Hello,
I just run the same code on my relatively new notebook
(Core 2 Duo 2GHz (T7250))
and the loop takes less than 2ns per
iteration (3x speedup). So it looks like the data sits entirely inside the
cache.
This core 2 has 2MB of cache and thats 4 times more than on
Athlon x2 I got.
> If what you say is true, and one core alone
fills the memory
> bandwidth, then there should be a net loss of
performance while
> running two copies of ami.
It depends
on complexity of the formula and the amount of data per symbol
you are
using. As each array element has 4 bytes, to fill 4 MB of cache
you
would need 1 million array elements or 100 arrays each having 10000
elements
or 10 arrays each having 100K elements. Generally speaking
people testing
on EOD data where 10 years is just 2600 bars should see
speed up.
People using very very long intraday data sets may see
degradation, but
rather unnoticeable.
Best regards,
Tomasz
Janeczko
amibroker.com
----- Original Message -----
From:
"dloyer123" <dloyer123@xxxxxxcom>
To:
<amibroker@xxxxxxxxxps.com>
Sent:
Tuesday, May 13, 2008 8:12 PM
Subject: [amibroker] Re: Dual-core vs.
quad-core
> Nice, tight loop. It is good to see someone that has
made the effort
> to make the most out of every cycle and the
result shows.
>
> My new E8400 (45nm 3GHz, dual core) system
should arrive tomorrow.
> The first thing I will do will be to
benchmark it running ami. I run
> portfolio backtests over a few
years of 5 minute data over a thousand
> or so symbols. Plenty of
data to overflow the cache, but still fit
> in memory. No trig.
>
> I'll post what I find.
>
> If what you say
is true, and one core alone fills the memory
> bandwidth, then
there should be a net loss of performance while
> running two
copies of ami.
>
>
>
> --- In amibroker@xxxxxxxxxps.com,
"Tomasz Janeczko" <groups@xxx>
>
wrote:
>>
>> Hello,
>>
>> FYI: SINGLE
processor core running an AFL formula is able to
> saturate memory
bandwidth
>> in majority of most common
operations/functions
>> if total array sizes used in given
formula exceedes DATA cache size.
>>
>> You need to
understand that AFL runs with native assembly speed
>> when using
array operations.
>> A simple array multiplication like
this
>>
>> X = Close * H; // array
multiplication
>>
>> gets compiled to just 8 assembly
instructions:
>>
>> loop: 8B 54 24 58 mov edx,dword ptr
[esp+58h]
>> 00465068 46 inc
> esi ; increase counters
>> 00465069 83 C0 04 add eax,4
>> 0046506C 3B F7 cmp
esi,edi
>> 0046506E D9 44 B2 FC fld dword ptr [edx+esi*4-
>
4] ; get element of close array
>> 00465072 D8 4C 08 FC fmul
dword ptr [eax+ecx-
> 4] ; multiply by element of high
array
>> 00465076 D9 58 FC fstp dword ptr [eax-
> 4] ;
store result
>> 00465079 7C E9 jl
> loop ; continue until
all elements are processed
>>
>> As you can see there
are three 4 byte memory accesses per loop
> iteration (2 reads each
4 bytes long and 1 write 4 byte long)
>>
>> On my (2
year old) 2GHz Athlon x2 64 single iteration of this loop
> takes 6
nanoseconds (see benchmark code below).
>> So, during 6
nanoseconds we have 8 byte reads and 4 byte store.
> Thats
(8/(6e-9)) bytes per second = 1333 MB per second read
>> and 667
MB per second write simultaneously i.e. 2GB/sec combined !
>>
>> Now if you look at memory benchmarks:
>> http://community.compuserve.com/n/docs/docDownload.aspx?webtag=ws-
>
pchardware&guid=6827f836-8c33-4063-aaf5-c93605dd1dc6
>>
you will see that 2GB/s is THE LIMIT of system memory speed on
>
Athlon x64 (DDR2 dual channel)
>> And that's considering the fact
that Athlon has superior-to-intel
> on-die integrated memory
controller (hypertransfer)
>>
>> // benchmark code -
for accurrate results run it on LARGE arrays -
> intraday database,
1-minute interval, 50K bars or more)
>>
GetPerformanceCounter(1);
>> for(k = 0; k < 1000; k++ )
X = C * H;
>> "Time per single iteration
[s]="+1e-3*GetPerformanceCounter()/
>
(1000*BarCount);
>>
>> Only really complex
operations that use *lots* of FPU (floating
> point)
cycles
>> such as trigonometric (sin/cos/tan) functions are slow
enough for
> the memory
>> to keep up.
