Performance

• Use batched reads (the default with DistDataset, or get_batch() directly). They cut per-sample cost by 10–27× for small rows and make the GPU path insensitive to worker threads; in the VAE every configuration got 1.2–3.9× faster per epoch.

• Reuse destination buffers and register_recv() them. Reading into a fresh buffer every time re-registers memory on every read; get_profile(name)["mr_miss"] counts those registrations.

• method=1 (one-sided fi_read) is the fastest backend; method=0 with batching (collective) comes close for small rows.

• DDSTORE_PROFILE=1 + get_profile(name) shows where get()/get_batch() time goes: lock wait, memory registration, posting and completing fi_read, GPU sync. vae-ddp.py prints an all-rank summary when it is set. examples/scripts/bench_get.py measures per-row latency and throughput vs row size, destination, batch size and threads.

Measurements, profiles and the experiments behind these choices: docs/results.md.