# Why @parallel doing nothing?
**URL:** https://discourse.julialang.org/t/why-parallel-doing-nothing/7010
**Category:** General Usage
**Created:** [November 11, 2017, 5:27pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010 "2017-11-11T17:27:58Z")
**Posts on this page:** 12
**Page:** 1
### Author:  [@programista](https://discourse.julialang.org/u/programista)
#### Post date: [November 11, 2017, 5:27pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/1 "2017-11-11T17:27:59Z")
Julia 5.0
Why my @parallel doing nothing ? all other proceses are OK .
julia\>addprocs(5)
…
file=jldopen(“U\_baza\_Ski\S\_Ski”)
julia\> tic()
0x000015af252b402e
julia\> @parallel for i=0:l-1
eval(parse(“S=read(file,"Sw$i")”))
minuty=zeros(minut)
for j=1:uu
minuty+=cumsum(S[:,j])
if mod(j,1000)==0 println(j) end
end
wynik[:,i+1]=minuty
println(i)
end
5-element Array{Future,1}:
Future(2,1,7,#NULL)
Future(3,1,8,#NULL)
Future(4,1,9,#NULL)
Future(5,1,10,#NULL)
Future(6,1,11,#NULL)
julia\> toc()
elapsed time: 57.977429403 seconds
57.977429403
Paul
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [November 11, 2017, 8:00pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/2 "2017-11-11T20:00:24Z")
Don’t think of it as a loop: it’s quite different in some respects. You should give a reduction, otherwise it returns the result of each iteration. Also, you’ll want to `@sync` that if you want the values computed before you use them.
---
### Author:  [@TomConlin](https://discourse.julialang.org/u/TomConlin)
#### Post date: [November 11, 2017, 10:00pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/3 "2017-11-11T22:00:50Z")
because without a “reducer” the process @parallel launches the loops and gets on with the next instructions without waiting for the futures to be fetchable.
to make @parallel wait for all those futures to return put an @sync in front of it or
come up with a reduce() function
```julia
@sync @parallel for ...
```
or
```julia
@parallel (reduce) for ...
```
---
### Author:  [@programista](https://discourse.julialang.org/u/programista)
#### Post date: [November 12, 2017, 8:50am UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/4 "2017-11-12T08:50:25Z")
😕
This did not work:
minuty=SharedArray(Float64,minut)
@sync @parallel for j=1:uu
minuty+=cumsum(S[:,j])
if mod(j,1000)==0 println(j) end
end
this work :
minuty=SharedArray(Float64,minut)
for j=1:uu
minuty+=cumsum(S[:,j])
if mod(j,1000)==0 println(j) end
end
Paul
W dniu 2017-11-11 o 23:06, Tom Conlin pisze:
---
### Author:  [@bernhard](https://discourse.julialang.org/u/bernhard)
#### Post date: [November 12, 2017, 1:10pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/5 "2017-11-12T13:10:49Z")
I am not sure if a simple sum will show benefits if parallelized.
Consider the example below (where I have inserted a small waiting time)
```julia
S=rand(300,12);
@everywhere function my_cumsum(x)
sleep(.5)
return cumsum(x)
end
function mi(S)
res=@parallel (+) for j=1:size(S,2)
my_cumsum(S[:,j])
end
return res
end
mi(S);
@time res=mi(S); #time is 6 seconds on 1 core
addprocs(4)
@everywhere function my_cumsum(x)
sleep(.5)
return cumsum(x)
end
mi(S);
@time res2=mi(S); #time is 1.5s on 4 cores
```
---
### Author:  [@programista](https://discourse.julialang.org/u/programista)
#### Post date: [November 12, 2017, 2:02pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/6 "2017-11-12T14:02:01Z")
Thanks is very useful, but i am lookingo too to compute cumsum of all
columns of S SEPARATLY.
