Every tape op allocates its output via Tensor::zeros -> GpuBuffer::alloc ->
cudaMalloc, a synchronous process-serialized driver call. Under the single-
process thread-per-GPU DDP model the rank threads' hundreds of per-step allocs
serialize through the driver (KI-5 root cause); it costs single-GPU too.
Add a per-device, size-classed caching pool: GpuBuffer::alloc serves from a
free-list (request rounded up to a size class so repeating training shapes
reuse buffers), only cudaMalloc on a miss; Drop returns the buffer to the pool
instead of cudaFree. Thread-safe via a global registry keyed by device id with
each device's free-list behind its own Mutex (registry lock held only to clone
out the per-device Arc<Mutex<_>>, so rank threads don't contend across devices).
The buffer records its alloc-time device so Drop returns to the right pool.
Transparent: physical capacity may be rounded up, but len()/memset/copy bounds
all use the requested length, so the rounded tail is never read and numerics are
unchanged. zeros() still memsets (reused buffers hold stale bytes).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Default-stream kernels run in order and every host read goes through a
stream-ordered cudaMemcpy (to_device), so the per-op cudaDeviceSynchronize
after each kernel was pure overhead — remove all 21 in tensor.rs. Host
data is still correctly ordered by the D2H memcpy that reads it.
Also zero op-output buffers with cudaMemset (device-side, async) instead of
a blocking H2D memcpy of a host zero buffer on every allocation — that
copy was itself a hidden per-op sync point.
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>
