Msieve GPU Linear Algebra

[EdH]

Originally Posted by Plutie

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.

[Plutie]

ah, in that case - you can look for the lanczos_kernel.ptx file (or stage1_core.ptx)

[EdH]

Originally Posted by Plutie

ah, in that case - you can look for the lanczos_kernel.ptx file (or stage1_core.ptx)

Thanks! I'll work with that.

[EdH]

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:

Wed Aug 17 22:53:22 2022  linear algebra at 0.0%, ETA 66h33m

and, current state (08:13):

linear algebra completed 2537146 of 16995095 dimensions (14.9%, ETA 53h31m)

Here is the GPU machine at start of LA:

Wed Aug 17 23:11:13 2022  linear algebra at 0.0%, ETA 24h39m

and. current state (08:13):

linear algebra completed 6241861 of 16995095 dimensions (36.7%, ETA 15h35m)

Here's a litle extra from the GPU machine log:

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.

[LaurV]

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).

[Plutie]

Originally Posted by LaurV

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.

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

compilation example here is for a GTX 1060 (CC 6.1)

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.

[RichD]

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.

[RichD]

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

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.

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)