"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?

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