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Space Ranger Computing Options

Space Ranger Computing Options

There are three primary ways to run Space Ranger:

  1. Single server: Space Ranger can run directly on a dedicated server. This is the most straightforward approach and the easiest to troubleshoot.
  2. Job submission mode: Space Ranger can run using a single node on the cluster. Less cluster coordination is required since all work is done on the same node. This method works well even with job schedulers that are not officially supported.
  3. Cluster mode: Space Ranger can run using multiple nodes on the cluster. This method provides high performance but is difficult to troubleshoot since cluster setup varies among institutions.

Here is a summary of our recommendations and requirements, in order of computational speed (left to right):

Cluster ModeJob Submission ModeSingle Server
Recommended forOrganizations using an HPC with SGE or LSF for job schedulingOrganizations using an HPCUsers without access to an HPC
Compute detailsSplits each analysis across multiple compute nodes to decrease run timeRuns each analysis on a single compute nodeRuns each analysis directly on a dedicated server
RequirementsHPC with SGE or LSF for job schedulingHPC with most job schedulersLinux computer with minimum 8 cores & 64 GB RAM

The majority of the information on this website uses the single server approach. Follow the instructions below to analyze a 10x Genomics library:

  1. Generating FASTQs
  2. Specifying input FASTQs
  3. Single-library analysis

By default spaceranger uses all available cores and 90% of detected memory. This behavior may be undesirable in a shared environment with multiple concurrent users and tasks. It is strongly recommended to run spaceranger with --localcores and --localmem to specify resource usage upper bounds. In practice, there is negligible return in allocating more than 32 cores or 256G to the pipeline.

Space Ranger can be run in job submission mode, by treating a single node from the cluster like a local server. This leverages existing institutional hardware for pipeline analysis.

This mode of operation is what most people have in mind when it comes to working with clusters. A large computational job is submitted to the cluster and there is one job ID to track during job execution.

Space Ranger can be run in cluster mode to run the stages on multiple nodes via batch scheduling. This allows highly parallel stages to utilize hundreds or thousands of cores concurrently, dramatically reducing time to solution.

Instead of submitting one job to the cluster, Space Ranger creates hundreds, and potentially thousands, of small stage jobs. Each of these stage jobs needs to be queued, launched, and tracked by the pipeline framework. The necessary coordination between Space Ranger and the cluster makes this approach harder to set up and troubleshoot, since every cluster configuration is different.