Diego,
Not to start a debate, I guess it is in how you look at it.
From Intel's descriptions:
How does Hyper-Threading work? When Intel® Hyper-Threading Technology is
active, the CPU exposes two execution contexts per physical core. This
means that one physical core now works like two “logical cores” that can
handle different software threads. The ten-core Intel® Core™ i9-10900K
<https://www.intel.com/content/www/us/en/gaming/i9-desktop-processors-for-gaming.html>processor,
for example, has 20 threads when Hyper-Threading is enabled.
Two logical cores can work through tasks more efficiently than a
traditional single-threaded core. /By taking advantage of idle time when
the core would formerly be waiting for other tasks to complete/, Intel®
Hyper-Threading Technology improves CPU throughput (by up to 30% in
server applications^3 ).
So if we are creating code that is hypothetically 100% efficient (it can
use the CPU 100% of the time), there would be no 'idle' time for another
process. If work is done on that other process, it would be at the
expense of the 100% efficiency enjoyed by our 'perfect' process.
Of course, the true performance answer lies in how any of the processes
work, which is why some of us do so many experimental runs of jobs and
gather timings. We have yet to see a 100% efficient process, but folks
are improving things all the time.
Brian Andrus
On 2/13/2023 9:56 PM, Diego Zuccato wrote:
I think that's incorrect:
> The concept of hyper-threading is not doubling cores. It is a single
> core that can 'instantly' switch work from one process to another.
> Only one is being worked on at any given time.
A core can have multiple (usually 2) independent execution pipelines,
so that multiple instructions from different threads run concurrently.
It does not switch from one to the other.
But it does have some shared resources, like the MMU and sometimes the
FPU (maybe only on older AMD processors). Having a single MMU means
that all the instructions running on a core must have the same "view"
of the memory space, and that means that they must come from a single
process. IOW that they're multiple threads of a single process.
If the sw you're going to run makes good use of multithreading, having
hyperthreading can pe a great boost. If the sw only uses multitasking,
then hyperthreading is a net loss (not only you can't use half the
available threads, you also usually get slower clock speeds).
Diego
Il 13/02/2023 15:29, Brian Andrus ha scritto:
Hermann makes a good point.
The concept of hyper-threading is not doubling cores. It is a single
core that can 'instantly' switch work from one process to another.
Only one is being worked on at any given time.
So if I request a single core on a hyper-threaded system, I would not
be pleased to find you are giving it to someone else 1/2 the time. I
would need to have the actual core assigned. If I request multiple
cores and my app is only going to affect itself, then I _may_ benefit
from hyper-threading.
In general, enabling hyper-threading is not the best practice for
efficient HPC jobs. The goal is that every process is utilizing the
CPU as close to 100% as possible, which would render hyper-threading
moot.
Brian Andrus
On 2/13/2023 12:15 AM, Hermann Schwärzler wrote:
Hi Sebastian,
I am glad I could help (although not exactly as expected :-).
With your node-configuration you are "circumventing" how Slurm
behaves, when using "CR_Core": if you read the respective part in
https://slurm.schedmd.com/slurm.conf.html
it says:
"CR_Core
[...] On nodes with hyper-threads, each thread is counted as a CPU
to satisfy a job's resource requirement, but multiple jobs are not
allocated threads on the same core."
That's why you got a full core (both threads) when allocating a
singe CPU. Or e.g. four threads when allocating three CPUs asf.
"Lying" to Slurm about the actual hardware-setup helps to avoid this
behaviour but are you really confident with potentially running two
different jobs on the hyper-threads of the same core?
Regards,
Hermann
On 2/12/23 22:04, Sebastian Schmutzhard-Höfler wrote:
Hi Hermann,
Using your suggested settings did not work for us.
When trying to allocate a single thread with --cpus-per-task=1, it
still reserved a whole CPU (two threads). On the other hand, when
requesting an even number of threads, it does what it should.
However, I could make it work by using
SelectTypeParameters=CR_Core
NodeName=nodename Sockets=2 CoresPerSocket=128 ThreadsPerCore=1
instead of
SelectTypeParameters=CR_Core
NodeName=nodename Sockets=2 CoresPerSocket=64 ThreadsPerCore=2
So your suggestion brought me in the right direction. Thanks!
If anyone thinks this is complete nonsense, please let me know!
Best wishes,
Sebastian
On 11.02.23 11:13, Hermann Schwärzler wrote:
Hi Sebastian,
we did a similar thing just recently.
We changed our node settings from
NodeName=DEFAULT CPUs=64 Boards=1 SocketsPerBoard=2
CoresPerSocket=32 ThreadsPerCore=2
to
NodeName=DEFAULT Boards=1 SocketsPerBoard=2 CoresPerSocket=32
ThreadsPerCore=2
in order to make use of individual hyper-threads possible (we use
this in combination with
SelectTypeParameters=CR_Core_Memory).
This works as expected: after this, when e.g. asking for
--cpus-per-task=4 you will get 4 hyper-threads (2 cores) per task
(unless you also specify e.g. "--hint=nomultithread").
So you might try to remove the "CPUs=256" part of your
node-specification to let Slurm do that calculation of the number
of CPUs itself.
BTW: on a side-note: as most of our users do not bother to use
hyper-threads or even do not want to as their programs might
suffer from doing so, we made "--hint=nomultithread" the default
in our installation by adding
CliFilterPlugins=cli_filter/lua
to our slurm.conf and creating a cli_filter.lua file in the same
directory as slurm.conf, that contains this
function slurm_cli_setup_defaults(options, early_pass)
options['hint'] = 'nomultithread'
return slurm.SUCCESS
end
(see also
https://github.com/SchedMD/slurm/blob/master/etc/cli_filter.lua.example).
So if user really want to use hyper-threads they have to add
"--hint=multithread" to their job/allocation-options.
Regards,
Hermann
On 2/10/23 00:31, Sebastian Schmutzhard-Höfler wrote:
Dear all,
we have a node with 2 x 64 CPUs (with two threads each) and 8
GPUs, running slurm 22.05.5
In order to make use of individual threads, we changed|
|
|SelectTypeParameters=CR_Core||
NodeName=nodename CPUs=256 Sockets=2 CoresPerSocket=64
ThreadsPerCore=2 |
to
|SelectTypeParameters=CR_CPU NodeName=nodename CPUs=256|
We are now able to allocate individual threads to jobs, despite
the following error in slurmd.log:
error: Node configuration differs from hardware: CPUs=256:256(hw)
Boards=1:1(hw) SocketsPerBoard=256:2(hw) CoresPerSocket=1:64(hw)
ThreadsPerCore=1:2(hw)
However, it appears that since this change, we can only make use
of 4 out of the 8 GPUs.
The output of "sinfo -o %G" might be relevant.
In the first situation it was
$ sinfo -o %G
GRES
gpu:A100:8(S:0,1)
Now it is:
$ sinfo -o %G
GRES
gpu:A100:8(S:0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126)
||Has anyone faced this or a similar issue and can give me some
directions?
Best wishes
Sebastian
||
