Hi Matt S,
Thanks again for your quick reply with useful information.
I will rerun with -reg-alloc-policy=dynamic
in my mini regression to see If it makes a difference
As for LRN, I won't make modifications to lrn_config.dnnmark
unless it's required to run additional DNN tests.
The 4 tests : test_fwd_softmax, test_bwd_softmax, test_fwd_pool, and
test_bwd_bn are good enough for now.
For Matt S and Matt P,
Are these parameters for "mem_req_latency" and "mem_resp_latency" valid for
both APU (Carrizo) and GPU (VEGA) ?
gem5/src/gpu-compute/GPU.py
mem_req_latency = Param.Int(40, "Latency for request from the cu to ruby. "\
"Represents the pipeline to reach the TCP "\
"and specified in GPU clock cycles")
mem_resp_latency = Param.Int(40, "Latency for responses from ruby to the "\
"cu. Represents the pipeline between the "\
"TCP and cu as well as TCP data array "\
"access. Specified in GPU clock cycles")
It seems like to me the GPU (VEGA) with dedicated memory (GDDR5) should be
using a different parameter for its memory access latencies.
My company's IP could be used to reduce interconnect latencies for the APU and
GPU and would to quantify this at system level with benchmarks.
We would like to determine if GPU can get performance boost with reduced memory
access latencies.
Please confirm which memory latencies parameters to modify and use for GPU
(VEGA).
Thanks,
David
From: Matt Sinclair <sincl...@cs.wisc.edu>
Sent: Tuesday, March 15, 2022 1:08 PM
To: David Fong <da...@chronostech.com>; gem5 users mailing list
<gem5-users@gem5.org>
Cc: Kyle Roarty <kroa...@wisc.edu>; Poremba, Matthew <matthew.pore...@amd.com>
Subject: RE: gem5 : X86 + GCN3 (gfx801) + test_fwd_lrn
Hi David,
The dynamic register allocation policy allows the GPU to schedule as many
wavefronts as there is register space on a CU. By default, the original
register allocator released with this GPU model ("simple") only allowed 1
wavefront per CU at a time because the publicly available dependence modeling
was fairly primitive. However, this was not very realistic relative to how a
real GPU performs, so my group has added better dependence tracking support
(more could probably still be done, but it reduced stalls by up to 42% relative
to simple) and a register allocation scheme that allows multiple wavefronts to
run concurrently per CU ("dynamic").
By default, the GPU model assumes that the simple policy is used unless
otherwise specified. I have a patch in progress to change that though:
https://gem5-review.googlesource.com/c/public/gem5/+/57537<https://urldefense.proofpoint.com/v2/url?u=https-3A__gem5-2Dreview.googlesource.com_c_public_gem5_-2B_57537&d=DwMFAg&c=euGZstcaTDllvimEN8b7jXrwqOf-v5A_CdpgnVfiiMM&r=OkH-8nM02VdNPRt_miVO36vI9580zW1SgNQ4MzWRfqc&m=-FsihDasL1fNssjztS5omIcHAqEWFVJsA1229NUHVPg&s=ruzg3Ubt4KOu5fVZrj9H2BR_v2Pz5LclHxnZGz-vSGU&e=>.
Regardless, if applications are failing with the simple register allocation
scheme, I wouldn't expect a more complex scheme to fix the issue. But I do
strongly recommend you use the dynamic policy for all experiments - otherwise
you are using a very simple, less realistic GPU model.
