Zarr DataLoader Performance Tuning

Use this skill when optimizing nvalchemi Zarr reads or writing stores that will later be read through the nvalchemi DataLoader.

Overview

The pipeline has clean ownership boundaries:

• Reader: storage I/O only. Returns raw CPU tensor dictionaries plus metadata.
• Dataset: validation, optional validation skipping, device transfer, and async

prefetch orchestration. Its canonical explicit batch API is loadbatches(batchindex_lists).

• DataLoader: sampler/batch iteration, fused prefetch, stream usage, and batch

construction.

• MultiDataset: global index composition over multiple Datasets while routing

load_batches requests to child datasets.

• Sampler / batch_sampler: semantic sample order and batch membership. Do not

rely on sampler windows to optimize storage I/O.

Reader public methods:

• reader.read(index): one sample.
• reader.read_many(indices): many samples, returned in the request order.

Reader backend hooks:

• loadsample(index): implement for simple single-sample formats.
• loadmany_samples(indices): implement for batch-optimized formats.
• len(): total logical samples.

The base Reader owns metadata finalization and optional pinned memory. Index validity is the concrete reader's responsibility. AtomicDataZarrReader supports negative logical indices, maps through the active sample mask, and implements loadmany_samples as the fast path.

Recommended DataLoader setup

from nvalchemi.data.datapipes import (
    AtomicDataZarrReader,
    Dataset,
    DataLoader,
)

reader = AtomicDataZarrReader("store.zarr")

dataset = Dataset(
    reader,
    device="cuda",
    num_workers=1,          # 1 is enough; concurrent Zarr reads contend
    skip_validation=True,   # safe when store was written by the toolkit
)

loader = DataLoader(
    dataset,
    batch_size=64,
    shuffle=True,
    prefetch_factor=16,     # up to 64 * 16 = 1024 indices per backend read
    num_streams=2,
    use_streams=True,
    pin_memory=True,        # request pinned CPU tensors from the reader
)

Use pinmemory=True on AtomicDataZarrReader(...) directly only for manual reader usage. For normal training, prefer DataLoader(..., pinmemory=True) so the loader owns the transfer optimization.

Key knobs

prefetch_factor (DataLoader)

Controls how many emitted batches are fused into one backend read:

effective_read_window = batch_size * prefetch_factor

For batchsize=64, prefetchfactor=16, the model still receives batches of 64 graphs, but the Zarr reader sees up to 1024 logical indices per read_many.

Use prefetchfactor=0 to disable fused prefetch and issue one backend read per emitted batch through Dataset.loadbatches([indices]). This is useful for debugging or for stores where larger windows do not help. Positive prefetchfactor values use the async prefetchfusedbatches(...) / getfused_batches() path.

Manual batch reads should use:

batches = dataset.load_batches([[0, 4, 2], [8, 1, 3]])

skip_validation (Dataset)

Bypasses per-sample AtomicData Pydantic validation (~4 ms/sample). Constructs Batch directly from raw tensor dicts via Batch.fromrawdicts().

Use when: the store was written by AtomicDataZarrWriter or has been validated externally. Do not use when: the store contents are untrusted or from a third party.

num_workers (Dataset)

Thread pool size for background Dataset prefetch work. Start with 1. Increase only if profiling shows CPU-side validation or device transfer is underlapping and storage reads are not contending.

pin_memory (DataLoader or Reader)

Pinned CPU tensors make async CPU-to-GPU transfer possible. Use with CUDA targets and use_streams=True.

Normal path:

loader = DataLoader(dataset, batch_size=64, pin_memory=True)

Manual reader path:

reader = AtomicDataZarrReader("store.zarr", pin_memory=True)
data, metadata = reader.read(0)

Writing stores for fast random reads

For shuffled training reads, avoid extremely large chunks unless reads are mostly sequential. A practical starting point:

from zarr.codecs import ZstdCodec

from nvalchemi.data.datapipes import (
    AtomicDataZarrWriter,
    ZarrWriteConfig,
    ZarrArrayConfig,
)

config = ZarrWriteConfig(
    core=ZarrArrayConfig(
        compressors=(ZstdCodec(level=3),),
        chunk_size=10_000,
        shard_size=500_000,
    ),
)
writer = AtomicDataZarrWriter("store.zarr", config=config)

Guidance:

• chunk_size is rows along dimension 0, not number of structures. Atom fields

are stored on the total atom axis; edge fields on the total edge axis.

