The P2P Benchmark That Proved the Kernel Upgrade: Validating System Tuning After a CUDA Crisis

Introduction

In the long arc of optimizing inference throughput for the GLM-5-NVFP4 model across eight RTX PRO 6000 Blackwell GPUs, few moments are as deceptively quiet as message 1343. On its surface, this message is simply the output of a bandwidth benchmark—a Python script measuring peer-to-peer (P2P) GPU transfers and host-device bandwidth. But to read it as merely a data dump misses the entire drama that precedes it. This message is the culmination of a multi-hour debugging saga involving a kernel upgrade, a post-reboot CUDA meltdown, stale device major numbers in LXC cgroup configuration, and the quiet satisfaction of seeing a complex system come back to life with all tuning parameters verified. It is a validation message, a sanity check, and a milestone all at once.

The Message Itself

The assistant executed a single bash command via SSH into the LXC container at 10.1.230.174, activating the Python virtual environment and running a pre-uploaded benchmark script:

GPU P2P Bandwidth Test - 256MB transfers, CUDA events timing
Kernel: 6.14.11, amd_pstate=active, max_cstate=1, MaxReadReq=4096
=================================================================
Pair              BW GB/s     Lat ms         Type
-----------------------------------------------------------------
GPU0->GPU1           50.4       4.96    SAME-NUMA
GPU1->GPU2           50.0       5.00    SAME-NUMA
GPU2->GPU3           50.0       5.00    SAME-NUMA
GPU0->GPU3           49.8       5.02    SAME-NUMA
GPU0->GPU4           37.1       6.74    CROSS-NUMA
GPU0->GPU7           37.0       6.76    CROSS-NUMA
GPU3->GPU4           37.1       6.74    CROSS-NUMA
GPU3->GPU7           37.0       6.76    CROSS-NUMA
GPU4->GPU5           50.1       4.99    SAME-NUMA
GPU5->GPU6           50.0       5.00    SAME-NUMA
GPU6->GPU7           50.0       5.00    SAME-NUMA

Host <-> GPU transfers:
-----------------------------------------------------------------
Host->GPU0           53.2       4.70    H2D
GPU0->Host           53.5       4.67    D2H
Host->GPU4           53.1       4.71    H2D
GPU4->Host           53.3       4.69    D2H

The header proudly announces the kernel version (6.14.11) and the three key tuning parameters that were the entire point of the preceding system audit: amd_pstate=active, max_cstate=1, and MaxReadReq=4096. These are not incidental details—they are the trophies of a hard-won battle.

Why This Message Was Written: The Reasoning and Motivation

To understand why this message exists, one must understand what came before it. The session had spent hours conducting a comprehensive system audit via ten parallel agents, uncovering critical misconfigurations: a suboptimal CPU governor (acpi-cpufreq instead of amd_pstate), an outdated kernel (6.8.12), enabled NUMA balancing, deep CPU C-states, and a PCIe MaxReadReq stuck at 512 bytes instead of 4096. All of these were identified as potential contributors to a staggering efficiency gap—the theoretical maximum single-stream performance was 309 tok/s, but actual throughput was only 10.36 tok/s, a mere 3.4% efficiency.

The team applied runtime fixes and executed a major kernel upgrade to 6.14.11 with amd_pstate=active and processor.max_cstate=1, requiring a full reboot. But when the container came back, CUDA was completely broken inside the LXC. The root cause was subtle: the new kernel reassigned device major numbers for the NVIDIA UVM and caps devices. The old cgroup rules allowed c 504:* rwm and c 507:* rwm, but the new kernel used 509 for nvidia-uvm and 237 for nvidia-caps. The container was denied access to these devices, and PyTorch returned a cryptic CUDA_ERROR_UNKNOWN (error code 999).

The assistant had to stop the container, edit the LXC configuration file (/etc/pve/lxc/129.conf), replace the stale major numbers, and restart. Only then did CUDA come back to life.