>>
>> Of course one may say that I am using "old" processor, and
new
> computers have faster RAM and that's true
>> but
processor speeds increase FASTER than bus speeds and the gap
>
between processor and RAM
>> becomes larger and larger so with
newer CPUs the situation will be
> worse, not better.
>>
>>
>> Best regards,
>> Tomasz
Janeczko
>> amibroker.com
>> ----- Original Message
-----
>> From: "dloyer123"
<dloyer123@x..>
>> To: <amibroker@xxxxxxxxxps.com>
>>
Sent: Tuesday, May 13, 2008 5:02 PM
>> Subject: [amibroker] Re:
Dual-core vs. quad-core
>>
>>
>> > All of
the cores have to share the same front bus and
> northbridge.
>> > The northbridge connects the cpu to memory and has
limited
> bandwidth.
>> >
>> > If several
cores are running memory hungry applications, the
> front
>> > buss will saturate.
>> >
>> >
The L2 cache helps for most applications, but not if you are
>
burning
>> > through a few G of quote data. The L2 cache is
just 4-8MB.
>> >
>> > The newer multi core
systems have much faster front buses and
> that
>> >
trend is likely to continue.
>> >
>> > So, it
would be nice if AMI could support running multi cores,
> even
>> > if it was just running different optimization passes on
different
>> > cores. That would saturate the front bus, but
take advantage of
> all
>> > of the memory bandwidth
you have. It would really help those
> multi
>> > day
walkforward runs.
>> >
>> >
>> >
>> > --- In amibroker@xxxxxxxxxps.com,
"markhoff" <markhoff@> wrote:
>> >>
>>
>>
>> >> If you have a runtime penalty when running
2 independent AB jobs
> on
>> > a
>> >>
Core Duo CPU it might be caused by too less memory (swapping to
>> > disk)
>> >> or other tasks which are also
running (e.g. a web browser, audio
>> >> streamer or
whatever). You can check this with a process explorer
>> >>
which shows each tasks CPU utilisation. Similar, 4 AB jobs on a
>
Core
>> >> Quad should have nearly no penalty in
runtime.
>> >>
>> >> Tomasz stated that
multi-thread optimization does not scale good
>> >
with
>> >> the CPU number, but it is not clear to me why
this is the case.
> In
>> > my
>> >>
understanding, AA optimization is a sequential process of
> running
>> > the
>> >> same AFL script with different
parameters. If I have an AFL with
>> >> significantly long
runtime per optimization step (e.g. 1 minute)
> the
>>
>> overhead for the multi-threading should become quite small
and
>> >> independent tasks should scale nearly with the
number of CPUs
> (as
>> > long
>> >> as
there is sufficient memory, n threads might need n-times more
>>
>> memory than a single thread). For sure the situation is
>
different if
>> >> my single optimization run takes only a
few millisecs or
> seconds,
>> > then
>>
>> the overhead for multi-thread-managment goes up
...
>> >>
>> >> Maybe Tomasz can give some
detailed comments on that issue?
>> >>
>>
>> Best regards,
>> >> Markus
>> >>
>> >
>> >
>> >
------------------------------------
>> >
>> > Please note that this group is for discussion between
users only.
>> >
>> > To get support from
AmiBroker please send an e-mail directly to
>> > SUPPORT {at}
amibroker.com
>> >
>> > For NEW RELEASE
ANNOUNCEMENTS and other news always check DEVLOG:
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>>
>
>> > For other support material please check
also:
>> > http://www.amibroker.com/support.html
>>
> Yahoo! Groups Links
>> >
>> >
>>
>
>>
>
>
>
>
------------------------------------
>
>
Please note that this group is for discussion between users only.
>
> To get support from AmiBroker please send an e-mail directly to
> SUPPORT {at} amibroker.com
>
> For NEW RELEASE
ANNOUNCEMENTS and other news always check DEVLOG:
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>
> For other support material please check also:
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>
Yahoo! Groups Links
>
>
>