• Smaller chunks reduce single-sample read amplification but increase metadata

and codec overhead.

• Sharding groups many chunks into fewer storage objects and is useful when small

chunks would create too many files.

• Use edgechunksize / edgeshardsize in nvalchemi-io-test when edge

arrays need different tuning from atom/system arrays.

• Zstd level 3 is a good default ratio/speed tradeoff. LZ4 is useful when write

and decompression speed matter more than compression ratio.

How the reader optimises random access

AtomicDataZarrReader.loadmanysamples(indices) is the optimized path behind public reader.readmany(indices).

It currently:

1. Resolves logical indices through the active sample mask.
2. Sorts requests by physical sample index.
3. Groups physical positions by Zarr chunk locality.
4. Uses coalesced range reads when a small number of chunk-local runs exists.
5. Falls back to orthogonal selection for highly fragmented requests.
6. Restores the caller's original request order.

This is transparent to Dataset, DataLoader, and Samplers. Larger fused read windows give the Zarr backend more indices to coalesce, which is why prefetch_factor matters most for shuffled reads.

For multidataset training, use MultiDatasetBatchSampler or MultiDatasetBatchSampler.balanced(...) to define semantic dataset mixing rates. samplesperdataset may be integer counts or float ratios. Use epochpolicy="maxsize", replacement=True when smaller datasets should be oversampled so the largest dataset does not dominate an epoch.

Benchmark workflow

Use the current CLI subcommands:

# Self-contained write + read benchmark.
env COLUMNS=240 uv run nvalchemi-io-test roundtrip \
    -n 10000 \
    --read-mode batch \
    --read-order shuffle \
    --batch-size 64 \
    --prefetch-factor 16 \
    --pin-memory

# Sweep prefetch factors on the same access pattern.
for pf in 8 16 32 64 128; do
    env COLUMNS=240 uv run nvalchemi-io-test roundtrip \
        -n 10000 \
        --read-mode batch \
        --read-order shuffle \
        --batch-size 64 \
        --prefetch-factor "$pf" \
        --pin-memory
done

# Benchmark an existing store without rewriting it.
env COLUMNS=240 uv run nvalchemi-io-test read /path/to/store.zarr \
    --read-order shuffle \
    --batch-size 64 \
    --prefetch-factor 32 \
    --pin-memory

# Compare DataLoader fused reads against one-sample-at-a-time reads.
env COLUMNS=240 uv run nvalchemi-io-test read /path/to/store.zarr \
    --read-mode both \
    --read-order shuffle \
    --batch-size 64 \
    --prefetch-factor 32

Important benchmark semantics:

• read-mode=batch uses the public DataLoader path with fused prefetch.
• Benchmark batch mode uses Dataset(skip_validation=True) to focus on storage

and batching throughput.

• read-mode=single calls reader.read(index) once per sample and is only a

baseline for one-sample-at-a-time access.

• batch_size is the model-facing batch size.
• prefetch_factor controls the backend read window.
• Use read-order=shuffle to model fully shuffled training reads.
• Use read-order=block-shuffle to test partial locality.

Diagnosing bottlenecks

1. Run nvalchemi-io-test read on an existing representative store.
2. Sweep prefetchfactor at the target batchsize.
3. Compare read-mode=batch against read-mode=single.
4. If batch mode is fast but training is slow, inspect validation, batching, and

device-transfer overhead. Try skipvalidation=True, pinmemory=True, and CUDA streams.

1. If batch mode is slow, inspect chunk/shard configuration, compression codec,

filesystem metadata pressure, and read order.

Quick checklist

• Use Dataset(skip_validation=True) for trusted toolkit-written stores.
• Use DataLoader(pin_memory=True) for CUDA training.
• Start with batch_size=64.
• Start with prefetch_factor=16 or 32 for shuffled reads.
• Sweep prefetch_factor=8,16,32,64,128 with nvalchemi-io-test.
• Keep sampler semantics independent from storage locality.
• Use load_batches(...) for explicit batch reads.
• Tune chunk/shard sizes on a representative store and filesystem.
• Use read-mode=single only as a baseline, not as the training path.