Distributed computing aims to offer tools and mechanisms that enable the sharing, selection, and aggregation of a wide variety of geographically distributed computational resources in a transparent way. The research done in this team is based on the past expertise of the group, and on extending it towards the aspects of distributed computing that can benefit from this expertise. The team at BSC has a strong focus on programming models and resource management and scheduling in distributed computing ...

Team created to publish applications during COMPSs Tutorials, and share them among participants.

This team is to publish workflows executed from WPs that are not the three main pillars of the project.

It explores the modelling of natural catastrophes – in particular, earthquakes and their associated tsunamis shortly after such an event is recorded.

It develops innovative adaptive workflows for climate and for the study of Tropical Cyclones (TC) in the context of the CMIP6 experiment, including in-situ analytics.

It focuses on the construction of DigitalTwins for the prototyping of complex manufactured objects integrating state-of-the-art adaptive solvers with machine learning and data-mining, contributing to the Industry 4.0 vision.

Project that aims to create the NeuroPlat portal for neurodrug design

Provenance registration is becoming more and more important, as we increase the size and number of experiments performed using computers. In particular, when provenance is recorded in HPC environments, it must be efficient and scalable. In this paper, we propose a provenance registration method for scientific workflows, efficient enough to run in supercomputers (thus, it could run in other ...

Session during the Innovative HPC workflows for industry (https://eflows4hpc.eu/event/innovative-hpc-workflows-for-industry/) that describes how Workflow Provenance is recorded with COMPSs: the background on the tools used, how the recording has been designed, and how to use it and inspect metadata.

Cheat sheet that serves as a quick guide to the many advanced features that PyCOMPSs offers. Updated for COMPSs release version 3.3.2.

COMPSs Matrix Multiplication resourceUsage profiling example.

MN5 MSIZE=20 BSIZE=768 7 Nodes (6 workers) (--num_nodes=7 --worker_in_master_cpus=0).

• Total number of tasks: 20^3 = 8000
• Maximum code parallelism: 20^2 = 400
• Total cores: 112*6 = 672
• Maximum utilisation: 400 / 112 = 3,57 Nodes

Overall stats from "pycompss inspect":

│ └── overall 
│ ├── matmul_tasks 
│ │ └── multiply 
│ │ ├── maxTime = 91,111 ms 
│ │ ├── executions = 8,000 
│ │ ├── avgTime = 84,839 ms 
│ │ └── minTime = 79,278 ms
...

Application that perform the multiplication between matrices. In this experiment, a new profiling visualization is available, showing the resource usage such as CPU, memory, data read and written to disk, and data sent and received over the network.

Lysozyme in water full COMPSs application. Added new WRROC profile: Provenance Run Crate

Application that perform the multiplication between matrices.

Name: PhysioNet kNN Kfold Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum5

Kfold to evaluate kNN accuracy on PhysioNet dataset (https://b2drop.bsc.es/index.php/s/8Q8MefXX2rrzaWs). This application used dislib-0.9.0

Name: PhysioNet CascadeCSVM Kfold Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum5

Kfold to evaluate CascadeCSVM accuracy on PhysioNet dataset (https://b2drop.bsc.es/index.php/s/8Q8MefXX2rrzaWs). This application used dislib-0.9.0

Name: PhysioNet RF Kfold Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum5

Kfold to evaluate RandomForest accuracy on PhysioNet dataset (https://b2drop.bsc.es/index.php/s/8Q8MefXX2rrzaWs). This application used dislib-0.9.0

Name: Random Forest Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum4 This is an example of Random Forest algorithm from dislib. To show the usage, the code generates a synthetical input matrix. The results are printed by screen. This application used dislib-0.9.0

Name: GridSearchCV Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum5

GridSearch of kNN algorithm for the iris.csv dataset (https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv). This application used dislib-0.9.0

Name: GridSearchCV Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum5

GridSearch of kNN algorithm for the iris.csv dataset (https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv). This application used dislib-0.9.0

Name: Matrix Multiplication Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs

Description

Matrix multiplication is a binary operation that takes a pair of matrices and produces another matrix.

If A is an n×m matrix and B is an m×p matrix, the result AB of their multiplication is an n×p matrix defined only if the number of columns m in A is equal to the number of rows m in B. When multiplying A and B, the elements of the ...

Name: Matrix Multiplication Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs

Description

Matrix multiplication is a binary operation that takes a pair of matrices and produces another matrix.

If A is an n×m matrix and B is an m×p matrix, the result AB of their multiplication is an n×p matrix defined only if the number of columns m in A is equal to the number of rows m in B. When multiplying A and B, the elements of the ...

Lysozyme in water full COMPSs application

Lysozyme in water full COMPSs application, using dataset_small

Lysozyme in water full COMPSs application

Name: KMeans Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum5

KMEans for clustering the housing.csv dataset (https://github.com/sonarsushant/California-House-Price-Prediction/blob/master/housing.csv). This application used dislib-0.9.0

Name: TruncatedSVD (Randomized SVD) Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: MareNostrum5

TruncatedSVD (Randomized SVD) for computing just 456 singular values out of a (4.5M x 850) size matrix. The input matrix represents a CFD transient simulation of air moving past a cylinder. This application used dislib-0.9.0

Application that counts the number of words in the passed a file or a group of files.

Name: SparseLU Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs

Description

The Sparse LU application computes an LU matrix factorization on a sparse blocked matrix. The matrix size (number of blocks) and the block size are parameters of the application.

As the algorithm progresses, the area of the matrix that is accessed is smaller; concretely, at each iteration, the 0th row and column of the current matrix are discarded. ...

Solutions to the exercises on the Workflow Provenance part, during January 2024 COMPSs Tutorial.