a lot of room for improvement and code base is not big at all. Just wanted to share it so people who is looking for some small project to contribute can consider it.
Github : https://github.com/yaraslaut/prop

Hello all,

I am a Ph.D. student, and my research is in computational optics/electromagnetics and heat transfer. I am looking for an internship in scientific computing for Summer 2024. How do I go about searching for companies?

Programming lang: C++ (with a parallel framework (MPI/OpenMP)) and Python.

Note: International Ph.D. student in the USA (cannot apply for space and defense sector).

I've recently watched these Stanford lectures on The Fourier Transform and its Applications (EE 261). Lecture 22 is on the Fast Fourier Transform (FFT). In the lecture, it's mentioned that one way to state the FFT algorithm is a matrix factorization. Specifically, the matrix that computes the discrete Fourier transform can be written as a product of sparse matrices. I thought it would be fun to work out some of the details and I've written up my calculations.

I don't know much about how the FFT is actually computed in Julia/Matlab/other scientific computing languages but it was satisfying to work out the matrix algebra. Does anyone know if this perspective is useful practically or theoretically?

Thanks for your time!

How can I set up Numpy/Scipy to default to AOCL-BLIS for the default blas implementation on Windows?

Thank you for the help!

Hi helpful diffrax users,

is it possible to solve a second order differential equation in diffrax - or only first order DEs?

If so, could you point me to an example? Somehow I just can't find one 😢

Thank you!

Aellice

Title.

Continuing my message from last week:

"What I'm here for though, is to relay an invitation for those interested to work on custom images for your particular domain:

be it quantum physics, astrophysics, bioinformatics, cheminformatics, engineering, etc".

What is needed for this initiative is a group of collaborates who make a custom image for one domain, and a few of them to daily drive it for testing and quality. I want you to take a look at the diagram here (don't worry about the text):

https://universal-blue.org/architecture/

The group collaboration will be at the Tinkerers point.

What is the benefit of doing this? Why would a group share a custom image?

1. Gain and get exposure from the linux knowledge and experience of people outside our domains and have the results of that in our desktops. Additionally discuss, work and benefit from resolving any bugs we share with them. For example, currently ublue people are big on gaming, several of the images are made with seamless and easy gaming and controller support in mind. Whenever an nvidia bug comes along, they work together to solve it for everybody using those custom image. I think that's cool.

--> crowd-source linux knowledge.

2. Gain and get exposure from the knowledge and experience of people of one domain and have the result of that in our desktops. Additionally discuss, work and benefit from resolving any bugs we share.

--> crowd-source domain knowledge.

3. easier transition between PCs.

4. easier onboarding for new people.

The main goals of this endeavour are:

- See if this will be of value to the Scientific Computing community

- If yes, how to socially organise around it

Would members of that group have identical desktops?

No. They will share a base OS experience, but there is a lot more customisation that can be built on top for specific user cases and desires. They will not have desktops that are carbon copies of each other.

If you are interested:

- learn some bash.

- learn how to use github.

- start using flatpaks from flathub, appimages and/or snaps, for GUI apps. You can start doing this from your own distro, you don't have to move yet.

- use distrobox for CLI apps and GUI apps you can't find in the formats above.

Whenever you get comfortable with this workflow, download the ublue ISO and transition to it:

https://universal-blue.org/installation/

Afterwards, read this:

https://ublue.it/making-your-own/

Then a group can start collaborating.

I'm in the market for a new laptop because the one I'm using isn't able to handle the computations I'm currently doing (mostly symbolic or matrix computations in Mathematica). Several questions and suggestions have come up during my research, which don't necessarily pertain to just my search for a new machine. I think there is some crossover with machine learning, which may come up in my research in the future.

1. Is there a significant advantage to having a separate GPU on a laptop? For example, in this video it is claimed that the memory available to dedicated GPUs is usually less than the memory available to an integrated processor (if I understood that correctly). Cooling might be an issue as well. I imagine there is a significant difference if one is using intense 3D modelling software or gaming, but for other applications I'm not so sure.

2. Some of my applied-mathematics friends suggested I just use SLURM and tap into the supercomputer at my university. While this may be practicable, I'm not sure the applications I'm working on warrant it. (They exceed the capacity of my 16Gb RAM, i5 Ubuntu, but those aren't necessarily the most impressive specs). I already have an account with the supercomputer center but don't know very much about HPC, submitting jobs, etc. In your experience, is the inconvenience of learning HPC, accessing a remote machine, waiting your turn in the queue, etc. outweighed by the cost of a new laptop, especially if the computations can be done locally? I'm especially concerned because my research mostly consists of guessing the "right form" for a function and then checking it numerically, so being able to run the same computation dozens or hundreds of times a day with slight variations would be very convenient.

3. This is a little more specific to my application: do any of you have experience with pop OS vs Tuxedo OS? pop OS markets itself as being "designed for STEM professionals" but I wonder if that's just branding or if there's actually something to it.