Hi,
You did run "make compiler" at least once after running "configure" and
"make", didn't you?
David
On 19/06/2020 18:17, Chun Tian (binghe) wrote:
Hi David,
I rebuilt and installed PolyML 5.8 ('master', up to 0ad5aa8) and ran again
the 'miller' selftests as Michael mentioned, but the results seem unchanged:
```
Composite test on 91 ...
(0.018s) OK
Composite test on 123 ... [GC = 0.037]
(0.013s) OK
Composite test on 4294967297 ... [GC = 3.487]
(0.376s) OK
Composite test on 18446744073709551617 ... [GC = 84.370]
(2.846s) OK
Composite test on 340282366920938463463374607431768211457 ... [GC =
429.267] (21.935s) OK
real 9m19.723s
user 9m7.408s
sys 0m12.139s
```
I'm on macOS 10.15 (catalina) with 2,3 GHz Intel Core i9 (8 core). The same
tests took less than 30s with PolyML 5.7.
Hope this helps,
Chun Tian
On Fri, Jun 19, 2020 at 6:54 PM David Matthews <
[email protected]> wrote:
Thanks, Phil, for reminding me of this. I've committed a fix (0ad5aa8)
for that. There was a tail-recursive function in your example that
returned a tuple. Poly/ML puts these on the stack where it can but in
this case it was causing the function to cease to be tail-recursive,
eating up the stack. The reason for the regression was a change in the
way tuples on the stack are handled in the 32-in-64 bit version. Your
example now works as it used to.
Michael, it would be worth trying your code with the latest version to
see if this has fixed it. If it hasn't then presumably it's a different
problem and I'll have to investigate further.
David
On 16/06/2020 09:58, Phil Clayton wrote:
I also found that 5.8 can consume a huge amount of memory compared to
5.7.1. In my case, it was a very small example doing simple arithmetic
operations (demonstrating Fermat's method to find pairs of factors) so
it may be related. I have raised an issue for it:
https://github.com/polyml/polyml/issues/121
Phil
On 16/06/20 02:30, Norrish, Michael (Data61, Acton) wrote:
I have some HOL code that runs to completion in ~24 seconds user time.
On 5.7.1 it runs in about that much wall clock time too.
(Unfortunately, this test is running on a different machine,
necessarily, but this is an old Linux desktop which is far less
powerful than the new Macbook Pro which is running 5.8.)
On 5.8 (updated for MacOS Catalina to the SHA above), wall clock time
is 8 minutes.
The relevant code is the selftest in examples/miller/miller which is a
big arithmetic normalisation.
On the Mac, memory consumption (as reported by the Activity Monitor)
gets as high as 20GB; on the Linux machine running 5.7.1, the
consumption remains very slight (top reports 3% in its %MEM column;
the machine has 16GB)
Michael
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