So when message 1343 is written, the motivation is clear: verify that everything works after the kernel upgrade and the cgroup fix. The P2P benchmark is not about discovering new performance characteristics—it is about confirmation. The assistant needs to prove to itself (and to the user) that:

1. CUDA is fully functional across all 8 GPUs
2. P2P bandwidth is consistent and matches expectations for PCIe Gen5
3. The kernel tuning parameters are actually in effect (they appear in the benchmark header)
4. The system is ready for the next phase of inference benchmarking## How Decisions Were Made: The Benchmark as a Verification Tool The decision to run this particular benchmark was not arbitrary. The assistant had already computed the theoretical maximum single-stream performance and identified a massive gap between theory and reality. The system audit had uncovered numerous potential bottlenecks, and the kernel upgrade was the most invasive fix applied. Before proceeding to re-run the full inference benchmark suite (which would take significant time), the assistant needed a lightweight, fast verification that the foundation was solid. The P2P benchmark serves this purpose perfectly. It tests: - GPU-to-GPU bandwidth (both same-NUMA and cross-NUMA), which validates that the PCIe topology, NVLink connections, and driver stack are working correctly - Host-to-GPU and GPU-to-Host bandwidth, which validates that the CPU-side memory path and PCIe tuning (MaxReadReq=4096) are effective - CUDA event timing, which validates that the CUDA runtime and driver are properly initialized The assistant chose 256 MB transfers with 50 iterations—large enough to saturate the PCIe bus and get stable measurements, but small enough to complete quickly. The use of CUDA events for timing (rather than CPU-side time.time()) ensures nanosecond-precision measurement that is not distorted by kernel dispatch latency.

Assumptions Made by the Assistant

Several assumptions underpin this message. The most important is that the P2P benchmark results are a reliable proxy for system health. The assistant assumes that if P2P bandwidth matches expected values (~50 GB/s same-NUMA, ~37 GB/s cross-NUMA, ~53 GB/s host-GPU), then the kernel upgrade, cgroup fix, and PCIe tuning are all working correctly. This is a reasonable assumption—if any of those components were broken, the benchmark would show dramatically lower numbers or outright failures.

The assistant also assumes that the benchmark's header (which prints the kernel version and tuning parameters) is accurate. This is verified by the fact that the SSH command runs inside the container, which inherits the host kernel. The amd_pstate=active and max_cstate=1 parameters are kernel boot parameters, so they are system-wide.

Another assumption is that the bandwidth numbers are PCIe Gen5-limited rather than GPU-compute-limited. At ~50 GB/s for same-NUMA transfers, this is consistent with PCIe 5.0 x16 theoretical bandwidth (~64 GB/s bidirectional, ~32 GB/s unidirectional per direction). The assistant implicitly assumes that no further tuning of the PCIe subsystem is needed—a judgment that later proves correct when inference benchmarks show the bottleneck is elsewhere.

Mistakes or Incorrect Assumptions

One subtle mistake in this message is the implication that the kernel tuning parameters printed in the benchmark header are the cause of the observed bandwidth. In reality, the P2P bandwidth is primarily a function of PCIe generation, link width, and GPU architecture—none of which changed with the kernel upgrade. The amd_pstate=active and max_cstate=1 parameters affect CPU frequency management and wake-up latency, not PCIe transfer bandwidth. The MaxReadReq=4096 tuning could affect bandwidth for large transfers, but the difference between 512 bytes and 4096 bytes is most visible in small, scattered reads rather than sustained 256 MB transfers.

The assistant does not explicitly acknowledge this nuance in the message. However, in the follow-up message ([msg 1344]), the assistant correctly notes: "The bandwidth is PCIe-limited so kernel changes don't change the raw numbers. The improvement should show up in CPU-side latency (kernel dispatch, scheduling) which affects inference throughput." This shows that the assistant understood the limitations of the benchmark—it was a validation of correctness, not a measurement of improvement.

Another potential issue: the benchmark only tests GPU pairs (0,1), (1,2), (2,3), (0,3) for same-NUMA and (0,4), (0,7), (3,4), (3,7) for cross-NUMA. This covers the topology but does not exhaustively test all 28 possible GPU pairs. The assumption is that the topology is symmetric (GPUs 0-3 on one NUMA node, 4-7 on another), which the benchmark confirms.## Input Knowledge Required to Understand This Message

To fully grasp what this message means, a reader needs several layers of context:

1. The system architecture: Eight RTX PRO 6000 Blackwell GPUs split across two NUMA nodes (GPUs 0-3 on node 0, GPUs 4-7 on node 1), connected via PCIe Gen5. The system runs Ubuntu 24.04 inside a Proxmox LXC container, with NVIDIA driver 590.48.01 and CUDA 13.1.
2. The kernel upgrade history: The system was originally running kernel 6.8.12 with acpi-cpufreq governor. A major upgrade to 6.14.11 was performed to enable amd_pstate=active and processor.max_cstate=1, which required a reboot.
3. The CUDA crisis: After the reboot, CUDA failed inside the LXC container with error code 999 (CUDA_ERROR_UNKNOWN). The root cause was that the new kernel reassigned device major numbers for nvidia-uvm (from 504 to 509) and nvidia-caps (from 507 to 237), but the LXC cgroup configuration still referenced the old numbers, blocking access.
4. The cgroup fix: The assistant stopped the container, edited /etc/pve/lxc/129.conf to replace the stale major numbers, and restarted. CUDA then worked.
5. The benchmark script: The Python script (/tmp/p2p_bench.py) was re-uploaded in message 1342 because the container restart had wiped /tmp. It uses CUDA events for precise timing, measures 256 MB transfers with 50 iterations, and tests both same-NUMA and cross-NUMA GPU pairs plus host-GPU transfers. Without this context, the message reads as a routine benchmark output. With the context, it reads as a triumphant return from a near-catastrophic failure.

Output Knowledge Created by This Message

This message produces several pieces of actionable knowledge:

1. Confirmation of CUDA functionality: All 8 GPUs are accessible and can perform P2P transfers. The earlier CUDA_ERROR_UNKNOWN is fully resolved.
2. Baseline P2P bandwidth: Same-NUMA transfers achieve ~50 GB/s, cross-NUMA ~37 GB/s, and host-GPU ~53 GB/s. These numbers serve as a baseline for future comparisons—if a subsequent change degrades performance, re-running this benchmark will reveal it.
3. Topology verification: The consistent bandwidth within each NUMA group and the drop across NUMA nodes confirms the expected topology. GPUs 0-3 share a PCIe root complex, GPUs 4-7 share another, and inter-NUMA transfers go through the CPU socket interconnect.
4. PCIe tuning validation: The MaxReadReq=4096 setting (visible in the benchmark header) is confirmed to be active. While the benchmark doesn't directly measure the effect of this parameter, its presence in the header proves the systemd tuning service (gpu-pcie-tuning.service) is working.
5. A checkpoint for the todo list: The assistant's todo list includes "Verify P2P works with micro-benchmark after changes" with status "completed." This message is the evidence for that completion.

The Thinking Process Visible in the Reasoning

The assistant's reasoning is visible in the structure of the benchmark and the choice of what to print. The header deliberately includes the kernel version and tuning parameters—this is not just decoration. It is the assistant's way of saying: "I have verified that the changes I made are actually in effect." The kernel command line parameters (amd_pstate=active, max_cstate=1) are not normally visible from inside a container; the assistant had to explicitly pass them through by hardcoding them in the Python script's print statement. This shows deliberate, careful thinking about what constitutes proof.

The choice of GPU pairs also reveals reasoning. The assistant tests:

• Intra-NUMA pairs within the first group (0→1, 1→2, 2→3, 0→3)
• Cross-NUMA pairs (0→4, 0→7, 3→4, 3→7)
• Intra-NUMA pairs within the second group (4→5, 5→6, 6→7) This covers the diagonal (adjacent GPUs), the full span within a NUMA node, and the critical cross-NUMA paths. It is a minimal set that maximizes diagnostic coverage. The host-GPU test on both GPU 0 and GPU 4 (one per NUMA node) shows the assistant is thinking about NUMA effects on host-device transfers as well. If one NUMA node had a broken PCIe path, the host-GPU bandwidth would differ between GPU 0 and GPU 4. The fact that both show ~53 GB/s confirms symmetry.

Conclusion

Message 1343 is a quiet milestone in a tumultuous optimization journey. It is the moment when the system, after a kernel upgrade that broke CUDA and required a delicate cgroup surgery, is declared healthy and ready for the next phase. The P2P benchmark numbers are not spectacular—they are exactly what PCIe Gen5 should deliver. But that is precisely the point. In system optimization, the goal is often not to achieve extraordinary numbers but to eliminate the obstacles that prevent ordinary, expected performance. The kernel upgrade, the cgroup fix, the PCIe tuning—none of these made the P2P bandwidth faster. But they restored the system to a state where the real bottleneck (the FP4 GEMM kernel efficiency on SM120) could be properly diagnosed and addressed.

The message is a testament to the value of systematic verification. Before chasing the next optimization, you must prove that the foundation is solid. The P2P benchmark is that proof.