The Ephemeral Filesystem Problem: A P2P Benchmark Script's Journey Through a Kernel Upgrade

Introduction

In the midst of an intensive performance optimization campaign for the GLM-5-NVFP4 large language model running on eight NVIDIA RTX PRO 6000 Blackwell GPUs, a seemingly trivial problem emerged: a freshly rebooted Linux container had lost its files. Message 1324 captures the moment when an AI assistant, moments after executing a major kernel upgrade from 6.8.12 to 6.14.11 and successfully rebooting the host system, discovers that the P2P bandwidth benchmark script it intended to run has vanished. The response is not merely a re-upload — it is a carefully constructed diagnostic instrument, a Python script designed to measure peer-to-peer GPU bandwidth across NUMA domains, that embodies the assistant's deep understanding of the system's topology and the performance questions that need answering.

This message, while appearing at first glance to be a simple file transfer, is in fact a pivotal moment in a much larger investigation. It represents the transition from system-level tuning (kernel upgrade, CPU governor changes, PCIe configuration) to performance validation — the moment where hypotheses about the impact of those changes must be tested against empirical measurement. The script itself is a miniature performance laboratory, encoding assumptions about GPU topology, NUMA boundaries, and the specific bandwidth characteristics that matter for distributed inference.

Context: The Road to the Reboot

To understand message 1324, one must appreciate the journey that preceded it. The assistant had been engaged in a weeks-long effort to optimize the inference throughput of the GLM-5-NVFP4 model, a massive Mixture-of-Experts architecture deployed via SGLang across eight GPUs. Despite achieving impressive raw throughput numbers — over 3,700 tokens per second in some configurations — the assistant had identified a troubling discrepancy: the theoretical maximum single-stream performance was calculated at approximately 309 tokens per second, but actual measurements showed only 10.36 tokens per second. This represented a staggering 3.4% efficiency, meaning the system was achieving less than 4% of its theoretical capability.

The investigation into this gap had been thorough. A parallel audit involving ten subagents had uncovered multiple system-level misconfigurations: the CPU frequency scaling driver was acpi-cpufreq instead of the superior amd_pstate; the kernel was outdated at 6.8.12; NUMA balancing was enabled; CPU C-states were too deep; and the PCIe MaxReadReq was stuck at 512 bytes instead of the optimal 4096. Each of these issues represented a potential drag on performance, and collectively they painted a picture of a system that was fundamentally misconfigured for high-performance GPU compute.

The assistant had methodically addressed each issue. Runtime fixes were applied where possible, but the most impactful changes — the kernel upgrade, the amd_pstate=active boot parameter, and the processor.max_cstate=1 setting — required a full system reboot. Message 1313 captured the moment the assistant sent the reboot command, and messages 1315-1317 documented the successful return to a system now running kernel 6.14.11 with the amd-pstate-epp driver active, only C0 and C1 C-states available, and all eight GPUs detected and healthy.

The Ephemeral Filesystem Problem

Message 1324 opens with a concise diagnosis: "Container restarted, /tmp is clean." This single sentence reveals the assistant's immediate understanding of the failure mode. The LXC container, which hosts the GPU compute environment, had been restarted after the host reboot. In a containerized environment, the /tmp directory is typically ephemeral — its contents are not preserved across container restarts. The P2P benchmark script that had been previously uploaded to /tmp/p2p_bench.py was gone, and the attempt to execute it in message 1323 had failed with a "No such file or directory" error.

This is a classic operational pitfall in containerized environments. Temporary filesystems are, by design, temporary. The assistant's response demonstrates an understanding of this reality — rather than expressing surprise or frustration, it immediately pivots to a solution: re-upload the script. The use of scp with a heredoc (<< 'PYEOF') is a clever technique that avoids the need for a temporary file on the host side, streaming the script content directly from the local session to the remote container.

The Benchmark Script: A Diagnostic Instrument

The Python script that constitutes the bulk of message 1324 is far more than a simple bandwidth test. It is a carefully designed diagnostic instrument that encodes specific knowledge about the system's topology and the performance characteristics that matter for the GLM-5-NVFP4 inference workload.

Architecture of the Test

The script defines two core functions: measure_p2p for GPU-to-GPU transfers and measure_host_gpu for host-to-GPU and GPU-to-host transfers. Both functions follow the same pattern: they allocate tensors on source and destination devices, perform warm-up iterations to stabilize the GPU state, then measure the time of a batch of transfers using CUDA events — the gold standard for precise GPU timing.

The use of torch.cuda.Event with enable_timing=True is a deliberate choice. CUDA events provide nanosecond-precision timing by recording timestamps on the GPU itself, avoiding the pitfalls of CPU-side timing which can be distorted by kernel launch latency, driver scheduling, and other overheads. The script records a start event, executes iters copies in a stream, records an end event, and then synchronizes to ensure all work is complete before reading the timestamps.

The transfer size of 256 MB is also significant. This is large enough to saturate the PCIe Gen5 x16 link (which has a theoretical bandwidth of approximately 64 GB/s in each direction) and to amortize any per-transfer overhead. The choice of 50 iterations provides a statistically meaningful sample, reducing the impact of individual outliers.

