20220817, 18:06  #111 
"Ed Hall"
Dec 2009
Adirondack Mtns
1010001011010_{2} Posts 
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.

20220817, 18:30  #112 
"Evan"
Dec 2020
Montreal
2^{2}×3×7 Posts 
ah, in that case  you can look for the lanczos_kernel.ptx file (or stage1_core.ptx)
Last fiddled with by Plutie on 20220817 at 18:31 Reason: oops 
20220817, 18:35  #113 
"Ed Hall"
Dec 2009
Adirondack Mtns
2·5·521 Posts 

20220818, 13:14  #114 
"Ed Hall"
Dec 2009
Adirondack Mtns
12132_{8} Posts 
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 40thread machine and the GPU machine.
This is the 40thread (40GB) machine at start of LA: Code:
Wed Aug 17 22:53:22 2022 linear algebra at 0.0%, ETA 66h33m Code:
linear algebra completed 2537146 of 16995095 dimensions (14.9%, ETA 53h31m) Code:
Wed Aug 17 23:11:13 2022 linear algebra at 0.0%, ETA 24h39m Code:
linear algebra completed 6241861 of 16995095 dimensions (36.7%, ETA 15h35m) 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 
20220819, 03:12  #115 
Romulan Interpreter
"name field"
Jun 2011
Thailand
10,273 Posts 
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). 
20220819, 03:56  #116  
"Evan"
Dec 2020
Montreal
1010100_{2} Posts 
Quote:
here's a quick guide for linux specifically, but I don't think the process will be too different on windows. Quote:


20220829, 01:57  #117 
Sep 2008
Kansas
2^{2}×13×73 Posts 
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.

20221005, 23:21  #118 
Sep 2008
Kansas
2^{2}×13×73 Posts 
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) 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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