Exploring All Options of the bsub Command in IBM LSF Scheduler

Introduction

IBM Spectrum LSF (Load Sharing Facility) Scheduler offers a wide range of powerful features to efficiently manage high-performance computing workloads. The bsub command is a fundamental tool for submitting jobs to the scheduler, and it comes with various options to customize job requirements and behavior. In this blog, we will explore examples of all the essential options available with the bsub command, empowering you to make the most of IBM LSF's capabilities.

  1. Job Name (-J)

The -J option allows you to specify a name for your job, providing a meaningful identifier in the queue. For instance:

bsub -J my_job_name < my_script.sh
  1. Number of CPU Cores (-n)

You can request a specific number of CPU cores for your job using the -n option. For example:

bsub -n 8 < my_script.sh
  1. CPU Resource Requirements (-R)

With the -R option, you can define complex resource requirements. For example, to request 2 CPU cores per host:

bsub -R "span[ptile=2]" < my_script.sh
  1. Wall Clock Time (-W)

To set the maximum time duration for your job, use the -W option. For example, to request 1 hour:

bsub -W 1:00 < my_script.sh
  1. Queue Selection (-q)

You can specify a particular queue for your job using the -q option. For instance:

bsub -q my_queue < my_script.sh
  1. Job Dependencies (-w)

To create job dependencies, use the -w option. For example, to run "job2" after "job1" completes:

bsub -w "ended(job1)" < job2.sh
  1. Environment Variables (-env)

The -env option allows you to set environment variables for your job. For example:

bsub -env "OMP_NUM_THREADS=4" < my_script.sh
  1. Standard Output and Error (-o, -e)

You can redirect the standard output and error streams to specific files using the -o and -e options, respectively. For instance:

bsub -o output.log -e error.log < my_script.sh

Conclusion

IBM LSF Scheduler's bsub command provides a comprehensive set of options for fine-tuning your job submissions. In this blog, we explored examples of essential options like job name, CPU core allocation, resource requirements, wall clock time, queue selection, job dependencies, environment variables, and standard output/error redirection. Armed with this knowledge, you can effectively harness the power of IBM LSF Scheduler to optimize your high-performance computing workloads and streamline job management.

Comments

Post a Comment

Popular posts from this blog

PyTorch Tutorial: Using ImageFolder with Code Examples

In this tutorial, we'll explore how to use PyTorch's ImageFolder dataset to load and preprocess image data efficiently. The ImageFolder dataset is a handy utility for handling image datasets organized in a specific folder structure. Table of Contents Introduction to ImageFolder Prerequisites Setting up the Dataset Data Augmentation (Optional) DataLoader Model and Training (Brief Overview) Let's get started! 1. Introduction to ImageFolder ImageFolder is a PyTorch dataset class designed to work with image data organized in folders. Each folder corresponds to a specific class, and images within those folders belong to that class. This structure makes it easy to load data for tasks like image classification. 2. Prerequisites Before we start, make sure you have the following installed: Python (>= 3.6) PyTorch (>= 1.8.0) torchvision (>= 0.9.0) You can install PyTorch and torchvision using pip: pip install torch torchvision 3. Setting up the Da...
Read more

A Tutorial on IBM LSF Scheduler with Examples

Introduction to IBM LSF Scheduler: IBM Spectrum LSF (Load Sharing Facility) is a powerful workload management and job scheduling system designed to optimize the utilization of computing resources in high-performance computing (HPC) environments. LSF provides a robust set of features that enable efficient resource allocation, job scheduling, and management, making it an essential tool for large-scale parallel computing. In this tutorial, we will explore the basics of using IBM LSF Scheduler, covering key concepts and command-line examples to help you get started with managing your HPC workload effectively. LSF Terminology: Host: A computing resource available in the cluster, which can be a physical machine or a virtual machine. Queue: A group of hosts sharing similar characteristics, such as hardware configuration or software environment. Job: A unit of work submitted to the scheduler, which consists of executable code and its resource requirements. Job ID: A unique identifie...
Read more

Explaining Chrome Tracing JSON Format

Chrome Tracing JSON format, also known as "Chrome trace events" or "Chrome performance trace," is a widely used data format for recording and analyzing performance data in web applications. It provides a detailed and structured representation of various events occurring during the execution of a web application, making it valuable for performance optimization and debugging. In this post, we'll delve into the Chrome Tracing JSON format, its structure, and how to interpret it with examples. JSON Format Overview Chrome Tracing JSON format consists of an array of trace events, where each event represents a specific point in time during the application's execution. Each event contains mandatory fields, such as "name" and "ts" (timestamp), along with optional fields like "args" (additional event-specific data) and "pid" (process ID). Example Chrome Tracing JSON: [ { "name" : "Event1" , ...
Read more