Workflows

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845 Workflows visible to you, out of a total of 923
My COMPSs Fibonacci Series
Stable

Calculates the Fibonacci series up to a specified length.

Type: COMPSs

Creator: Uploading this Workflow under the guidance of Raül Sirvent.

Submitter: Ashish Bhawel

COMPSs GPU Cache Matrix Multiplication
Stable

Name: Matmul GPU Case 1 Cache-ON Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

Matmul running on the GPU leveraging COMPSs GPU Cache for deserialization speedup. Launched using 32 GPUs (16 nodes). Performs C = A @ B Where A: shape (320, 56_900_000) block_size (10, 11_380_000)             B: shape (56_900_000, 10)   block_size (11_380_000, 10)             C: shape (320, 10)                block_size ...

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.798.1

COMPSs GPU Matrix Multiplication
Stable

Name: Matmul GPU Case 1 Cache-OFF Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs 3.3 Machine: Minotauro-MN4

Matmul running on the GPU without Cache. Launched using 32 GPUs (16 nodes). Performs C = A @ B Where A: shape (320, 56_900_000) block_size (10, 11_380_000)             B: shape (56_900_000, 10)   block_size (11_380_000, 10)             C: shape (320, 10)                block_size (10, 10) Total dataset size 291 ...

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.797.1

COMPSs GPU K-Means
Stable

Name: K-Means GPU Cache OFF Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

K-Means running on GPUs. Launched using 32 GPUs (16 nodes). Parameters used: K=40 and 32 blocks of size (1_000_000, 1200). It creates a block for each GPU. Total dataset shape is (32_000_000, 1200). Version dislib-0.9

Average task execution time: 194 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.799.1

COMPSs GPU Cache K-Means
Stable

Name: K-Means GPU Cache ON Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

K-Means running on the GPU leveraging COMPSs GPU Cache for deserialization speedup. Launched using 32 GPUs (16 nodes). Parameters used: K=40 and 32 blocks of size (1_000_000, 1200). It creates a block for each GPU. Total dataset shape is (32_000_000, 1200). Version dislib-0.9

Average task execution time: 16 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.800.1

COMPSs GPU Cache DNN Distributed Training
Stable

Name: Dislib Distributed Training - Cache ON Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

PyTorch distributed training of CNN on GPU and leveraging COMPSs GPU Cache for deserialization speedup. Launched using 32 GPUs (16 nodes). Dataset: Imagenet Version dislib-0.9 Version PyTorch 1.7.1+cu101

Average task execution time: 36 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.802.1

COMPSs GPU DNN Distributed Training
Stable

Name: Dislib Distributed Training - Cache OFF Contact Person: [email protected] Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

PyTorch distributed training of CNN on GPU. Launched using 32 GPUs (16 nodes). Dataset: Imagenet Version dislib-0.9 Version PyTorch 1.7.1+cu101

Average task execution time: 84 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.801.1

HiC scaffolding pipeline
Stable

HiC scaffolding pipeline

Snakemake pipeline for scaffolding of a genome using HiC reads using yahs.

Prerequisites

This pipeine has been tested using Snakemake v7.32.4 and requires conda for installation of required tools. To run the pipline use the command:

snakemake --use-conda --cores N

where N is number of cores to use. There are provided a set of configuration and running scripts for exectution on a slurm queueing system. After configuring the cluster.json file run:

./run_cluster ...

Type: Snakemake

Creator: Tom Brown

Submitter: Tom Brown

DOI: 10.48546/workflowhub.workflow.796.2

Purge retained haplotypes using Purge-Dups

Purge dups

This snakemake pipeline is designed to be run using as input a contig-level genome and pacbio reads. This pipeline has been tested with snakemake v7.32.4. Raw long-read sequencing files and the input contig genome assembly must be given in the config.yaml file. To execute the workflow run:

snakemake --use-conda --cores N

Or configure the cluster.json and run using the ./run_cluster command

Type: Snakemake

Creator: Tom Brown

Submitter: Tom Brown

DOI: 10.48546/workflowhub.workflow.506.2

HiC contact map generation
Stable

HiC contact map generation

Snakemake pipeline for the generation of .pretext and .mcool files for visualisation of HiC contact maps with the softwares PretextView and HiGlass, respectively.

Prerequisites

This pipeine has been tested using Snakemake v7.32.4 and requires conda for installation of required tools. To run the pipline use the command:

snakemake --use-conda

There are provided a set of configuration and running scripts for exectution on a slurm queueing system. After configuring ...

Type: Snakemake

Creator: Tom Brown

Submitter: Tom Brown

DOI: 10.48546/workflowhub.workflow.795.2

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