Re: [julia-users] matrix multiplications

[Cameron McBride]

I agree on the consistency part with other in-place operations. I certainly
don’t feel it’s hard to just use A_mul_B!, but my familiarity with BLAS
shouldn’t be taken for granted. And if an in-place operator syntax exists
in the language already…

How about the flip side of the argument, it appears to works now — but
doesn’t do what one expects. I understand why it doesn’t, and not a big
deal with an evolving landscape of a developing language, but it certainly
can be confusing and suggests matrices are a different beast. I’m not sure
that is consistent with Julian way.  But yeah, no fusing like broadcast so
the gain is much reduced in implementing this.

Cameron

On Wed, Oct 19, 2016 at 8:58 AM, Milan Bouchet-Valat 
wrote:

> Le mardi 18 octobre 2016 à 15:28 -0700, Steven G. Johnson a écrit :
> >
> >
> > > Since it uses the in-place assignment operator .= it could be made
> > > to work as desired, but there's some designing to do.
> > >
> >
> > The problem is that it doesn't know that * is a matrix multiplication
> > until compile-time.
> >
> > In any case, I don't think there's a huge amount to be gained from
> > special syntax here.  Unlike broadcast operations, matrix
> > multiplications cannot in general be fused with other operations.
> > So you might as well do A_mul_B!.
> I think the biggest gain would be discoverability and consistency with
> other in-place operations. A_mul_B! isn't the most Julian of our APIs
> (to say the least)...
>
>
> Regards
>

Re: [julia-users] matrix multiplications

[zamani . majid1989]

yes exactly ! 

On Wednesday, October 19, 2016 at 4:28:21 PM UTC+3:30, Milan Bouchet-Valat 
wrote:
>
> Le mardi 18 octobre 2016 à 15:28 -0700, Steven G. Johnson a écrit : 
> > 
> > 
> > > Since it uses the in-place assignment operator .= it could be made 
> > > to work as desired, but there's some designing to do. 
> > > 
> > 
> > The problem is that it doesn't know that * is a matrix multiplication 
> > until compile-time. 
> > 
> > In any case, I don't think there's a huge amount to be gained from 
> > special syntax here.  Unlike broadcast operations, matrix 
> > multiplications cannot in general be fused with other operations.   
> > So you might as well do A_mul_B!. 
> I think the biggest gain would be discoverability and consistency with 
> other in-place operations. A_mul_B! isn't the most Julian of our APIs 
> (to say the least)... 
>
>
> Regards 
>

Re: [julia-users] matrix multiplications

[Milan Bouchet-Valat]

Le mardi 18 octobre 2016 à 15:28 -0700, Steven G. Johnson a écrit :
> 
> 
> > Since it uses the in-place assignment operator .= it could be made
> > to work as desired, but there's some designing to do.
> > 
> 
> The problem is that it doesn't know that * is a matrix multiplication
> until compile-time.
> 
> In any case, I don't think there's a huge amount to be gained from
> special syntax here.  Unlike broadcast operations, matrix
> multiplications cannot in general be fused with other operations.  
> So you might as well do A_mul_B!.
I think the biggest gain would be discoverability and consistency with
other in-place operations. A_mul_B! isn't the most Julian of our APIs
(to say the least)...


Regards

Re: [julia-users] matrix multiplications

[Steven G. Johnson]

On Tuesday, October 18, 2016 at 4:10:57 PM UTC-4, Stefan Karpinski wrote:
>
> Since it uses the in-place assignment operator .= it could be made to work 
> as desired, but there's some designing to do.
>

The problem is that it doesn't know that * is a matrix multiplication until 
compile-time.

In any case, I don't think there's a huge amount to be gained from special 
syntax here.  Unlike broadcast operations, matrix multiplications cannot in 
general be fused with other operations.   So you might as well do A_mul_B!. 

Re: [julia-users] matrix multiplications

[Stefan Karpinski]

Since it uses the in-place assignment operator .= it could be made to work
as desired, but there's some designing to do.

On Tue, Oct 18, 2016 at 2:55 PM, Cameron McBride 
wrote:

> You mean like the following?
>
> julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B); Y.= A *
> B
>
> This doesn’t work as you might hope. I believe it just creates a temporary
> result of A*B and then stuffs it into the preexisting Y.
>
> On Tue, Oct 18, 2016 at 2:41 PM, Jérémy Béjanin 
> wrote:
>
>> I know, I was asking about that being the default behaviour of *
>>
>> On Tuesday, October 18, 2016 at 2:10:14 PM UTC-4, Stefan Karpinski wrote:
>>>
>>> That's what A_mul_B! does.
>>>
>>> On Tue, Oct 18, 2016 at 1:45 PM, Jérémy Béjanin 
>>> wrote:
>>>
 I think this is something I might have read about in the past, but are
 there plans to make y = a*b use an already allocated y?

