Re: comparing r.cost and r.terracost [was: [GRASS-dev] Re: grass-dev Digest, Vol 43, Issue 8]

[Markus Metz]

Hi Laura,

Laura Toma wrote:
Hi Markus,
Your conclusions are based on the hypothesis that you can model the 
performance of r.cost in the presence of low memory by tweaking the 
memory limit in the code 
I think I improved disk IO, AFAICT memory limits in r.cost are handled 
as before through the percent_memory option. BTW, I added an -i option 
to r.cost, reporting estimated memory and disk space usage for the given 
percent_memory option, inspired by r.terracost.
and using a machine with a large physical memory. I don't think that 
this hypothesis is true, and here is the evidence so far:

r.cost on a machine with 8GB physical memory: 1h
r.cost on a machine with 2.5GB physical memory: 4h
r.terracost memory=2000 on a machine with 8GB physical memory: 24h

r.terracost numtiles=1 would need here 2380MB + xMB for Dijkstra Search, 
but there are only 2000MB free.
r.cost on a machine with 2.5GB physical memory: >24h

If you reboot the machine with 1GB RAM, you will see the running time 
go up (by a lot). 
Please test if the running time of r.cost reaches the running time of 
t.terracost.
I have run similar tests in the past, and r.cost did not finish in 40 
hours. It may be better now
It may be so. You're welcome to test yourself. Unfortunately, I can't 
afford (time contraints...) to do further testing myself, but will 
continue as soon as I have some spare time. r.cost seems particularly 
challenging
with regard to external memory, and I like challenges ;-)

Markus M

, and you are the best person to re-try these tests as you know how to 
tweak it.

I'll get back about what terracost is doing and why it has such large 
files after we see these new numbers.

-Laura




On Nov 17, 2009, at 3:51 PM, Markus Metz wrote:

Hi Laura,

Laura Toma wrote:
Hi Markus,

Processing a grid of 312 M cells takes about 8 x 312M = 2GB of RAM,
That is only true for r.terracost with numtiles=1, because r.terracost
stores costs as float. Is it possible that there is a bug in r.terracost
when using numtiles > 1, because creating 64GB of temporary files seems
a bit inordinate for 2GB of data? And if r.terracost would use double
for costs, it would be about 130GB of temporary files? OK, disk space is
nearly for free nowadays.
r.cost stores costs as double, so the size of temporary files is about
4GB. Additionally, 2GB where used for processing, i.e. at least 6GB of
system RAM are required to also keep cached files in RAM.
so on a machine with 8GB of RAM it will not use virtual memory at all,
irrespective of how you tweak it.
Right, but it still uses the disk IO algorithm and reads from/writes to
disk.

With 8GB of RAM, the correct comparison is between r.cost and
r.terracost with numtiles=1
I don't think so because r.cost still uses its disk IO algorithm while
r.terracost doesn't. That's like comparing r.watershed in ram mode with
r.terraflow. A module not using a disk IO algorithm should always be
faster than a corresponding module using a disk IO algorithm, as long as
intermediate data fit into RAM.

In other words, if you tweak r.cost, you also need to tweak
r.terracost, which means you run with numtiles=1 for as long as data
fits in real memory.
I tweaked the disk IO algorithm to be faster, not to use less disk
space. I can also do serious tweaking and write a true all-in-memory
version of r.cost and compare that to r.terracost numtiles=1, but I'm
interested in the performance of r.cost with the disk IO algorithm and
thus compare it to r.terracost with its disk IO algorithm (requires
numtiles > 1).

If you want any real numbers on how r.cost behaves with low memory you
need to reboot the machine with 1GB or better 512MB of RAM. There is
no way around it. Just try it, it is easy to do. I run experiments
like this all the time.
OK, would you mind running experiments with r.cost in grass7 and
r.terracost numtiles>1 so you can see for yourself?

I rebooted with 2500MB of RAM in order to run the same test command as
before on the 312 million cells region, giving about 2000MB of RAM to
r.cost, same like before. I used the same region and start points as
before because I think these settings are challenging for r.cost. My
test system went into swap space, all memory was used up (system file
cache was in swap anyway, OS needs some RAM too), and r.cost took, as
expected, longer, namely 4 hours 10 min.

Still much less than the 24 hours 22 min of r.terracost with memory=2000
and 8GB of system RAM...

The latest version of r.cost (r39749) needs 2 hours 30 min with 2500MB
of RAM and 2000MB of RAM assigned to it, remainder used by OS.

