Comments (2)
We use by default multithreading. Juno automatically starts as many threads as there are logical CPU cores on your machine. In general the number of threads is governed by the environment variable JULIA_NUM_THREADS
. I think for Jupyter you have to enable this manually (here is a discussion on Discourse regarding that topic).
You can check the number of threads by Threads.nthreads()
. The matrix multiplication could multithread since BLAS has his own multithreading.
from homotopycontinuation.jl.
Thank you, I found something while googling about using
using Distributed
addprocs(6)
but this didn't work either. However, now when I use Threads.nthreads()
as you suggested I can see that the answer is still 1, which helps explains it didn't work. Your link is more helpful, thanks.
from homotopycontinuation.jl.
Related Issues (20)
- Converting from Nemo.fmpq_mpoly to HomotopyContinuation.Expression HOT 3
- cache_min_length failing with empty iterator HOT 1
- Seed for mondromy not working as intended
- Symbolic linear system solving not supported by ModelKit HOT 2
- Issues dealing with BigFloat coefficients HOT 6
- find_start_pair failing on some linearly-parametrized square systems HOT 2
- `OP_POW` not defined HOT 5
- Add checkpoints for monodromy_solve
- HC fails to solve sanity check HOT 7
- Strange behaviour of numerical irreducible decomposition HOT 4
- Can't build a system with Unicode symbols in variable names HOT 2
- `Threads.resize_nthreads!` is deprecated HOT 1
- Non-real solution incorrectly certified as real
- Start System Complaints: Incorrect Rank Calculation HOT 2
- What is the best way to approximate trigonometric functions in HC HOT 2
- LaTeX pretty printing breaks for (negative) exponents HOT 2
- sing_accuracy not recognised as endgame parameter HOT 9
- Missing Solutions HOT 8
- Arblib dependencies incompatible with Oscar.jl HOT 13
- Small bugs in `n_components` HOT 2
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from homotopycontinuation.jl.