Overhead introduced by constructing buffers for bfloat16 dtypes on read

I've been running some benchmarks to test bandwidth on tensorstore reads, and I've found significant overhead for non-standard dtypes (e.g. bfloat16) where tensorstore seems to initialize the array to zero before overwriting it with the data. Here's the benchmark that I've been running: ```python import time import tensorstore as ts dataset = ts.open( { "driver": "zarr3", "kvstore": { "driver": "gcs", "bucket": "...", "path": "...", }, "open": True, "create": False, } ).result() start = time.perf_counter() array = dataset.read().result() duration = time.perf_counter() - start gb = array.nbytes / 1024**3 bandwidth = gb / duration print(f"{gb:.2f} GB loaded in {duration:.2f} s ({bandwidth:.2f} GB/s)") ``` where the tensor that I'm loading is about 55 GB of bfloat16 data. If I run this benchmark using the current master branch of tensorstore, I see: ``` 54.93 GB loaded in 58.35 s (0.94 GB/s) ``` but, if I disable zero initialization on reads (more details below), I get: ``` 54.93 GB loaded in 31.47 s (1.75 GB/s) ``` which suggests that there's about 30s of overhead introduced by the current implementations construction of the 55 GB array! I've narrowed the offending line down to: https://github.com/google/tensorstore/blob/367ef7d380618624b8f7e5078b1765345b65e3a4/tensorstore/data_type.cc#L86 I can avoid this overhead by hacking tensorstore to skip that `r->construct(...)` or replace [this line](https://github.com/google/tensorstore/blob/367ef7d380618624b8f7e5078b1765345b65e3a4/tensorstore/util/bfloat16.h#L76) with: ```diff - constexpr BFloat16() : rep_(0) {} + BFloat16() = default; ``` for bfloat16 specifically. I wanted to ask here if anyone had advice about what would be the preferred approach for safely avoiding this overhead. I'd be very happy to open a PR if we decide that there's something general to do here. Thanks!!