> >
Paul
W dniu 2017-11-12 o 14:15, bernhard pisze:
---
### Author:  [@TomConlin](https://discourse.julialang.org/u/TomConlin)
#### Post date: [November 12, 2017, 9:13pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/7 "2017-11-12T21:13:13Z")
continuing with @bernhard standalone example
```julia
function mi(N)
S=rand(N,N)
result=SharedArray(Float64,(N,N))
@sync @parallel for j=1:size(S,2)
result[:,j]= cumsum(S[:,j])
end
fetch(result)
end
@time r=mi(300);
size(r)
```
---
### Author:  [@programista](https://discourse.julialang.org/u/programista)
#### Post date: [November 13, 2017, 10:04am UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/8 "2017-11-13T10:04:52Z")
Unfornatly is longer becouse job is moving to SWAP
my ram = 8 G, job in swap with 2 workeres is 13 G 😉 and evrything is
slower:/ What now?🙂
> S=read(file,“S0”)
> 263641×5693 sparse matrix with 1693405 Float64 nonz
function cum(S)
k,N=size(S)
result=SharedArray(Float64,(k,N))
@sync @parallel for j=1:N
result[:,j]= cumsum(S[:,j])
end
fetch(result)
end
@time sum(S)
114 sec
(Of coures is to long becose I have of thousends Arrays like S )
addprocs(1)
@time sum(S)
400 sec !!!
Paul
W dniu 2017-11-12 o 22:18, Tom Conlin pisze:
---
### Author:  [@TomConlin](https://discourse.julialang.org/u/TomConlin)
#### Post date: [November 13, 2017, 5:31pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/9 "2017-11-13T17:31:16Z")
get more ram is one solution, which is part a throw away joke and part true, parallel work typically requires more resources than serial.
Running this on a machine that is already near capacity for serial will require extra work.
Your goal will be to partition `S` into as many parts as you have processors and each process only receive its portion; perhaps using DistributedArrays, or perhaps coercing `S` to a Shared array could work.
But I am only a little bit ahead of where you are and I have not needed DistributedArrays yet, so hopefully some one who has will chime in.
---
### Author:  [@programista](https://discourse.julialang.org/u/programista)
#### Post date: [November 13, 2017, 5:39pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/10 "2017-11-13T17:39:15Z")
Thanks, I am steel looking 🙂
Paul;
W dniu 2017-11-13 o 18:36, Tom Conlin pisze:
---
### Author:  [@programista](https://discourse.julialang.org/u/programista)
#### Post date: [November 13, 2017, 7:51pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/11 "2017-11-13T19:51:09Z")
Unfornatly is longer becouse job is moving to SWAP
my ram = 8 G, job in swap with 2 workeres is 13 G 😉 and evrything is
slower:/ What now?🙂
> S=read(file,“S0”)
> 263641×5693 sparse matrix with 1693405 Float64 nonz
function cum(S)
k,N=size(S)
result=SharedArray(Float64,(k,N))
@sync @parallel for j=1:N
result[:,j]= cumsum(S[:,j])
end
fetch(result)
end
@time sum(S)
114 sec
(Of coures is to long becose I have of thousends Arrays like S )
addprocs(1)
@time sum(S)
400 sec !!!
Paul
W dniu 2017-11-12 o 22:18, Tom Conlin pisze:
---
### Author:  [@TomConlin](https://discourse.julialang.org/u/TomConlin)
#### Post date: [November 13, 2017, 11:05pm UTC](https://discourse.julialang.org/t/why-parallel-doing-nothing/7010/12 "2017-11-13T23:05:36Z")
- reread my previous suggestion
(your `S` needs to avoid being copied in its entirety for each process)
- consider understanding Distributed Arrays
([https://github.com/JuliaParallel/DistributedArrays.jl#distributed-arrays](https://github.com/JuliaParallel/DistributedArrays.jl#distributed-arrays))
- consider doing your own in place edits on S instead of creating a result copy
(you still have the original on disk if you need it)