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This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current CUDA device query and kernel smoke tests issue: Inspect GPU temperature and power traces, clock throttle reasons, error counters and completed stress duration. Observed scenario: A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

This alternative is relevant to Cluster-wide preflight hardware inventory but does not directly resolve the current CUDA device query and kernel smoke tests issue: Capture a reproducible preflight inventory of node hardware, software, connections and expected acceptance tests. Observed scenario: A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current CUDA device query and kernel smoke tests issue: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. Observed scenario: The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch.

That option concerns DCGM active single-node diagnostic suites. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch.

This alternative is relevant to NCCL communicator and rank-to-device consistency but does not directly resolve the current CUDA device query and kernel smoke tests issue: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang. Observed scenario: The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch.

That option concerns Multi-process HPL rank placement and CPU affinity. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. Observed scenario: The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch. The governing mechanism is: Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator.

This alternative is relevant to Intra-node NCCL all-reduce test scope but does not directly resolve the current CUDA device query and kernel smoke tests issue: Inspect local device topology, nccl-tests rank count, all-reduce bandwidth and link error metrics. Observed scenario: The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch.

That option concerns HPL numerical result and residual validation. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. Observed scenario: The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch. The governing mechanism is: Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator. A direct check is: Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes. The acceptance condition is: Each intended GPU successfully initializes and executes a minimal repeatable CUDA workload under its supported stack.

This alternative is relevant to NCCL device buffers and data integrity checks but does not directly resolve the current CUDA device query and kernel smoke tests issue: The collective returns numerically valid outputs across intended ranks at accepted latency and bandwidth. Observed scenario: The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch.

This alternative is relevant to Multi-process HPL rank placement and CPU affinity but does not directly resolve the current CUDA device query and kernel smoke tests issue: Repeat runs produce stable accepted performance with no unintended GPU or host-core oversubscription. Observed scenario: The deployed driver and CUDA user-space runtime mismatch, and short GPU kernels fail at launch.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. An administrator runs only nvidia-smi on a controller and forgets to test compute on workers.

This alternative is relevant to Cluster-wide preflight hardware inventory but does not directly resolve the current CUDA device query and kernel smoke tests issue: Capture a reproducible preflight inventory of node hardware, software, connections and expected acceptance tests. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current CUDA device query and kernel smoke tests issue: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. An administrator runs only nvidia-smi on a controller and forgets to test compute on workers. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

That option concerns HPL configuration matrix dimensions and performance. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator.

This alternative is relevant to NCCL communicator and rank-to-device consistency but does not directly resolve the current CUDA device query and kernel smoke tests issue: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers.

That option concerns HPL configuration matrix dimensions and performance. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current CUDA device query and kernel smoke tests issue: Inspect GPU temperature and power traces, clock throttle reasons, error counters and completed stress duration. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers. The governing mechanism is: Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers. The governing mechanism is: Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator. A direct check is: Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes. The acceptance condition is: Each intended GPU successfully initializes and executes a minimal repeatable CUDA workload under its supported stack.

This alternative is relevant to NCCL device buffers and data integrity checks but does not directly resolve the current CUDA device query and kernel smoke tests issue: The collective returns numerically valid outputs across intended ranks at accepted latency and bandwidth. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current CUDA device query and kernel smoke tests issue: The node sustains approved workload performance without unplanned throttling, errors or facility alarms. Observed scenario: An administrator runs only nvidia-smi on a controller and forgets to test compute on workers.

This alternative is relevant to Multi-process HPL rank placement and CPU affinity but does not directly resolve the current HPL configuration matrix dimensions and performance issue: Align MPI ranks, CPU NUMA affinity and GPU allocation with the tested host and fabric topology. Observed scenario: A large HPL test shows low delivered FLOPS because the chosen matrix is too small to use cluster memory efficiently.

This alternative is relevant to HPL numerical result and residual validation but does not directly resolve the current HPL configuration matrix dimensions and performance issue: Check the complete HPL result including residual validity, process exit status and repeatability before acceptance. Observed scenario: A large HPL test shows low delivered FLOPS because the chosen matrix is too small to use cluster memory efficiently.

This tempting but unsafe shortcut is not a sufficient remediation for HPL configuration matrix dimensions and performance: Treat advertised GPU peak FLOPS as an actual measured HPL result without executing the benchmark.

This tempting but unsafe shortcut is not a sufficient remediation for HPL configuration matrix dimensions and performance: Treat advertised GPU peak FLOPS as an actual measured HPL result without executing the benchmark. HPL measures achieved dense linear-algebra throughput under a specific matrix and process mapping, not marketing peak clocks.

This alternative is relevant to NCCL device buffers and data integrity checks but does not directly resolve the current HPL configuration matrix dimensions and performance issue: Inspect nccl-tests output verification flags, message sizes, GPU logs and reproducible error traces. Observed scenario: A large HPL test shows low delivered FLOPS because the chosen matrix is too small to use cluster memory efficiently.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current HPL configuration matrix dimensions and performance issue: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. Observed scenario: A large HPL test shows low delivered FLOPS because the chosen matrix is too small to use cluster memory efficiently.