I’m fairly new to julia, and not overly experienced with scaling to huge numbers of processes, so forgive me if I’m missing something obvious. Alright, to be honest, I actually used a couple thousand cpu-hours to run using DoubleFloats, but it looks like 1+2 would have done the same. From my testi…

I’ve hacked together a precompilation script here , which produces a sysimage with all the necessary Distributed code precompiled. This drastically reduces the time taken to call @everywhere 1+2. I’ve tested it on 100 processes, and the time goes down from ~70 seconds to ~2 seconds. If this scales…

Why does julia use thousands of cpu-hours to compute 1+2?

General Usage

moble August 8, 2021, 10:02pm 2

See also here and here.

Topic		Replies	Views
@everywhere takes a very long time when using a cluster General Usage	16	2942	February 11, 2022
Parallel sampling Performance	5	444	June 4, 2021
For loop in function and multiplication of larger matrices, slow speed in parallel Performance performance , parallel , loops	3	1283	November 22, 2019
@everywhere works, sort of General Usage distributed , parallel-computing	2	118	July 8, 2024
Using Julia with @parallel pmap or blank makes no difference in speed. Julia at Scale	3	848	March 22, 2018

Why does julia use thousands of cpu-hours to compute 1+2?

Related topics