htop | htop - an interactive process viewer | Command Line Interface library

Here form is submitted via POST method but password given in the input field of type=password not assigning to 'upassword' given in the userregister function.When I print the 'upassword' it gives an output "None".Also it gives an error like this when I give JAVASCRIPT validation.

So I am running a slurm job across 2 laptops (2 nodes), and I notice both laptop gets insanely laggy, to the point that the mouse could not even move. When I uses htop I saw the job was using 4 cores in 100% usage. I know for sure that the job does not take up that many cpu usage. How do I make slurm so that it uses only the required amount of cpu power?

I have a Python 3.6 data processing task that involves pre-loading a large dict for looking up dates by ID for use in a subsequent step by a pool of sub-processes managed by the multiprocessing module. This process was eating up most if not all of the memory on the box, so one optimisation I applied was to 'intern' the string dates being stored in the dict. This reduced the memory footprint of the dict by several GBs as I expected it would, but it also had another unexpected effect.

Before applying interning, the sub-processes would gradually eat more and more memory as they executed, which I believe was down to them having to copy the dict gradually from global memory across to the sub-processes' individual allocated memory (this is running on Linux and so benefits from the copy-on-write behaviour of fork()). Even though I'm not updating the dict in the sub-processes, it looks like read-only access can still trigger copy-on-write through reference counting.

I was only expecting the interning to reduce the memory footprint of the dict, but in fact it stopped the memory usage gradually increasing over the sub-processes lifetime as well.

Here's a minimal example I was able to build that replicates the behaviour, although it requires a large file to load in and populate the dict with and a sufficient amount of repetition in the values to make sure that interning provides a benefit.

I wonder if someone can help explain what is happening?

I run 2 subprocesses, 1 for ffprobe and 1 for ffmpeg.

I am using mclapply in my R script for parallel computing. It saves overall memory usage and it is fast so I want to keep it in my script. However, one thing I noticed is that the number of child processes generated during running the script is more than the number of cores I specified using mc.cores. Specifically, I am running my script on a server with 128 cores. And when I run my script, I set mc.cores to 18. During the running of the script, I checked the processes related to my script using htop. First, I can find 18 processes like this: enter image description here

3_GA_optimization.R is my script. This all look good. But I also found more than 100 processes running at the same time with similar memory and CPU usage. The screenshot below shows some of them: enter image description here

The problem of this is that although I only required 18 cores, the script actually uses all the 128 cores on the server and this makes the server very slow. So my first question is why is this happening? And what is the difference between these processes with green color compared to the 18 processes with black color?

My second question is that I tried to use ulimit -Su 100 to set the soft limit of maximum number of processes that I can use before running Rscript 3_GA_optimization.R. I chose 100 based on the current number of processes I am using before running the script and the number of cores I want to use when running the script. However, I got an error saying:

Error in mcfork(): unable to fork, possible reason: Resource temporarily unavailable

So it seems that mclapply has to generate a lot more processes than mc.cores in order for the script to run, which is confusing to me. So my second question is that why does mclapply behaves in this way? Is there any other way to fix the total number of cores mclapply can use?

I have a somewhat large code that uses the libraries numpy, scipy, sklearn, matplotlib. I need to limit the CPU usage to stop it from consuming all the available processing power in my computational cluster. Following this answer I implemented the following block of code that is executed as soon as the script is run:

I am using ELK stack with Netflow module. First of all, when I checked CPU usage Logstash was using a lot of resources and I decided to stop it. This moment Elasticsearch/Kibana/Logstash is stopped. I mean, I ran command sudo service elasticsearch/kibana/logstash stop. Basically, I think that something is wrong with logstash. When I am see log in htop I am getting something like this, I do not understand why.

When checking logstash service status, getting something like this.

Logstash is still running, and I am trying to figure out how to stop it. I think, I ran it in a wrong manner at the start, but why not possible to stop it forever?

htop command shows the CPU is 100% used even tho I do not have the app running or anything else. The DigitalOcean dashboard metric shows this same data (100% usage) as well.

The top tasks on the htop list take less than 10% CPU usage. The biggest is pm2 taking ~5.2 % usage.

Is it possible that there are hidden tasks that are not displaying on the list and, in general, how I can start investigating what's going on?

My droplet used this one-click installation: https://marketplace.digitalocean.com/apps/nodejs

Thanks in advance!

Update 1)

The droplet has a lot of free disk space

I wrote a C++ code whose bottleneck is the diagonalization of a possibly large symmetric matrix. The code uses OpenMP, CBLAS and LAPACKE C-interfaces.

However, the call on dsyev runs on a single thread both on my local machine and on a HPC cluster (as seen by htop or equivalent tools). It takes about 800 seconds to diagonalize a 12000x12000 matrix, while NumPy's function eigh takes about 250 seconds. Of course, in both cases $OMP_NUM_THREADS is set to the number of threads.

Here is an example of a C++ code that calls LAPACK that is basically what I do in my program. (I am reading the matrix that is in binary format).