Records the key empirical finding: process-per-GPU is statistically identical
to thread-per-GPU at this scale (thread 5.27x vs proc 5.31x @8, <1% noise; all
8 GPUs 95-99% util). The residual ~5.3x@8 non-linearity is the NCCL/PCIe
communication wall, NOT single-CUDA-context launch/cuBLAS serialization as the
old KI-5/T11 note speculated — measurement falsifies that hypothesis (same
methodology as T11 falsifying "bucket the all-reduce"). Correctness all green:
proc==thread loss 1.5e-7, cross-rank 1.2e-7, full regression + xserv md5
b04fc9f9 identical. Closes the process-per-GPU backlog item (measured no-op);
default training path unchanged. evolution.md Infra row + README T17 row +
known-issues entry.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Fill in the design doc's measured results (grad-check, flash==composed,
PyTorch parity, peak mem -16%/-23%, tok/s tradeoff), add the T14 row to
evolution.md (算法/Infra) and the README build-journey table.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stand up the xtrain project skeleton: a Cargo workspace mirroring xserv's
csrc/ + crates/ layout, with a single xtrain-cuda crate that wraps the CUDA
Runtime over hand-written extern "C" FFI. build.rs compiles csrc/test/vecadd.cu
via the cc crate targeting sm_120 (RTX 5090) and links cudart.
A gated integration test runs the vector-add kernel on the GPU and asserts the
result. When nvcc is absent (local GPU-less machine), build.rs skips CUDA
compilation and sets a `no_cuda` cfg so host-side cargo check still works.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
