I am using MCMCglmm to run a PGLMM model. Since the aim is not to make predictions, I'm using dredge (from MuMIn) to calculate model-weighted parameter values a
I have this code: #pragma acc kernels #pragma acc loop seq for(i=0; i<bands; i++) { mean=0; #pragma acc loop seq for(j=0; j<N; j++) m
I'm currently building server software in Java. I already have a running backend, which is build with Spring Boot. It has an REST interface to read and write da
I am starting to have a big project and I am currently using and including many of packages and .jl files: a = time() @info "Loading JuMP" using JuMP @info "Loa
tbb::parallel_for(0, 33, [&](int indexNum) { print(indexNum) }); hi, I expect the indexNum to be unique numbers and to print unique numbers. But in practic
I have a quick question with respect to the doParallel package in R. I have an optimize.R file where it contains roughly 18 functions A1, A2, A3, A4, ..., A18 w
Many are familiar with foreach() to assign a loop across many cores in parallel using %dopar%. However, in R how do you send a single job request for a variety
Would someone be able to clarify what each of these things actually are? From what I gathered, nodes are computing points within the cluster, essentially a sing
It might be a silly question but, with OpenMP you can achieve to distribute the number of operations between all the cores your CPU has. Of course, it is going
I have two tensors that are batches of matrices: x = torch.randn(100,10,10) y = torch.randn(100,2,2) I want to parallelize the kronecker on each matrix, not d
I have a model.predict()-method and 65536 rows of data which takes about 7 seconds to perform. I wanted to speed this up using the joblib.parallel_backend tooli
I am running a python script which uses scipy.optimize.differential_evolution to find optimum parameters for given data samples. I am processing my samples seq
I have two programs server and client. server terminates after an unknown duration. I want to run client in parallel to server (both from the same Bash script)
This is probably very basic, but I am not a Java person. Here is my processing code which simply prints and sleeps: private static void myProcessings(int va
I have a mac (MacOs 10.15.4, Python ver 3.82) and need to work in multiprocessing, but on my pc the procedures doesn’t work. For example, I have copied a
In my case I have a test file containing a few hundred tests using jest describe('my test-suite', () => { test('test 1', () => { expect(1).toBe(
I wasn't expecting this to happen. The relevant code pieces are: import os import tensorflow as tf os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices' .
I'm running a simple kernel which adds two streams of double-precision complex-values. I've parallelized it using OpenMP with custom scheduling: the slice_indic
I have included failsafe plugin with parallel methods and threadcount 4. And framework is cucumber with junit. I'm trying to run features in parallel with fails
I don't have a big knowledge in hardware, GPU and CPU so I'm trying to create it. I have a server with N processor, the description for each of them is more or