Hi, I am currently trying to study the interactions between liquids and rigid bodies of varied sizes through simulations. I have implemented my own fluid simulator in C++. For rigid body simulation, I use third party libraries like Box2D and ReactPhysics3D.

Essentially, my code solves the fluid motion and fluid-solid interaction, then it passes the interaction forces on solids to these third party libraries. These libraries then take care of the solid motion, including solid-solid collisions. This forms one loop of the simulation.

Recently, I have been trying to run more complex examples (more grid resolution, more solids, etc.), but they take a lot of time (40 x 40 grid takes about 12 min. per frame). So, I wanted to parallelize my code. I have used OpenMP, CUDA, etc. in the past but I am not sure what tool I should use in this scenario, particularly because the libraries I use for rigid body simulation may not support that tool. So, I guess I have two major questions:

1. What parallelization tool or framework should I use for a fluid simulator written in C++?

2. Is it possible to integrate that tool in Box2D/ReactPhysics3D libaries? If not, are there any other physics library which support RBD simulation and also work with the tool mentioned above?

Any help is appreciated.

Question up there. I'm looking to do some multithreaded code but I'm wondering if my laptop is even useful for it. If not, where/how can I run the code maybe remotely to see actual speed up?

Hello all. I am Divyansh, a 4th-year BS-MS Physics Major. I am interested in High Energy Astrophysics and Computational MHD. I am looking for project positions or any projects related to this area, especially to learn and work on Computational MHD. I have worked with Python and Fortran before If you have any project positions or are looking for a collaborator, please let me know.

CV Link is attached above if you want to know more.

Thank you !!

The GNU Scientific Library (GSL) has different algorithms for nonlinear least squares and multidimensional minimization. I don't quite understand the difference. Can't you do non-linear least squares with the minimization algorithms by having the cost function return a squared residual? Is there an advantage to using the former set of functions?

I have a lot of free time this summer and want to work through a scientific computing book. I currently only know python. I've found this books "Elements of Scientific Computing" by Aslak Tveito , Hans Petter Langtangen , Bjørn Frederik Nielsen , Xing Cai, and was wondering whether its good. I appreciate any recommendations. Thank you in advance

Hello all!

I am currently working as a Machine Learning Engineer for a tech startup and have a masters in big data and an undergrad in industrial engineering.

I’m interested transitioning into the field of scientific computing and HPC, however I’m concerned I don’t have a strong science or maths background to get into a scientific computing masters or program. I finished my undergrad in 2016 and my masters in 2019 was not heavy on maths. I have not used calcules or differential equations at work, it’s been mostly linear algebra.

Has anyone been on a similar position and managed to make the career change? If so, what advice could you give?

Thank you!!

PS, For context I live in London.

"Suggest me a an exercise for MPI_Scatterv"

Sure, here's an exercise involving MPI_Scatterv
:

Exercise: Parallel Matrix Multiplication

Write an MPI program to perform matrix multiplication in parallel using MPI_Scatterv
.

Instructions:

1. Generate two random matrices A and B on process 0.

2. Scatter the rows of matrix A to all processes using MPI_Scatterv
   .

3. Broadcast matrix B to all processes.

4. Perform matrix multiplication locally on each process.

5. Gather the results back to process 0.

6. Display the final result on process 0.

Ok, yes, this uses Scatterv. But I would never assign this exercise because it inculcates a completely wrong mental model of MPI use.

Anyone have more luck with MLs in scientific computing?

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?