 On Tuesday, October 18, 2016 at 12:38:00 PM UTC-4, Stefan Karpinski
 wrote:
>
>   A_mul_B!(Y, A, B) -> Y
>
>   Calculates the matrix-matrix or matrix-vector product A⋅B and stores
> the result in Y, overwriting the
>   existing value of Y. Note that Y must not be aliased with either A
> or B.
>
>   julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B);
> A_mul_B!(Y, A, B);
>
>   julia> Y
>   2×2 Array{Float64,2}:
>3.0  3.0
>7.0  7.0
>
> On Tue, Oct 18, 2016 at 10:27 AM,  wrote:
>
>> hi guys
>> is there a way to reduce allocated memory in matrix multiplications?
>>
>> for example this code blew in my machine gives :
>>
>> function test4(n)
>>   a = rand(n,n)
>>   for i = 1:100
>>  a*a
>>end
>>  end
>>
>>
>> -- answer  --
>> test4(1)
>> # force compiling
>>
>> @time test4(1000)
>> 16.589743 seconds (433 allocations: 770.587 MB, 0.68% gc time)
>>
>
>
>>>
>

Re: [julia-users] matrix multiplications

[Cameron McBride]

You mean like the following?

julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B); Y.= A * B

This doesn’t work as you might hope. I believe it just creates a temporary
result of A*B and then stuffs it into the preexisting Y.

On Tue, Oct 18, 2016 at 2:41 PM, Jérémy Béjanin 
wrote:

> I know, I was asking about that being the default behaviour of *
>
> On Tuesday, October 18, 2016 at 2:10:14 PM UTC-4, Stefan Karpinski wrote:
>>
>> That's what A_mul_B! does.
>>
>> On Tue, Oct 18, 2016 at 1:45 PM, Jérémy Béjanin 
>> wrote:
>>
>>> I think this is something I might have read about in the past, but are
>>> there plans to make y = a*b use an already allocated y?
>>>
>>> On Tuesday, October 18, 2016 at 12:38:00 PM UTC-4, Stefan Karpinski
>>> wrote:

   A_mul_B!(Y, A, B) -> Y

   Calculates the matrix-matrix or matrix-vector product A⋅B and stores
 the result in Y, overwriting the
   existing value of Y. Note that Y must not be aliased with either A or
 B.

   julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B);
 A_mul_B!(Y, A, B);

   julia> Y
   2×2 Array{Float64,2}:
3.0  3.0
7.0  7.0

 On Tue, Oct 18, 2016 at 10:27 AM,  wrote:

> hi guys
> is there a way to reduce allocated memory in matrix multiplications?
>
> for example this code blew in my machine gives :
>
> function test4(n)
>   a = rand(n,n)
>   for i = 1:100
>  a*a
>end
>  end
>
>
> -- answer  --
> test4(1)
> # force compiling
>
> @time test4(1000)
> 16.589743 seconds (433 allocations: 770.587 MB, 0.68% gc time)
>


>>

Re: [julia-users] matrix multiplications

[Jérémy Béjanin]

I know, I was asking about that being the default behaviour of *

On Tuesday, October 18, 2016 at 2:10:14 PM UTC-4, Stefan Karpinski wrote:
>
> That's what A_mul_B! does.
>
> On Tue, Oct 18, 2016 at 1:45 PM, Jérémy Béjanin  > wrote:
>
>> I think this is something I might have read about in the past, but are 
>> there plans to make y = a*b use an already allocated y?
>>
>> On Tuesday, October 18, 2016 at 12:38:00 PM UTC-4, Stefan Karpinski wrote:
>>>
>>>   A_mul_B!(Y, A, B) -> Y
>>>
>>>   Calculates the matrix-matrix or matrix-vector product A⋅B and stores 
>>> the result in Y, overwriting the
>>>   existing value of Y. Note that Y must not be aliased with either A or 
>>> B.
>>>
>>>   julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B); 
>>> A_mul_B!(Y, A, B);
>>>
>>>   julia> Y
>>>   2×2 Array{Float64,2}:
>>>3.0  3.0
>>>7.0  7.0
>>>
>>> On Tue, Oct 18, 2016 at 10:27 AM,  wrote:
>>>
 hi guys
 is there a way to reduce allocated memory in matrix multiplications?

 for example this code blew in my machine gives :

 function test4(n)
   a = rand(n,n)
   for i = 1:100
  a*a
end
  end


 -- answer  --
 test4(1)
 # force compiling 

 @time test4(1000)
 16.589743 seconds (433 allocations: 770.587 MB, 0.68% gc time)

>>>
>>>
>

Re: [julia-users] matrix multiplications

[Stefan Karpinski]

That's what A_mul_B! does.

