World: r3wp

would *like*

A thing should be noted.
repeat and foreach do a bind/copy of the evaluated block.

Even if they are the fastest loops, they should be not used too intensivly 
because they will polluate the memory.

It's particularly sensitive for graphics applications or services 
that linger in memory. 


So, that's why  I advise to use only LOOP, WHILE and UNTIL for intensive 
repeated loopings, if you don't want to blow up the memory used by 
your app.

Your bench doesn''t take in account the time taken by the GC to recycle 
 the memory.
Some functions polluate the memory some other not.
You should add the time needed to recycle after each test.

but perhaps i'm wrong, you take it in account

thanks steeve, I'm accumulating all comments

First revision of the benchmarks will include:
	-RAM stats

 -empty vs filled-up loops.  many words and a single func with the 
 same content called from the loop
	-GC de-activated tests + recycle time stats

as noted in the document test notes:

I specifically didn't do any GC control, cause I wanted, at this 
point, to see how the loops react under normal rebol execution.  

the GC normally is pretty aggressive and when you look at the tests, 
most loops roll for several hundred thousands times, so the GC will 
have kicked-in... if it can.

I did note, that there is a HUGE memory leak which occured probably 
in the actual benchmark procedure itself.


although I keep no reference to any of the data or transient test 
blocks and funcs, they are kept somewhere, and my rebol.exe process 
keeps growing and growing.... I caught it at 500MB !! but it didn't 
do any difference in actual speeds... after a few tests.... cause 
i was a bit scared.

this will also have to be investigated further (the leak)

I tried manually recycling... but it didn't do anything.

what do you mean ?, it does it here:


>> recycle s: stats loop 1000000 [foreach a [1 2 3][a: a]] print 
stats - s recycle print stats - s
1569504  ;memory allocated by the loop
-320          ; after the recycle

>> stats
== 541502965
>> recycle
>> stats
== 272784493


but that's just for about 10 % of the tests... the more tests I do 
the more ram stays "stuck" somewhere inside the interpreter.

yes, i noticed that too, it's a probem with R2

R3 is better with that

and if you activate recycle/on, does that make any difference ?

I think R2 GC can't determine co-dependent unused references... in 
some situations.
ex:
blk: reduce [ a: context [b: none] b: context [c: a] a/b: b ]
blk: none


in this case both a and b point to each other, and clearing blk doesn't 
tell a or b that they aren't used anymore... that is my guess.

yep, but your tests seem not having such cases

I reduce a block which is the test... and since foreach copy/deep, 
and there is NO word ever refering to the content of the refered 
block, I think the contents of the blocks prevent the blocks and 
the data they contain from being collected... 


the block contains words which are not GC counted as zero reference, 
so nothing gets de-allocated...

that's just my guess.

not sure I'm making sense... in how I explain it.

in any case I want to build a single script which does all the tests, 
statistics, and eventually graphics and html pages of all results 
in one (VERY) long process.

so I can better control how the tests are done and prevent automated 
test creation as I am doing now.

M: looks more like 50 thousands than 50 millions for repeat.
So take care of your powers of 10

as indicated in the document introductions... the repeat test is:

loop ops [repeat i 1000 []]   

so with 100000 ops taking near 2 seconds.  we end up with:

100,000 * 1000 / 2 = (50 million loops / second)

but I did miscalculate the remove-each MB/second create/scan/erase 
cycle... its not 100MB... its 10MB.

updated document.

Thanks for the info, Maxim. We can do a little deduction from that 
data to guess how REBOL is implemented. The scientific method :)

for my 3D engine, this base line test was neccessary.  I need to 
squeze every hz out of rebol... its nice to see how some exit conditions 
are 10-15% faster in some equivalebt tests...  who would have tought 
that 'PICK was faster than NOT TAIL  ?  :-/

For instance, 1000 > i would be faster than i < 1000 because ops 
redirect to actions, and actions dispatch based on the type of their 
first argument. If the first argument is a literal value, the action 
of that type can be called directly. If it is a referenced value, 
it woulkd have to be dereferenced first, which is apparently slower.


