monolish: MONOlithic Liner equation Solvers for Highly-parallel architecture

Related tags

Machine Learningcpuhpcgpumatrixlinear-algebracpp14openmpcudascientific-computingblaslapacklinear-algebra-librarysparse-matrixmklmatrix-structures
Overview

monolish: MONOlithic LIner equation Solvers for Highly-parallel architecture

monolish is a linear equation solver library that monolithically fuses variable data type, matrix structures, matrix data format, vendor specific data transfer APIs, and vendor specific numerical algebra libraries.

monolish let developer forget about:

  • Performance tuning
  • Processor differences which execute library (Intel CPU, NVIDIA GPU, AMD CPU, ARM CPU, NEC SX-Aurora TSUBASA, etc.)
  • Vendor specific data transfer APIs (host RAM to Device RAM)
  • Finding bottlenecks and performance benchmarks
  • The argument data type of matrix/vector operations
  • Matrix structures and storage formats
  • Cumbersome package dependency

License

Copyright 2021 RICOS Co. Ltd.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Comments
  • It seems that the `Dense(M, N, min, max)` constructor is not completely random.

    It seems that the `Dense(M, N, min, max)` constructor is not completely random.

    Running the following simple program

    #include <iostream>
#include <monolish_blas.hpp>

int main() {
  monolish::matrix::Dense<double>x(2, 3, 0.0, 10.0);
  x.print_all();
  return 0;
}

    $ g++ -O3 main.cpp -o main.out -lmonolish_cpu

    will produce results like this.

    [email protected]:/$ ./main.out
1 1 5.27196
1 2 2.82358 <--
1 3 2.13893 <--
2 1 9.72054
2 2 2.82358 <--
2 3 2.13893 <--
[email protected]:/$ ./main.out
1 1 5.3061
1 2 9.75236
1 3 7.15652
2 1 5.28961
2 2 2.05967
2 3 0.59838
[email protected]:/$ ./main.out
1 1 9.33149 <--
1 2 4.75639 <--
1 3 8.71093 <--
2 1 9.33149 <--
2 2 4.75639 <--
2 3 8.71093 <--

    The arrows (<--) indicate that the number is repeating.

    This is probably due to that the pseudo-random number generator does not split well when it is parallelized by OpenMP.

    https://github.com/ricosjp/monolish/blob/1b89942e869b7d0acd2d82b4c47baeba2fbdf3e6/src/utils/dense_constructor.cpp#L120-L127

    This may happen not only with Dense, but also with random constructors of other data structures.

    I tested this on docker image ghcr.io/ricosjp/monolish/mkl:0.14.1.

    opened by lotz84 5
  • impl. transpose matvec, matmul

    impl. transpose matvec, matmul

    I want to give modern and intuitive transposition information. But I have no idea how to implement it easily.

    First, we create the following function as a prototype

    matmul(A,B,C) // C=AB
matmul_TNN(A, B, C); // C=A^TB
matvec(A,x,y); // y = Ax
matvec_T(A, x, y); // y=A^Tx

    This interface is not beautiful. However, it has the following advantages

    • It does not affect other functions.
    • Easy to trace with logger
    • Simple to implement FFI in the future.
    • When beautiful ideas appear in the future, these functions can be implemented wrapping it.
    opened by t-hishinuma 2
  • try -fopenmp-cuda-mode flag

    try -fopenmp-cuda-mode flag

    memo:

    Clang supports two data-sharing models for Cuda devices: Generic and Cuda modes. The default mode is Generic. Cuda mode can give an additional performance and can be activated using the -fopenmp-cuda-mode flag. In Generic mode all local variables that can be shared in the parallel regions are stored in the global memory. In Cuda mode local variables are not shared between the threads and it is user responsibility to share the required data between the threads in the parallel regions.

    https://clang.llvm.org/docs/OpenMPSupport.html#basic-support-for-cuda-devices

    opened by t-hishinuma 2
  • Reserch the effect of the level information of the performance of cusparse ILU precondition

    Reserch the effect of the level information of the performance of cusparse ILU precondition

    The level information may not improve the performance but spend extra time doing analysis. For example, a tridiagonal matrix has no parallelism. In this case, CUSPARSE_SOLVE_POLICY_NO_LEVEL performs better than CUSPARSE_SOLVE_POLICY_USE_LEVEL. If the user has an iterative solver, the best approach is to do csrsv2_analysis() with CUSPARSE_SOLVE_POLICY_USE_LEVEL once. Then do csrsv2_solve() with CUSPARSE_SOLVE_POLICY_NO_LEVEL in the first run and with CUSPARSE_SOLVE_POLICY_USE_LEVEL in the second run, picking faster one to perform the remaining iterations.

    https://docs.nvidia.com/cuda/cusparse/index.html#csric02

    opened by t-hishinuma 2
  • ignoring return value in test

    ignoring return value in test 

    matrix_transpose.cpp:60:3: warning: ignoring return value of 'monolish::matrix::COO<Float>& monolish::matrix::COO<Float>::transpose() [with Float = double]', declared with attribute nodiscard [-Wunused-result]
   60 |   A.transpose();
    opened by t-hishinuma 2
  • Automatic deploy at release

    Automatic deploy at release

    impl. in github actions

    • [x] generate Doxyben (need to chenge version name)
    • [x] generate deb file
    • [x] generate monolish docker

    need to get version number...

    opened by t-hishinuma 2
  • write how to install nvidia-docker

    write how to install nvidia-docker 

    distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee > /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update -y
sudo apt install -y nvidia-docker2
sudo systemctl restart docker
    opened by t-hishinuma 1
  • Resolve conflict of libmonolish-cpu and libmonolish-nvidia-gpu deb package

    Resolve conflict of libmonolish-cpu and libmonolish-nvidia-gpu deb package

    What conflicts?

    libomp.so is contained in both package

    How to resolve?

