mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Dmitriy Korchemkin
bdee4d6172
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
114 lines
3.6 KiB
YAML
114 lines
3.6 KiB
YAML
name: Linux
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on: [push, pull_request]
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jobs:
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build:
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name: ${{matrix.os}}-${{matrix.build_type}}-${{matrix.lib}}-${{matrix.gpu}}
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runs-on: ubuntu-latest
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container: ${{matrix.os}}
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defaults:
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run:
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shell: bash -e -o pipefail {0}
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env:
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CCACHE_DIR: ${{github.workspace}}/ccache
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CMAKE_GENERATOR: Ninja
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DEBIAN_FRONTEND: noninteractive
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strategy:
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fail-fast: true
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matrix:
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os:
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- ubuntu:20.04
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- ubuntu:22.04
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build_type:
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- Release
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lib:
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- shared
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- static
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gpu:
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- cuda
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- no-cuda
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steps:
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- uses: actions/checkout@v3
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- name: Setup Dependencies
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run: |
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apt-get update
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apt-get install -y \
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build-essential \
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ccache \
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cmake \
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libbenchmark-dev \
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libblas-dev \
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libeigen3-dev \
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libgflags-dev \
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libgoogle-glog-dev \
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liblapack-dev \
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libmetis-dev \
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libsuitesparse-dev \
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ninja-build \
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wget
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# nvidia cuda toolkit shipped with 20.04 LTS does not support stream-ordered allocations
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- name: Setup CUDA toolkit repositories (20.04)
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if: matrix.gpu == 'cuda' && matrix.os == 'ubuntu:20.04'
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run: |
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wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb
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dpkg -i cuda-keyring_1.0-1_all.deb
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# nvidia cuda toolkit + gcc combo shipped with 22.04LTS is broken
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# and is not able to compile code that uses thrust
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# https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=1006962
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- name: Setup CUDA toolkit repositories (22.04)
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if: matrix.gpu == 'cuda' && matrix.os == 'ubuntu:22.04'
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run: |
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wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
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dpkg -i cuda-keyring_1.0-1_all.deb
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- name: Setup CUDA toolkit
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if: matrix.gpu == 'cuda'
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run: |
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apt-get update
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apt-get install -y cuda
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echo "CUDACXX=/usr/local/cuda/bin/nvcc" >> $GITHUB_ENV
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- name: Cache Build
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id: cache-build
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uses: actions/cache@v3
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with:
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path: ${{env.CCACHE_DIR}}
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key: ${{matrix.os}}-ccache-${{matrix.build_type}}-${{matrix.lib}}-${{matrix.gpu}}-${{github.run_id}}
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restore-keys: ${{matrix.os}}-ccache-${{matrix.build_type}}-${{matrix.lib}}-${{matrix.gpu}}-
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- name: Setup Environment
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if: matrix.build_type == 'Release'
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run: |
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echo 'CXXFLAGS=-flto' >> $GITHUB_ENV
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- name: Configure
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run: |
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cmake -S . -B build_${{matrix.build_type}} \
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-DBUILD_SHARED_LIBS=${{matrix.lib == 'shared'}} \
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-DUSE_CUDA=${{matrix.gpu == 'cuda'}} \
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-DCMAKE_BUILD_TYPE=${{matrix.build_type}} \
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-DCMAKE_C_COMPILER_LAUNCHER=$(which ccache) \
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-DCMAKE_CXX_COMPILER_LAUNCHER=$(which ccache) \
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-DCMAKE_INSTALL_PREFIX=${{github.workspace}}/install
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- name: Build
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run: |
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cmake --build build_${{matrix.build_type}} \
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--config ${{matrix.build_type}}
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- name: Test
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if: matrix.gpu == 'no-cuda'
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run: |
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cd build_${{matrix.build_type}}/
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ctest --config ${{matrix.build_type}} \
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--output-on-failure \
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-j$(nproc)
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- name: Install
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run: |
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cmake --build build_${{matrix.build_type}}/ \
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--config ${{matrix.build_type}} \
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--target install
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