On Tue, Oct 18, 2016 at 1:45 PM, Jérémy Béjanin 
wrote:

> I think this is something I might have read about in the past, but are
> there plans to make y = a*b use an already allocated y?
>
> On Tuesday, October 18, 2016 at 12:38:00 PM UTC-4, Stefan Karpinski wrote:
>>
>>   A_mul_B!(Y, A, B) -> Y
>>
>>   Calculates the matrix-matrix or matrix-vector product A⋅B and stores
>> the result in Y, overwriting the
>>   existing value of Y. Note that Y must not be aliased with either A or B.
>>
>>   julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B);
>> A_mul_B!(Y, A, B);
>>
>>   julia> Y
>>   2×2 Array{Float64,2}:
>>3.0  3.0
>>7.0  7.0
>>
>> On Tue, Oct 18, 2016 at 10:27 AM,  wrote:
>>
>>> hi guys
>>> is there a way to reduce allocated memory in matrix multiplications?
>>>
>>> for example this code blew in my machine gives :
>>>
>>> function test4(n)
>>>   a = rand(n,n)
>>>   for i = 1:100
>>>  a*a
>>>end
>>>  end
>>>
>>>
>>> -- answer  --
>>> test4(1)
>>> # force compiling
>>>
>>> @time test4(1000)
>>> 16.589743 seconds (433 allocations: 770.587 MB, 0.68% gc time)
>>>
>>
>>

Re: [julia-users] matrix multiplications

[Jérémy Béjanin]

I think this is something I might have read about in the past, but are 
there plans to make y = a*b use an already allocated y?

On Tuesday, October 18, 2016 at 12:38:00 PM UTC-4, Stefan Karpinski wrote:
>
>   A_mul_B!(Y, A, B) -> Y
>
>   Calculates the matrix-matrix or matrix-vector product A⋅B and stores the 
> result in Y, overwriting the
>   existing value of Y. Note that Y must not be aliased with either A or B.
>
>   julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B); 
> A_mul_B!(Y, A, B);
>
>   julia> Y
>   2×2 Array{Float64,2}:
>3.0  3.0
>7.0  7.0
>
> On Tue, Oct 18, 2016 at 10:27 AM,  
> wrote:
>
>> hi guys
>> is there a way to reduce allocated memory in matrix multiplications?
>>
>> for example this code blew in my machine gives :
>>
>> function test4(n)
>>   a = rand(n,n)
>>   for i = 1:100
>>  a*a
>>end
>>  end
>>
>>
>> -- answer  --
>> test4(1)
>> # force compiling 
>>
>> @time test4(1000)
>> 16.589743 seconds (433 allocations: 770.587 MB, 0.68% gc time)
>>
>
>

Re: [julia-users] matrix multiplications

[Stefan Karpinski]

  A_mul_B!(Y, A, B) -> Y

  Calculates the matrix-matrix or matrix-vector product A⋅B and stores the
result in Y, overwriting the
  existing value of Y. Note that Y must not be aliased with either A or B.

  julia> A=[1.0 2.0; 3.0 4.0]; B=[1.0 1.0; 1.0 1.0]; Y = similar(B);
A_mul_B!(Y, A, B);

  julia> Y
  2×2 Array{Float64,2}:
   3.0  3.0
   7.0  7.0

On Tue, Oct 18, 2016 at 10:27 AM,  wrote:

> hi guys
> is there a way to reduce allocated memory in matrix multiplications?
>
> for example this code blew in my machine gives :
>
> function test4(n)
>   a = rand(n,n)
>   for i = 1:100
>  a*a
>end
>  end
>
>
> -- answer  --
> test4(1)
> # force compiling
>
> @time test4(1000)
> 16.589743 seconds (433 allocations: 770.587 MB, 0.68% gc time)
>

[julia-users] matrix multiplications

[zamani . majid1989]

hi guys
is there a way to reduce allocated memory in matrix multiplications?

for example this code blew in my machine gives :

function test4(n)
  a = rand(n,n)
  for i = 1:100
 a*a
   end
 end


-- answer  --
test4(1)
# force compiling 

@time test4(1000)
16.589743 seconds (433 allocations: 770.587 MB, 0.68% gc time)