    • (a) Use libomp5-12 distributed by ubuntu
    • (b) Create another package of libomp in allgebra stage (libomp-allgebra)
    opened by termoshtt 1
  • resolve curse of type name in src/

    resolve curse of type name in src/

    In src/, int and size_t are written. When change the class or function declarations in include/, I don't want to rewrite src/. Use auto or decltype() to remove them.

    opened by t-hishinuma 1
  • LLVM OpenMP Offloading can be installed by apt?

    LLVM OpenMP Offloading can be installed by apt? 

    docker run -it --gpus all -v $PWD:/work nvidia/cuda:11.7.0-devel-ubuntu22.04
==
apt update -y
apt install -y git intel-mkl cmake ninja-build ccache clang clang-tools libomp-14-dev gcc gfortran
git config --global --add safe.directory /work
cd /work; make gpu

    pass??

    opened by t-hishinuma 0
  • cusparse IC / ILU functions is deprecated

    cusparse IC / ILU functions is deprecated

    but, sample code of cusparse is not updated

    https://docs.nvidia.com/cuda/cusparse/index.html#csric02

    I dont like trial and error, so wait for the sample code to be updated.

    opened by t-hishinuma 0
Releases(0.17.0)
Owner
RICOS Co. Ltd.
株式会社科学計算総合研究所 / Research Institute for Computational Science Co. Ltd.
RICOS Co. Ltd.
GitHub Repository
Open MLOps - A Production-focused Open-Source Machine Learning Framework

Open MLOps - A Production-focused Open-Source Machine Learning Framework Open MLOps is a set of open-source tools carefully chosen to ease user experi

Data Revenue 590 Dec 28, 2022
Laporan Proyek Machine Learning - Azhar Rizki Zulma

Laporan Proyek Machine Learning - Azhar Rizki Zulma Project Overview Domain proyek yang dipilih dalam proyek machine learning ini adalah mengenai hibu

Azhar Rizki Zulma 6 Mar 12, 2022
Machine-Learning with python (jupyter)

Machine-Learning with python (jupyter) 머신러닝 야학 작심 10일과 쥬피터 노트북 기반 데이터 사이언스 시작 들어가기전 https://nbviewer.org/ 페이지를 통해서 쥬피터 노트북 내용을 볼 수 있다. 위 페이지에서 현재 레포 기

HyeonWoo Jeong 1 Jan 23, 2022
Simple data balancing baselines for worst-group-accuracy benchmarks.

BalancingGroups Code to replicate the experimental results from Simple data balancing baselines achieve competitive worst-group-accuracy. Replicating

Facebook Research 29 Dec 02, 2022
Customers Segmentation with RFM Scores and K-means

Customer Segmentation with RFM Scores and K-means RFM Segmentation table: K-Means Clustering: Business Problem Rule-based customer segmentation machin

5 Aug 10, 2022
DaCeML - Machine learning powered by data-centric parallel programming.

Data-centric machine learning powered by DaCe

SPCL 48 Dec 12, 2022
Predict the income for each percentile of the population (Python) - FRENCH

05.income-prediction Predict the income for each percentile of the population (Python) - FRENCH Effectuez une prédiction de revenus Prérequis Pour ce

1 Feb 13, 2022
An easier way to build neural search on the cloud

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the effici

Jina AI 17k Jan 01, 2023
A quick reference guide to the most commonly used patterns and functions in PySpark SQL

Using PySpark we can process data from Hadoop HDFS, AWS S3, and many file systems. PySpark also is used to process real-time data using Streaming and

Sundar Ramamurthy 53 Dec 21, 2022
Firebase + Cloudrun + Machine learning

A simple end to end consumer lending decision engine powered by Google Cloud Platform (firebase hosting and cloudrun)

Emmanuel Ogunwede 8 Aug 16, 2022
pymc-learn: Practical Probabilistic Machine Learning in Python

pymc-learn: Practical Probabilistic Machine Learning in Python Contents: Github repo What is pymc-learn? Quick Install Quick Start Index What is pymc-

pymc-learn 196 Dec 07, 2022
Binary Classification Problem with Machine Learning

Binary Classification Problem with Machine Learning Solving Approach: 1) Ultimate Goal of the Assignment: This assignment is about solving a binary cl

Dinesh Mali 0 Jan 20, 2022
XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

92 Dec 14, 2022
A linear equation solver using gaussian elimination. Implemented for fun and learning/teaching.

A linear equation solver using gaussian elimination. Implemented for fun and learning/teaching. The solver will solve equations of the type: A can be

Sanjeet N. Dasharath 3 Feb 15, 2022
Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Amplo 10 May 15, 2022
Open source time series library for Python

PyFlux PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array

Ross Taylor 2k Jan 02, 2023
A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

⚡ funk-svd funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize co

Geoffrey Bolmier 171 Dec 19, 2022
Exemplary lightweight and ready-to-deploy machine learning project

Exemplary lightweight and ready-to-deploy machine learning project

snapADDY GmbH 6 Dec 20, 2022
Flightfare-Prediction - It is a Flightfare Prediction Web Application Using Machine learning,Python and flask

Flight_fare-Prediction It is a Flight_fare Prediction Web Application Using Machine learning,Python and flask Using Machine leaning i have created a F

1 Dec 06, 2022
A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

matrixprofile-ts matrixprofile-ts is a Python 2 and 3 library for evaluating time series data using the Matrix Profile algorithms developed by the Keo

Target 696 Dec 26, 2022