For uc riverside hpcc infrastructure and platform teams

UC Riverside HPCC: GPU selection, preemption or kernel compatibility?

Connect the first useful exception with the relevant runtime evidence. Did the binary support the GPU selected by the generic request?

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Why this workflow may fit UC Riverside HPCC

UCR HPCC documents heterogeneous GPU types, explicit Slurm requests and separate preemptible capacity. An allocation and a worker launch need different checks.

Read the official UC Riverside HPCC workflow reference UC Riverside HPCC additional implementation context

Evaluation scope: A generic GPU request may select a different architecture. Current allocations and local policy supersede static inventories. Preemption is not proof of a failing device. 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 Riverside HPCC or an endorsement.

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

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Replay a resolved UC Riverside HPCC workflow incident

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

Before uploading, review cloud data handling and local options.

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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 binary support the GPU selected by the generic request? Only assign a rank, device or safe recovery state when the submitted evidence establishes it.

Did the binary support the GPU selected by the generic request?

Evidence to compare: Requested feature constraints, assigned GPU architecture and first kernel-launch exception.

A kernel compiled for one architecture may fail on another valid allocation. Compare the actual device and build before recommending a hardware reset or changing the model.

Does the executable match the allocated architecture?

Evidence to compare: Actual CPU and GPU architecture, image identity and the first loader error.

A binary built for another architecture can fail before useful computation. Confirm the allocated hardware and software combination before recommending changes to the device.

Was termination expected under the selected capacity policy?

Evidence to compare: Scheduler termination reason, allocation end time and last completed save.

A preemptible allocation can end without an application defect. Match the termination reason and inspect persisted state before diagnosing a device fault or suggesting resume.

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.