>From a user's perspective, one reason or side-effect concerning modules
with disk IO algorithms is IMO that you do not need to use up all
available system memory and can still do other things in parallel, so I
would always assign max 75% percent of RAM to these modules and can
still do other work, potentially preventing the system from caching 
files.

BTW, there was a typo in my g.region command, must be res=30 in order to
get 312 million cells, sorry!

Markus M


-Laura


On Nov 14, 2009, at 6:51 AM, Markus Metz wrote:

Hi Laura,

Laura Toma wrote:

my experience is that , if you want to see how an application would
behave with 500 MB of RAM, you have to physically reboot the machine
with 500 MB of RAM (it's very easy to do this on a Mac, and 
relatively
easy on Linux. on windows, i don't know).

if the machine has more than 500MB RAM, even if you restrict the
application to use less, the system gives it all it can. in your
setup, it is almost as if r.cost would run fully in memory, because
even it it places the segments on disk, the system file cache fits 
all
segments in memory. the same is true for terracost, its streams 
fit in
memory. but using tiles has a big CPU overhead, which is why it is
slower.
I haven't rebooted my Linux box with less RAM, but I set up a test
region with about 312 million cells (details below), I think we can
agree that this is for current standards a pretty large region, maybe
not in the future. Your argument still holds true that r.cost may have
some advantage because its temp files are much smaller than the temp
files of r.terracost and therefore a larger proportion can be 
cached by
the system (beyond the control of the module). I could however see 
a lot
of disk IO on both modules.

For 312 million cells, r.cost needed 51 min, r.terracost needed 24 
h 22
min, both got 2GB memory.

Now that sounds like really bad news for r.terracost. But this is not
the whole truth. First, I had to tweak r.cost a little bit in order to
be so fast, still have to come up with a solution to do that 
tweaking in
the module. Second, r.cost may suffer more from memory reduction, not
physical RAM reduction, than r.terracost. Reducing the percent_memory
option already slows the module down considerably. But that is also 
true
for r.terracost, there the bottleneck seems to be INTERTILE DIJKSTRA
which took well over 12 hours with heavy disk IO and full memory
consumption. Third, r.cost performs better with less start points
keeping region settings constant. I'm not sure if this applies as well
to r.terracost.

In summary, I think that on even larger regions, say >1 billion cells,
and many small separate start points (>100 000), r.terracost should
outperform r.cost, but I would not bet on it ;-) For what I guess is
current everyday use (< 100 million cells), r.cost in grass7 might 
most
of the time outperform r.terracost with numtiles>1, sometimes
considerably as in my tests. Speed performance of r.cost is 
variable and
dependent on the combination of region size, number and 
distribution of
start points, and the amount of memory it is allowed to use. There may
still be some scope for improvement in r.cost, I just did a quick job
there, no in-depth code analysis (yet). The extraordinarily large temp
files of r.terracost (total 64GB, largest single file was about 
56GB, no
typo) could be a handicap when processing such large regions. Finally,
the results of the tests I did are valid for my test system only, they
will be different on other systems.


when i did some preliminary testing, i rebooted the machine with 
512MB
RAM, and ran r.cost on grids of 50M-100M cells. it was slow,
completely IO bound, and took several hours or more. or if you use 
1GB
of RAM, you may need to go to larger grids.
Please test r.cost in grass7 yourself, and maybe share your test
commands, then others can run the tests too and compare.

Here is my test region:

The 312 million cells test region was created in the North Carolina
sample dataset with
g.region rast=elev_state_5...@permanent res=40
Then I created a cost layer with
r.mapcalc "cost = 1"
You wanted many start points, so I generated 10000 start points with
v.random output=start_points_10000 n=10000
and converted this vector to raster with
v.to.rast start_points_10000 use=val val=1 out=start_points_10000 --o

The test command for r.cost was
time r.cost input=cost start_rast=start_points_10000
output=dist_random_10000 percent_memory=40 --o
This setting was equivalent to 2 GB of memory.
time:
real 51m18.172s
user 34m4.067s
sys 0m45.100s

For r.terracost, I used as temp dir again a directory on a separate 
hard
drive, faster than the one that r.cost used, so let's say
tmpdir="/path/to/some/fast/dir"
and the test command for r.terracost was
time r.terracost in=cost start_rast=start_points_10000
out=dist_random_10000_terracost STEAM_DIR=$tmpdir VTMPDIR=$tmpdir
memory=2000 numtiles=20788 --o
numtiles=20788 I got with r.terracost -i
time:
real 1453m37.022s
user 513m56.549s
sys 43m38.519s

Sorry for that long post!

Markus M





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