NIC autodetection based on PCI distance for cuda:X and/or cpu:Y (meta-pytorch#1305)

Summary:
Pull Request resolved: meta-pytorch#1305

Implements an automated RDMA Network Interface Card (NIC) selection system that automatically pairs compute devices (CUDA GPUs, CPU/NUMA nodes) with the optimal RDMA NICs based on PCI topology distance.

### **Key Components:**

1.  **PCI Topology Discovery** (`parse_pci_topology()`)

    *   Parses [`**/sys/bus/pci/devices/**`](command:code-compose.open?%5B%22%2Fsys%2Fbus%2Fpci%2Fdevices%2F%22%2Cnull%5D "/sys/bus/pci/devices/") to build a hierarchical tree of PCI devices
    *   Establishes parent-child relationships by following symlinks in the filesystem
    *   Creates a complete topology map of the system's PCI bus structure
2.  **Distance Calculation Algorithm** (`PCIDevice::distance_to()`)

    *   **Intra-NUMA Distance**: For devices on the same PCI tree, calculates hop count to the lowest common ancestor
    *   **Cross-NUMA Penalty**: Uses penalty-based scoring for devices across different NUMA domains:
        *   Base penalty: 20.0 (higher than typical intra-NUMA distances of 0-8 hops)
        *   Cross-domain penalty: 1000.0 for different PCI domains
        *   Bus distance scaling: 0.1 factor for tie-breaking between different bus numbers
3.  **Device Resolution Strategy**:

    *   **CUDA devices** (`cuda:N`): Maps GPU index to PCI address via [`**gpus**`](command:code-compose.open?%5B%22%2Fproc%2Fdriver%2Fnvidia%2Fgpus%22%2Cnull%5D "/proc/driver/nvidia/gpus")
    *   **CPU/NUMA devices** (`cpu:N`): Finds representative PCI device for NUMA node N
    *   **Direct NIC selection** (`nic:mlx5_N`): Bypasses topology and uses specified NIC directly
4.  **Unified Selection Interface** (`select_optimal_rdma_device()`)

    *   Supports format: `"type:id"` (e.g., `"cuda:0"`, `"cpu:1"`, `"nic:mlx5_3"`)
    *   Automatically finds the RDMA NIC with minimum PCI distance to the specified compute device
    *   Falls back to first available device if topology resolution fails

### **Algorithm Benefits:**

*   **Performance Optimization**: Ensures data paths use the shortest PCI routes, minimizing latency and maximizing bandwidth
*   **NUMA Awareness**: Heavily penalizes cross-NUMA communication to prefer local NICs
*   **Hardware Agnostic**: Works across different server configurations and PCI topologies
*   **Automatic Fallback**: Gracefully handles edge cases with sensible defaults

### **Validation Results:**

The algorithm was validated on GT20 hardware and produces identical GPU-to-NIC mappings as the proven Python reference implementation, demonstrating correctness and reliability.

This topology algorithm is critical for high-performance RDMA workloads where optimal device pairing can significantly impact communication performance in distributed computing scenarios.

Reviewed By: zdevito

Differential Revision: D83008226

fbshipit-source-id: cd94bca32815d4c10fbbdde2e93b23fd2cb44ef7

Author: dstaay-fb

monarch_rdma/Cargo.toml

@@ -17,6 +17,7 @@ cuda-sys = { path = "../cuda-sys" }
 hyperactor = { version = "0.0.0", path = "../hyperactor" }
 rand = { version = "0.8", features = ["small_rng"] }
 rdmaxcel-sys = { path = "../rdmaxcel-sys" }
+regex = "1.11.1"
 serde = { version = "1.0.219", features = ["derive", "rc"] }
 tracing = { version = "0.1.41", features = ["attributes", "valuable"] }