Setting all of that aside, I looked up the perror message you sent last night
and it appears that happens when your physical machine has run out of memory
(which means we can't do much to fix gem5, since the machine itself wouldn't
allocate as much memory as you requested). So, if you want to run LRN and
can't run on a machine with more memory, one thing you could do is change the
LRN config file to use smaller NCHW values (e.g., reduce the batch size, N,
from 100 to something smaller that fits on your machine):
https://gem5.googlesource.com/public/gem5-resources/+/refs/heads/develop/src/gpu/DNNMark/config_example/lrn_config.dnnmark#6<https://urldefense.proofpoint.com/v2/url?u=https-3A__gem5.googlesource.com_public_gem5-2Dresources_-2B_refs_heads_develop_src_gpu_DNNMark_config-5Fexample_lrn-5Fconfig.dnnmark-236&d=DwMFAg&c=euGZstcaTDllvimEN8b7jXrwqOf-v5A_CdpgnVfiiMM&r=OkH-8nM02VdNPRt_miVO36vI9580zW1SgNQ4MzWRfqc&m=-FsihDasL1fNssjztS5omIcHAqEWFVJsA1229NUHVPg&s=UUNQuxWw2evznkakAuo9_teQSdLxGjhk111Z0ZOHIac&e=>.
If you do this though, you will likely need to re-run the generate_cachefile
to generate the MIOpen binaries for this different sized LRN.
Hope this helps,
Matt
From: David Fong <da...@chronostech.com<mailto:da...@chronostech.com>>
Sent: Tuesday, March 15, 2022 2:58 PM
To: Matt Sinclair <sincl...@cs.wisc.edu<mailto:sincl...@cs.wisc.edu>>; gem5
users mailing list <gem5-users@gem5.org<mailto:gem5-users@gem5.org>>
Cc: Kyle Roarty <kroa...@wisc.edu<mailto:kroa...@wisc.edu>>; Poremba, Matthew
<matthew.pore...@amd.com<mailto:matthew.pore...@amd.com>>
Subject: RE: gem5 : X86 + GCN3 (gfx801) + test_fwd_lrn
Hi Matt S.,
Thanks for the detailed reply.
I looked at the link you sent me for the weekly run.
I see an additional parameter which I didn't use:
--reg-alloc-policy=dynamic
What does this do ?
I was able to run the two other tests you use in your weekly runs :
test_fwd_pool, test_bwd_bn
for CUs=4.
David
From: Matt Sinclair <sincl...@cs.wisc.edu<mailto:sincl...@cs.wisc.edu>>
Sent: Monday, March 14, 2022 7:41 PM
To: gem5 users mailing list <gem5-users@gem5.org<mailto:gem5-users@gem5.org>>
Cc: David Fong <da...@chronostech.com<mailto:da...@chronostech.com>>; Kyle
Roarty <kroa...@wisc.edu<mailto:kroa...@wisc.edu>>; Poremba, Matthew
<matthew.pore...@amd.com<mailto:matthew.pore...@amd.com>>
Subject: RE: gem5 : X86 + GCN3 (gfx801) + test_fwd_lrn
Hi David,
I have not seen this mmap error before, and my initial guess was the mmap error
is happening because you are trying to allocate more memory than we created
when mmap'ing the inputs for the applications (we do this to speed up SE mode,
because otherwise initializing arrays can take several hours). However, the
fact that it is failing in physical.cc and not in the application itself is
throwing me off there. Looking at where the failure is occurring, it seems the
backing store code itself is failing here (from such a large allocation).
Since the failure is with a C++ mmap call itself, that is perhaps more
problematic - is "Cannot allocate memory" the failure from the perror() call on
the line above the fatal() print?
Regarding the other question, and the failures more generally: we have never
tested with > 64 CUs before, so certainly you are stressing the system and
encountering different kinds of failures than we have seen previously.
