Thanks Giacomo,
A few questions:
1. Who is Jason? So that I can contact him 😊
2. Regarding ELFie, I found these:
*
https://github.com/UT-LCA/Scalability-Phase-Simpoint-of-SPEC-CPU2017/releases
* https://github.com/intel/pinball2elf
#2 would seem to be able to generate ELFies from the x86 pinballs that were
captured. However, I’m unfamiliar with how ELFies work. Do they still capture
state and thus need to inject processor state prior to simulation? In which
case the problem still exists that the ARM Cycle Model is incompatible….
Thanks,
Jonathan.
From: Giacomo Travaglini <giacomo.travagl...@arm.com>
Date: Wednesday, March 8, 2023 at 6:26 AM
To: The gem5 Users mailing list <gem5-users@gem5.org>
Cc: Jonathan Kang <mos...@meta.com>
Subject: Re: [gem5-users] Capturing SimPoint and running on ARM CycleModel
Hi Jonathan On 08/03/2023 01: 43, Jonathan Kang via gem5-users wrote: Hi all,
I’m attempting to get SPEC 2k17 to run on our performance model that uses an
ARM Cycle Model (A55). Obviously it’d take forever to run the whole binary so I
was wondering
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Hi Jonathan
On 08/03/2023 01:43, Jonathan Kang via gem5-users wrote:
Hi all,
I’m attempting to get SPEC 2k17 to run on our performance model that uses an
ARM Cycle Model (A55). Obviously it’d take forever to run the whole binary so I
was wondering if anyone has had experience capturing SimPoints using Gem5 and
then running the resulting reduced binary on a Cycle Model. I know you can do
it easily on Gem5 simple CPU in SE mode but I need it to work with our more
accurate model.
You can easily generate SimPoints with gem5 as you correctly said. The problem
is the checkpoint incompatibility between gem5 and Cycle Model. (restoring the
gem5 checkpoint from the CA simulator).
It would be nice if we could generate an ELFie [1] from a gem5 checkpoint. As I
have recently seen some commits mentioning ELFies you could ask Jason and his
group if there's something going on...
Kind Regards
Giacomo
[1]:
https://dl.acm.org/doi/abs/10.1109/CGO51591.2021.9370340<https://dl.acm.org/doi/abs/10.1109/CGO51591.2021.9370340>
Thanks in advance!
Jonathan Kang.
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