Topology Encoding

The most interesting aspect of the script is how it encodes the assistant's understanding of the GPU topology. The test pairs are carefully chosen:

• Same-NUMA pairs: (0,1), (1,2), (2,3), (4,5), (5,6), (6,7) — these test P2P bandwidth within the same NUMA domain, where GPUs are likely connected via the same PCIe root complex or NVSwitch.
• Cross-NUMA pairs: (0,4), (0,7), (3,4), (3,7) — these test P2P bandwidth across NUMA boundaries, which is critical for understanding the cost of cross-socket communication in a dual-socket AMD EPYC system. The selection of GPU 0 and GPU 4 as representative endpoints for host-GPU transfers is also deliberate. GPU 0 is likely on NUMA node 0 and GPU 4 on NUMA node 1, allowing the script to measure whether host access latency differs between NUMA domains — a common issue in multi-socket systems where memory affinity matters.

Assumptions Embedded in the Script

Every benchmark makes assumptions, and this one is no exception. Several implicit assumptions are worth examining:

Assumption 1: P2P Access is Available

The script assumes that direct P2P access between GPUs is enabled and functional. On systems with PCIe Gen5 switches or NVLink, this is typically true for GPUs within the same NUMA domain. However, cross-NUMA P2P access may require going through the CPU's PCIe root complexes or even through system memory, which would dramatically reduce bandwidth. The script's structure — testing both same-NUMA and cross-NUMA pairs — acknowledges this uncertainty and is designed to measure it.

Assumption 2: CUDA Stream Semantics are Correct

The script uses a single CUDA stream for all operations, which is correct for serialized copy operations. However, it does not test overlapping transfers or bidirectional bandwidth, which could reveal different performance characteristics. The assumption is that unidirectional bandwidth is the primary metric of interest.

Assumption 3: Tensor Allocation Does Not Introduce Bias

The script allocates fresh tensors for each measurement and calls torch.cuda.empty_cache() after each test. This is a reasonable approach to avoid memory fragmentation effects, but it also means that each test starts with a "cold" GPU memory state, which could differ from the steady-state behavior during actual inference.

Assumption 4: 256 MB is Representative

The fixed transfer size of 256 MB may not be representative of the actual communication patterns in the GLM-5-NVFP4 inference workload. MoE models like GLM-5 involve many small all-to-all communication operations between experts, which may be dominated by latency rather than bandwidth. The script's focus on bandwidth (GB/s) implicitly assumes that the workload is bandwidth-bound, which may not be the case for small-message collective operations.

The Thinking Process Visible in the Message

The assistant's reasoning is evident in the structure of the response. The opening line — "Container restarted, /tmp is clean" — is a diagnosis that draws on system administration knowledge: temporary filesystems are not persistent across container restarts. The solution — "Let me re-upload" — is immediate and practical.

The choice of scp with a heredoc rather than a multi-line ssh command or a separate file creation step reveals an understanding of the constraints. The script needs to be on the remote container's filesystem to be executed by Python. Using scp ensures the file is transferred atomically and has the correct permissions. The heredoc avoids the need for intermediate files on the host, which is important in a session where the assistant is working through a series of SSH commands and doesn't have a persistent local workspace.

The script itself reveals a deep understanding of GPU benchmarking methodology. The use of CUDA events, the warm-up iterations, the choice of transfer size, and the careful selection of test pairs all demonstrate that the assistant is not merely running a generic benchmark but is constructing a targeted diagnostic tool for a specific system and workload.

What This Message Creates

Message 1324 creates several forms of output knowledge:

1. A reusable benchmark script — The Python code uploaded to /tmp/p2p_bench.py is a standalone diagnostic tool that can be executed repeatedly to measure P2P bandwidth under different system configurations.
2. A baseline for comparison — The script is designed to be run immediately after the kernel upgrade, providing a post-tuning baseline that can be compared against pre-reboot measurements (if any) and against theoretical maximums.
3. NUMA topology validation — By testing both same-NUMA and cross-NUMA pairs, the script will validate whether the assistant's understanding of the GPU topology (which GPUs share a NUMA node) is correct.
4. Host-GPU transfer characterization — The H2D and D2H measurements will reveal whether host memory bandwidth is a bottleneck, which is relevant for any workload that involves CPU-side data preparation.

Potential Limitations and Missed Opportunities

While the script is well-designed for its purpose, several limitations are worth noting:

The script does not test bidirectional bandwidth, which can be significantly higher than unidirectional bandwidth on NVLink-connected GPUs. It also does not test the latency of small transfers, which may be more representative of the actual MoE communication patterns in the GLM-5 model. The fixed 256 MB size is appropriate for saturation bandwidth measurement but may miss important characteristics at smaller message sizes.

The script also does not include any error handling for cases where P2P access is not available between specific GPU pairs. On some configurations, attempting a direct copy between GPUs on different NUMA nodes may fall back to host-memory copies, which would produce misleadingly low bandwidth numbers without any indication that the fallback occurred.

Conclusion

Message 1324 is a deceptively simple response to a mundane operational problem — a file lost to an ephemeral filesystem. But the content of that file reveals the assistant's sophisticated understanding of GPU topology, benchmarking methodology, and the specific performance questions that need answering after a major system reconfiguration. The P2P benchmark script is not just a test; it is a hypothesis about what matters for performance, encoded in Python and CUDA calls.

The message also demonstrates a key characteristic of effective AI-assisted system administration: the ability to recognize and adapt to the ephemeral nature of containerized environments, to reconstruct lost state without frustration, and to embed deep domain knowledge into the tools being created. In the broader context of the GLM-5-NVFP4 optimization campaign, this message represents the moment of validation — the point at which the system-level changes (kernel upgrade, CPU governor, C-state limits, PCIe tuning) are about to be tested against empirical measurement, and the assistant is ensuring that the right instruments are in place.