As for PICK being faster than NOT TAIL?, that is one native compared 
to two, with interpreter overhead between the two. Low-level natives 
like PICK, NOT and TAIL? don't do much in comparison with the interpreter 
overhead. Large numbers of small operations tend to be slower than 
small numbers of large operations, if the amount of work done is 
comparable. This is why structure manipulation is faster in REBOL 
than simple math.

better described than I would put it, but those where my assumptions... 
I will include this info in my next revision of the document.

I intend to devote a whole site to these tests eventually.  with 
a very extensive and comprehensive set of test functions and statistics.

With the typos fixed I hope :)

you learn a lot by doing it.

with the tests I did, I think I can probably optimise liquid by at 
least 20% just by changing the loops and changing none of the algorithms 
or features.


I am about to create a second reference liquid node type. which will 
be completely compatible from the outside, but with less features 
inside.  I expect to DOUBLE its processing capacity.

Code converted from R2 to R3 tends to need reoptimizing - the balance 
of what is native vs what is mezz has changed, usually for the better. 
All of the loop functions are native now, for instance. Also, some 
natives have been converted to actions, and vice versa.

The plus side is that what you would just do, before you try to optimize, 
tends to be faster in R3 than it is in R2.

yeah amd with the faster fuunction calling in R3 liquid should get 
an immediate boost in speed... it does A LOT of tiny function calls 
to manage the graph as if they where first level types with accessors 
instead of using a controler which loops over the graphs and limits 
flexibility.

.

just for Terry.....


rebol []


feach: func[
	series 
	value
	i "iterations"
	/local result st
][
	prin "feach(): "
	st: now/precise
	x: length? series
	
	while [i > 0][
		prin i
		prin "."
		result: make series 0 

  foreach[s p v] series [if s = value [append result reduce [s p v]]]
		i: i - 1
	]
	prin join " -> " difference now/precise st
	print ["  " (length? result) / 3 "matches found"]
	result 
]


find-fast: func [
	series
	value
	record-length
	i "iterations"
	/local result st s
][
	prin "find-fast(): "
	st: now/precise
	while [i > 0][
		prin i
		prin "."
		result: clear [] ;length? series
		s: head series
		until [
			not all[
				s: find/tail s value
				either 0 =  ( (-2 + index? s ) // record-length) [
					insert tail result copy/part series record-length
					not tail? s: skip s record-length
				][
					not tail? s: next s
				]
			]
		]
		i: i - 1
	]
	
	prin join " -> " difference now/precise st
	print ["  " (length? result) / record-length "matches found"]
	
	head result
]


find-really-fast: func [
	series
	keys
	value
	record-length
	i "iterations"
	/local result st s
][
	prin "find-really-fast(): "
	st: now/precise
	while [i > 0][
		prin i
		prin "."
		result: clear [] ;length? series
		s: head keys
		until [
			not all [
				s: find/tail s value

    insert tail result copy/part at series (record-length * ( -1 + index? 
    s )) record-length
			]
		]
		i: i - 1
	]
	
	prin join " -> " difference now/precise st
	print ["  " (length? result) / record-length "matches found"]
	
	head result
]

test-size: 2000000 ; number of records
iterations: 5
rsize: 3


print "preparing sparse data"
dataset: make block! test-size 
loop test-size [
	; we exclude 9 from dataset
	loop rsize [
		dataset: insert dataset random 8
	]
]
dataset: head dataset
insert at dataset (100 * rsize + 1) 9
insert at dataset (2000 * rsize + 1) 9
insert at dataset (50000 * rsize + 1) 9
insert at dataset (10000 * rsize + 1) 9
insert at dataset (20000 * rsize + 1) 9

probe length? dataset
print "sparse tests:"
feach dataset 9 iterations
print "----------------"
find-fast dataset 9 rsize iterations
print "----------------"
keys: extract dataset rsize
find-really-fast dataset keys 9 rsize iterations
print "----------------"


print "^/dense match with key/value collisions"
dataset: make block! test-size 
loop test-size [
	; we include 9 in the dataset
	loop rsize [
		dataset: insert dataset random 9
	]
]
dataset: head dataset
probe length? dataset
feach dataset 9 iterations
print "----------------"
find-fast dataset 9 rsize iterations
print "----------------"
keys: extract dataset rsize
find-really-fast dataset keys 9 rsize iterations
print "----------------"


