For uc san diego tscc infrastructure and platform teams

UC San Diego TSCC: Hotel partition, Condo allocation or device mapping?

Connect the first useful exception with the relevant runtime evidence. Did the program use an index outside its assigned device list?

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Why this workflow may fit UC San Diego TSCC

TSCC distinguishes Hotel and Condo GPU partitions and uses Slurm to assign devices. The scheduler supplies process-visible GPU identifiers.

Read the official UC San Diego TSCC workflow reference UC San Diego TSCC additional implementation context

Evaluation scope: This is the shared computing cluster, not the Triton programming language. Do not overwrite scheduler-provided device mappings or assume one Condo GPU configuration applies everywhere. Use approved redacted evidence. No native integration, automatic repair or current outage is implied.

Prepared from public materials. This is a proposed evaluation, not a report of an incident at UC San Diego TSCC or an endorsement.

Selectable demo logs are shared examples, not logs from UC San Diego TSCC. Upload a shareable incident from your own workflow to evaluate the fit.

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Replay a resolved UC San Diego TSCC workflow incident

Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.

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Up to 50 diagnoses a day for 30 days. Verify your work email to activate an eligible company evaluation, then replay a resolved incident and compare the evidence and next steps with your team's confirmed cause.

What a useful evaluation should establish

Compare one incident your team already resolved with the first failure, cited evidence and remaining questions. Did the program use an index outside its assigned device list? Only assign a rank, device or safe recovery state when the submitted evidence establishes it.

Did the program use an index outside its assigned device list?

Evidence to compare: Hotel or Condo partition, allocated GPU count and process-visible device identifiers.

Application-local GPU indices can differ from physical card numbers. Compare the assigned mapping before interpreting an invalid ordinal as an unavailable or defective GPU.

Was a GPU assigned to the affected process?

Evidence to compare: Job resource request, actual allocation and process-visible device mapping.

A GPU-capable node does not prove this process owns a device. Establish the allocation before interpreting missing visibility as a driver failure.

Did the application use the workers it requested?

Evidence to compare: Launch configuration, actual process count and per-worker progress before failure.

Additional resources do not automatically create parallel execution. Establish how the application launched before interpreting unused capacity or synchronization symptoms.

Use Denpex alongside your existing telemetry tools to investigate submitted incident evidence. With monitoring configured on a supported plan, detected failures can trigger alerts and an automatic diagnosis from the captured evidence.

Inside Denpex

See the evidence, not just the claim

Follow the causal chain, inspect the quoted evidence and prepare the next handoff. These actual product captures use founder-account examples and the VS Code extension, with the source identified under each image. Open a capture to inspect it at full size.