In terms of applications, I had thought most/all of them passed previously, but
we do not test each and every one all the time because this would make our
weekly regressions run for a very long time. You can see here:
https://gem5.googlesource.com/public/gem5/+/refs/heads/develop/tests/weekly.sh#176<https://urldefense.proofpoint.com/v2/url?u=https-3A__gem5.googlesource.com_public_gem5_-2B_refs_heads_develop_tests_weekly.sh-23176&d=DwMFAg&c=euGZstcaTDllvimEN8b7jXrwqOf-v5A_CdpgnVfiiMM&r=OkH-8nM02VdNPRt_miVO36vI9580zW1SgNQ4MzWRfqc&m=PHGn1HCe8I3xN31ZIG4ubHju1ngyERkZLvihkRk2ZXk&s=19clmMzYHLZtPwMARK0v5V0YZvD3ESFCoS4dnaX_tZo&e=>
which ones we run on a weekly basis. I expect all of those to pass (although
your comment seems to indicate that is not always true?). Your issues are
exposing that perhaps we need to test more of them beyond these 3 - perhaps on
a quarterly basis or something though to avoid inflating the weekly runtime.
Having said that, I have not run LRN in a long time, as some ML people told me
that LRN was not widely used anymore. But when I did run it, I do remember it
requiring a large amount of memory - which squares with what you are seeing
here. I thought LRN needed -mem-size=32 GB to run, but based on your message
it seems that is not the case.
@Matt P: have you tried LRN lately? If so, have you run into the same
OOM/backing store failures?
I know Kyle R. is looking into your other failure, so this one may have to wait
behind it from our end, unless Matt P knows of a fix.
Thanks,
Matt
From: David Fong via gem5-users
<gem5-users@gem5.org<mailto:gem5-users@gem5.org>>
Sent: Monday, March 14, 2022 4:38 PM
To: David Fong via gem5-users <gem5-users@gem5.org<mailto:gem5-users@gem5.org>>
Cc: David Fong <da...@chronostech.com<mailto:da...@chronostech.com>>
Subject: [gem5-users] gem5 : X86 + GCN3 (gfx801) + test_fwd_lrn
Hi,
I'm getting an error related to memory for test_fwd_lrn
I increased the memory size from 4GB to 512GB I got memory size issue : "out of
memory".
build/GCN3_X86/gpu-compute/gpu_compute_driver.cc:599: warn: unimplemented
ioctl: AMDKFD_IOC_SET_SCRATCH_BACKING_VA
build/GCN3_X86/gpu-compute/gpu_compute_driver.cc:609: warn: unimplemented
ioctl: AMDKFD_IOC_SET_TRAP_HANDLER
build/GCN3_X86/sim/mem_pool.cc:120: fatal: fatal condition freePages() <= 0
occurred: Out of memory, please increase size of physical memory.
But once I increased mem size to 1024GB, 1536GB,2048GB I'm getting this DRAM
device capacity issue.
docker run --rm -v ${PWD}:${PWD} -v
${PWD}/gem5/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0
-w ${PWD} gcr.io/gem5-test/gcn-gpu:v21-2 gem5/build/GCN3_X86/gem5.opt
gem5/configs/example/apu_se.py --mem-size 1536GB --num-compute-units 256 -n3
--benchmark-root=gem5/gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_lrn
-cdnnmark_test_fwd_lrn --options="-config
gem5/gem5-resources/src/gpu/DNNMark/config_example/lrn_config.dnnmark -mmap
gem5/gem5-resources/src/gpu/DNNMark/mmap.bin" |& tee
gem5_gpu_cu256_run_dnnmark_test_fwd_lrn_50latency.log
Global frequency set at 1000000000000 ticks per second
build/GCN3_X86/mem/mem_interface.cc:791: warn: DRAM device capacity (8192
Mbytes) does not match the address range assigned (2097152 Mbytes)
mmap: Cannot allocate memory
build/GCN3_X86/mem/physical.cc:231: fatal: Could not mmap 1649267441664 bytes
for range [0:0x18000000000]!
Smaller number of CUs like 4 also have same type of error.
Is there a regression script or regression log for DNNMark to show mem-size or
configurations that are known working for DNNMark tests so
I can use same setup to run a few DNNMark tests?
Only test_fwd_softmax, test_bwd_softmax are working for CUs from
{4,8,16,32,64,128,256}
Thanks,
David
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