print "^/dense match without key/value collisions"
dataset: make block! test-size 
loop test-size [
	; we include 9 in the dataset
	dataset: insert dataset random 9
	loop (rsize - 1) [
		dataset: insert dataset 10 + random 100000 
	]
]
dataset: head dataset
probe length? dataset
feach dataset 9 iterations
print "----------------"
find-fast dataset 9 rsize iterations
print "----------------"
keys: extract dataset rsize
find-really-fast dataset keys 9 rsize iterations
print "----------------"

ask "done"

the results speak for themselves.

preparing sparse data
6000005
sparse tests:
feach(): 5.4.3.2.1. -> 0:00:04.968   4 matches found
----------------
find-fast(): 5.4.3.2.1. -> 0:00:00.735   4 matches found
----------------
find-really-fast(): 5.4.3.2.1. -> 0:00:00.234   4 matches found
----------------

dense match with key/value collisions
6000000
feach(): 5.4.3.2.1. -> 0:00:13.328   221606 matches found
----------------
find-fast(): 5.4.3.2.1. -> 0:00:14.375   181756 matches found
----------------

find-really-fast(): 5.4.3.2.1. -> 0:00:06.453   221606 matches found
----------------

dense match without key/value collisions
6000000
feach(): 5.4.3.2.1. -> 0:00:13.734   222405 matches found
----------------
find-fast(): 5.4.3.2.1. -> 0:00:08.469   200322 matches found
----------------

find-really-fast(): 5.4.3.2.1. -> 0:00:07.516   222405 matches found
----------------
done

if you are willing to keep a version of the keys accessible at run-time, 
then the keyed search algorithm is MUCH faster, but incurs memory 
overhead, and the creation of the keys, on big blocks, is not negligible 
(time not included in tests, but will generally put the keyed algorythm 
slower than the fast-find)

also note that I am REUSING the result block at each call, so run 
times on first calls are actullaly slower than on successive calls.

noticed a little discrepancy in results... strange... probably cause 
by a last minute change...

ah found it....

the series was being skipped one too many (since I'm using /tail 
on the find, as suggested by andreas)

find-fast: func [
	series
	value
	record-length
	i "iterations"
	/local result st s
][
	prin "find-fast(): "
	st: now/precise
	while [i > 0][
		prin i
		prin "."
		result: clear [] ;length? series
		s: head series
		until [
			not all[
				s: find/tail s value
				either 0 =  ( (-2 + index? s ) // record-length) [
					insert tail result copy/part series record-length
					not tail? s: skip s record-length
				][
					not tail? s: next s
				]
			]
		]
		i: i - 1
	]
	
	prin join " -> " difference now/precise st
	print ["  " (length? result) / record-length "matches found"]
	
	head result
]

results with this function fixed are similar...

preparing sparse data
6000005
sparse tests:
feach(): 5.4.3.2.1. -> 0:00:04.89   4 matches found
----------------
find-fast(): 5.4.3.2.1. -> 0:00:00.719   4 matches found
----------------
find-really-fast(): 5.4.3.2.1. -> 0:00:00.25   4 matches found
----------------

dense match with key/value collisions
6000000
feach(): 5.4.3.2.1. -> 0:00:13.328   221606 matches found
----------------
find-fast(): 5.4.3.2.1. -> 0:00:14.843   221606 matches found
----------------

find-really-fast(): 5.4.3.2.1. -> 0:00:06.5   221606 matches found
----------------

dense match without key/value collisions
6000000
feach(): 5.4.3.2.1. -> 0:00:13.843   222405 matches found
----------------
find-fast(): 5.4.3.2.1. -> 0:00:09.672   222405 matches found
----------------

find-really-fast(): 5.4.3.2.1. -> 0:00:06.375   222405 matches found
----------------
done

Thanks Maxim

just trying to grok it :)  so 2,000,000 keys against 6, 000,000 values?

setup as records of 3 items,  1 key, 2 values.

3 items  (1 key, 2 values).

2 values?

not following ( as usual)

a typical hash (for example is [key1 value1 key2 value2]  .. one 
key, one value