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Block-sparse to CRS conversion using block-structure

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Instead of pre-computing pemutation from block-sparse to CRS order,
index of value in CRS matrix is computed in the process of updating
values using block-sparse structure.

When it is possible to update values via a simple host-to-device copy,
block-sparse structure on GPU is discarded after computing CRS
structure.

Computing index is significantly slower than using pre-computed
permutation, but is still hidden by host-to-device transfer.

On problems from BAL dataset this results into reduction of extra
gpu memory consumption from 33% (permutation stored as 32-bit indices)
to ~10% for storing block-sparse structure.

Benchmark results:

======================= CUDA Device Properties ======================
Cuda version         : 11.8
Device ID            : 0
Device name          : NVIDIA GeForce RTX 2080 Ti
Total GPU memory     :  11012 MiB
GPU memory available :  10852 MiB
Compute capability   : 7.5
Warp size            : 32
Max threads per block: 1024
Max threads per dim  : 1024 1024 64
Max grid size        : 2147483647 65535 65535
Multiprocessor count : 68
====================================================================
Running ./bin/evaluation_benchmark
Run on (112 X 3200 MHz CPU s)
CPU Caches:
  L1 Data 32 KiB (x56)
  L1 Instruction 32 KiB (x56)
  L2 Unified 1024 KiB (x56)
  L3 Unified 39424 KiB (x2)
Load Average: 24.58, 11.75, 8.52

-----------------------------------------------------------------------
Benchmark                                                          Time
-----------------------------------------------------------------------
Using on-the-fly computation of CRS index corresponding to block-sparse
index:

JacobianToCRS<g/final/problem-4585-1324582-pre.txt>             1607 ms
JacobianToCRSView<g/final/problem-4585-1324582-pre.txt>          564 ms
JacobianToCRSMatrix<g/final/problem-4585-1324582-pre.txt>       2226 ms
JacobianToCRSViewUpdate<g/final/problem-4585-1324582-pre.txt>    228 ms
JacobianToCRSMatrixUpdate<g/final/problem-4585-1324582-pre.txt>  400 ms

Using precomputed permutation:
JacobianToCRS</final/problem-4585-1324582-pre.txt>              1656 ms
JacobianToCRSView</final/problem-4585-1324582-pre.txt>           553 ms
JacobianToCRSMatrix</final/problem-4585-1324582-pre.txt>        2255 ms
JacobianToCRSViewUpdate</final/problem-4585-1324582-pre.txt>     228 ms
JacobianToCRSMatrixUpdate</final/problem-4585-1324582-pre.txt>   406 ms

Performance of JacobianToCRSViewUpdate is still limited by
host-to-device transfer, and JacobianToCRSView is faster than computing
CRS structure on CPU.

Change-Id: Ifb6910fb01ae6071400d36c277846fadc5857964
This commit is contained in:
Dmitriy Korchemkin
2023-05-17 14:43:33 +03:00
parent 0f9de3daf4
commit bdee4d6172
23 changed files with 828 additions and 479 deletions
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.github/workflows/linux.yml
+8 -5
View File
@@ -49,20 +49,23 @@ jobs:
libsuitesparse-dev \
ninja-build \
wget
- name: Setup CUDA toolkit (system repositories)
# nvidia cuda toolkit shipped with 20.04 LTS does not support stream-ordered allocations
- name: Setup CUDA toolkit repositories (20.04)
if: matrix.gpu == 'cuda' && matrix.os == 'ubuntu:20.04'
run: |
apt-get install -y \
nvidia-cuda-dev \
nvidia-cuda-toolkit
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb
dpkg -i cuda-keyring_1.0-1_all.deb
# nvidia cuda toolkit + gcc combo shipped with 22.04LTS is broken
# and is not able to compile code that uses thrust
# https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=1006962
- name: Setup CUDA toolkit (nvidia repositories)
- name: Setup CUDA toolkit repositories (22.04)
if: matrix.gpu == 'cuda' && matrix.os == 'ubuntu:22.04'
run: |
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
dpkg -i cuda-keyring_1.0-1_all.deb
- name: Setup CUDA toolkit
if: matrix.gpu == 'cuda'
run: |
apt-get update
apt-get install -y cuda
echo "CUDACXX=/usr/local/cuda/bin/nvcc" >> $GITHUB_ENV