# Minimizing Rounding Errors with IDL's TOTAL function

The idea for my blog topic this week came from a question
from Ron Kneusel about the loss of precision using IDL’s TOTAL function. IDL functions
are often implemented in a way that makes them run fast, since processing speed
is important. This is certainly true for the TOTAL function. If you are not using
any of the keywords and additional arguments, then it is basically adding to
the sum in the input array order. The exception is if you have enough elements
in the array to trigger the multi-threaded code path, which I will ignore for
now.

The issue with loss of precision is therefore best seen if
you start with the largest term and keep adding smaller and smaller terms. At
some point, the small term gets so small compared to the large sum that the
additional terms get lost in round off.

Here is an example of a sum that we know should approach 128
(with a deviation in the 52 decimal place):

IDL> data = (

**1**-**1d**/**128**) ^**lindgen**(**15000**)IDL>

**128**-**total**(data) 5.2580162446247414e-013

So, we are getting an error in the 13

^{th}decimal place in this case. This array starts with the largest term and ends with the smallest term, so that is the worst case for the TOTAL implementation. If we reverse the order of the terms, we get:IDL>

**128**-**total**(**reverse**(data)) 5.6843418860808015e-014

The error gets 10 times smaller in this case. I decided to
compare the algorithm that Ron suggested, which is called Kahan sum, with a
divide-and-conquer scheme that I have previously used for execution on a
massively parallel GPU (which runs fast with 10000’s of independent threads).
The Kahan sum algorithm can be found on Wikipedia and the IDL code is pretty
short, (but very slow):

; Kahan algorithm, from wikipedia

**function**

**KahanSum**, data

sum =

**0d** c =

**0d****for**i=

**0**, data.

*length*-

**1**

**do**

**begin**

y = data[i] - c

t = sum + y

c = (t - sum) - y

sum = t

**endfor**

**return**, sum

**end**

The massively parallel algorithm will run much faster than
the Kahan Sum, and the IDL code is listed here:

; Divide and concure total,

; algorithm lends itself to massively parallel execution

**function**

**total_mp**, data

**compile_opt**idl2,logical_predicate

n =

**ishft**(**1ull**,**total**(**ishft**(**1ull**,**lindgen**(**63**))**lt****n_elements**(data),/integer)) pad =

**make_array**(n, type=**size**(data,/type)) pad[

**0**] = data[*]**while**n

**gt**

**1**

**do**

**begin**

pad[

**0**:n/**2**-**1**] += pad[n/**2**:n-**1**] n /=

**2****endwhile**

**return**, pad[

**0**]

**end**

Here are the result with the decreasing magnitude terms:

IDL>

**128**-**KahanSum**(data) 5.6843418860808015e-014

IDL>

**128**-**total_mp**(data) 5.6843418860808015e-014

So, in this case the error is similar to the best case
sorted input to TOTAL. As an independent test, I also tried randomly ordering
the terms, and both KahanSum and TOTAL_MP, are still consistent on the order of
5e-14:

IDL>

**128**-**KahanSum**(data[order]) 5.6843418860808015e-014

IDL>

**128**-**total_mp**(data[order]) 5.6843418860808015e-014

IDL>

**128**-**total**(data[order]) 4.1211478674085811e-013

My conclusion is that the TOTAL_MP example is just as
accurate as the Kahan sum, and has the added advantage of allowing for
massively parallel execution, as opposed to the highly sequential execution
needed for the Kahan algorithm.