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Old 2022-08-17, 18:06   #111
EdH
 
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Quote:
Originally Posted by Plutie View Post
the easiest way would probably be running "msieve -nc2 -g 0" - if it outputs a line showing the VBITS value you compiled msieve with, then it's compiled properly.
Thanks, but to do that, I think -np1 would work better since I wouldn't need to create as many other files first. But I'd still need to look for a value (such as "using GPU" in the log. I was looking for a simple value check or existence check for a file, perhaps a .ptx.
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Old 2022-08-17, 18:30   #112
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ah, in that case - you can look for the lanczos_kernel.ptx file (or stage1_core.ptx)

Last fiddled with by Plutie on 2022-08-17 at 18:31 Reason: oops
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Old 2022-08-17, 18:35   #113
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Quote:
Originally Posted by Plutie View Post
ah, in that case - you can look for the lanczos_kernel.ptx file (or stage1_core.ptx)
Thanks! I'll work with that.
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Old 2022-08-18, 13:14   #114
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I'm too excited to keep this to myself. I finally have sufficient cooling for my M40 GPU and am running a c173 that is in LA on both, the 40-thread machine and the GPU machine.

This is the 40-thread (40GB) machine at start of LA:
Code:
Wed Aug 17 22:53:22 2022  linear algebra at 0.0%, ETA 66h33m
and, current state (08:13):
Code:
linear algebra completed 2537146 of 16995095 dimensions (14.9%, ETA 53h31m)
Here is the GPU machine at start of LA:
Code:
Wed Aug 17 23:11:13 2022  linear algebra at 0.0%, ETA 24h39m
and. current state (08:13):
Code:
linear algebra completed 6241861 of 16995095 dimensions (36.7%, ETA 15h35m)
Here's a litle extra from the GPU machine log:
Code:
Wed Aug 17 22:59:01 2022  using VBITS=256
Wed Aug 17 22:59:01 2022  skipping matrix build
Wed Aug 17 22:59:04 2022  matrix starts at (0, 0)
Wed Aug 17 22:59:07 2022  matrix is 16994916 x 16995095 (5214.6 MB) with weight 1611774956 (94.84/col)
Wed Aug 17 22:59:07 2022  sparse part has weight 1163046519 (68.43/col)
Wed Aug 17 22:59:07 2022  saving the first 240 matrix rows for later
Wed Aug 17 22:59:11 2022  matrix includes 256 packed rows
Wed Aug 17 22:59:16 2022  matrix is 16994676 x 16995095 (4829.9 MB) with weight 1060776224 (62.42/col)
Wed Aug 17 22:59:16 2022  sparse part has weight 994218947 (58.50/col)
Wed Aug 17 22:59:16 2022  using GPU 0 (Tesla M40 24GB)
Wed Aug 17 22:59:16 2022  selected card has CUDA arch 5.2
Wed Aug 17 23:10:30 2022  commencing Lanczos iteration
Wed Aug 17 23:10:31 2022  memory use: 11864.2 MB
The GPU is showing "12701MiB / 22945MiB" for its memory use, so I should be able to do some even larger numbers.
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Old 2022-08-19, 03:12   #115
LaurV
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Sorry I didn't follow this thread very close.

Are you saying that you do NFS completely on GPU? I mean, I knew poly can be done, and I am reading now about LA? How about sieving?
If so, where can I grab the exe and the "for dummy" tutorial?
Windows/Linux available? I may give it a try on local (where I run few quite powerful AMD and Nvidia cards) or on Colab (where I have occasional access to P100, V100 and - if lucky- A100).
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Old 2022-08-19, 03:56   #116
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Quote:
Originally Posted by LaurV View Post
Sorry I didn't follow this thread very close.

Are you saying that you do NFS completely on GPU? I mean, I knew poly can be done, and I am reading now about LA? How about sieving?
If so, where can I grab the exe and the "for dummy" tutorial?
Windows/Linux available? I may give it a try on local (where I run few quite powerful AMD and Nvidia cards) or on Colab (where I have occasional access to P100, V100 and - if lucky- A100).
currently, polyselect and LA can be done on GPU - sieving and filtering are still on CPU.

here's a quick guide for linux specifically, but I don't think the process will be too different on windows.

Quote:
find the compute capability of your GPU - can be found here.

compilation example here is for a GTX 1060 (CC 6.1)
Code:
git clone https://github.com/gchilders/msieve_nfsathome -b msieve-lacuda-nfsathome
cd msieve_nfsathome
make all CUDA=61 VBITS=256
once compiled, you can run both polyselect and LA just as you would with normal msieve, just add "-g (gpu_num)" to the command. you can lower the VBITS value to fit larger matrices onto GPU during LA, but at a performance penalty.
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Old 2022-08-29, 01:57   #117
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I forgot to add -g 0 to the command line and it seemed to default to device 0. I did specify use_managed=1 so maybe that was enough to invoke the GPU. Then again, I may be using an earlier release.
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Old 2022-10-05, 23:21   #118
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Here is a data point for the crossover using a GPU for LA.

Attempt to run 50+% memory over subscribed on a 6GB card. use_managed=1
Code:
saving the first 240 matrix rows for later
matrix includes 256 packed rows
matrix is 10820818 x 10821229 (4662.2 MB) with weight 1103319671 (101.96/col)
sparse part has weight 1049028095 (96.94/col)
using GPU 0 (NVIDIA GeForce GTX 1660)
selected card has CUDA arch 7.5
Nonzeros per block: 1750000000
Storing matrix in managed memory
converting matrix to CSR and copying it onto the GPU
1049028095 10820818 10821229
1049028095 10821229 10820818
commencing Lanczos iteration
vector memory use: 2311.7 MB
dense rows memory use: 330.2 MB
sparse matrix memory use: 8086.0 MB
memory use: 10727.9 MB
Allocated 761.4 MB for SpMV library
Allocated 761.4 MB for SpMV library
linear algebra at 0.1%, ETA 139h41m821229 dimensions (0.1%, ETA 139h41m)    
checkpointing every 80000 dimensions21229 dimensions (0.1%, ETA 139h44m)    
linear algebra completed 376713 of 10821229 dimensions (3.5%, ETA 136h25m)
Running without the use of a GPU.
Code:
saving the first 240 matrix rows for later
matrix includes 256 packed rows
matrix is 10820818 x 10821229 (4662.2 MB) with weight 1103319671 (101.96/col)
sparse part has weight 1049028095 (96.94/col)
using block size 8192 and superblock size 147456 for processor cache size 6144 kB
commencing Lanczos iteration (4 threads)
memory use: 6409.8 MB
linear algebra at 0.0%, ETA 105h56m821229 dimensions (0.0%, ETA 105h56m)    
checkpointing every 110000 dimensions1229 dimensions (0.0%, ETA 107h23m)    
linear algebra completed 45961 of 10821229 dimensions (0.4%, ETA 103h24m)
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