CDL - Celeres Data Loader
A Parallel Data Loading System with Constant-Memory Shuffling for Scalable Deep Learning Training
The Problem
Training large models requires shuffling billions of samples every epoch. Traditional shuffling creates two scaling bottlenecks:
| Fisher-Yates Shuffle | CDL | |
|---|---|---|
| Shuffle memory | O(N) — 8 GB for 1B samples | O(1) — 24 bytes |
| Disk seeks per epoch | N random seeks | N / block_size sequential reads |
| Epoch initialisation | O(N) permutation | O(1) key derivation |
| Checkpoint size | ~8 GB shuffle state | 24 bytes |
| Distributed sync | Broadcast O(N) array | Zero sync (same seed) |
How It Works
CDL replaces the O(N) permutation array with a two-level Feistel network that computes shuffled indices on the fly:
Level 1 — Block Order Level 2 — Intra-Block
┌──────────────────────┐ ┌──────────────────────┐
│ Block 0 ──────► B3 │ │ Offset 0 ──────► O4 │
│ Block 1 ──────► B0 │ │ Offset 1 ──────► O2 │
│ Block 2 ──────► B4 │ │ Offset 2 ──────► O0 │
│ ... │ │ ... │
│ Block N ──────► B1 │ │ Offset B ──────► O3 │
└──────────────────────┘ └──────────────────────┘
Shuffles WHICH blocks Shuffles items WITHIN
to read from disk each block (in RAM)
The disk reads stay sequential within each block (minimising seeks), while items are still fully shuffled across the epoch.
Quick Start
Install
pip install celeres-dl
As a PyTorch Sampler (drop-in replacement)
from torch.utils.data import DataLoader
from cdl import CDLSampler
dataset = torchvision.datasets.ImageNet(root="./data", split="train")
sampler = CDLSampler(dataset, block_size=1024, seed=42)
loader = DataLoader(dataset, sampler=sampler, batch_size=128)
for epoch in range(100):
sampler.set_epoch(epoch) # O(1) — instant!
for batch in loader:
train(batch)
Full I/O-Optimised Loader
from cdl import CDLLoader, HDF5BlockDataset
dataset = HDF5BlockDataset("train.h5") # block size auto-inferred
loader = CDLLoader(dataset, batch_size=64, seed=42)
for epoch in range(100):
loader.set_epoch(epoch) # 24-byte state change
for images, labels in loader:
train(images, labels)
state = loader.get_state() # checkpoint: 24 bytes!
save_checkpoint(model, state)
Benchmarks
Memory Usage
| Dataset Size | Fisher-Yates | CDL | Savings |
|---|---|---|---|
| 1 Million | 8 MB | 24 B | 99.9997% |
| 100 Million | 800 MB | 24 B | 99.9999% |
| 1 Billion | 8 GB | 24 B | 99.9999% |
I/O Efficiency
| Metric | Random Shuffle | CDL (block=1024) |
|---|---|---|
| I/O Locality | 0.03–0.43% | 98–99% |
| Disk Seeks / Epoch | N | N / 1024 |
| HDD Speedup | 1× | 25–30× |
Shuffle Quality
| Metric | CDL | vs Random |
|---|---|---|
| Batch Entropy | 96.85% | 0.03% gap |
| Training Accuracy (CIFAR-10) | 69.79% | +0.50% vs 69.29% |
API Reference
CDLSampler
from cdl import CDLSampler
sampler = CDLSampler(
data_source=dataset, # or dataset_size=N
block_size=1024,
seed=42,
rounds=6,
)
sampler.set_epoch(epoch) # O(1)
state = sampler.get_state() # 24-byte checkpoint
CDLLoader
from cdl import CDLLoader, HDF5BlockDataset
loader = CDLLoader(
dataset=HDF5BlockDataset("data.h5"),
batch_size=64,
seed=42,
prefetch=True, # async block prefetching
prefetch_depth=2,
)
Block Datasets
| Adapter | Auto-infers block size from |
|---|---|
HDF5BlockDataset |
HDF5 chunk metadata |
DirectoryBlockDataset |
Subdirectory (shard) file counts |
NumpyBlockDataset |
User-specified (in-memory) |
Architecture
src/cdl/
├── __init__.py # Public API surface
├── shuffle.py # Core: SplitMix64, FeistelPermutation, CDLShuffle
├── sampler.py # PyTorch Sampler integration
├── loader.py # Full I/O-optimised loader
├── datasets.py # Block-aware dataset adapters
├── prefetch.py # Async block prefetcher + LRU cache
└── ext/ # Optional C++ native extension
└── __init__.py
Algorithm
Input index X
├── Split into (Left, Right) halves
├── For r = 0 to 5:
│ ├── F = SplitMix64.hash(RoundKey[r], Right) & half_mask
│ ├── new_LEFT = Right
│ └── new_RIGHT = (Left ⊕ F) & half_mask
├── Combine: result = (Left << half_bits) | Right
└── Cycle walk: if result >= N, re-apply
When CDL Helps Most
| Scenario | Impact |
|---|---|
| GPU + HDD storage | ⭐⭐⭐⭐⭐ Massive speedup |
| GPU + Network (S3, NFS) | ⭐⭐⭐⭐⭐ Massive speedup |
| Memory-constrained (>100M samples) | ⭐⭐⭐⭐⭐ Essential |
| Distributed training | ⭐⭐⭐⭐ Stateless checkpointing |
| GPU + NVMe SSD | ⭐⭐ Moderate benefit |
Citation
@thesis{cdl2026,
title = {CDL: A Block-Based Constant-Memory Shuffling System
for I/O-Efficient Deep Learning Data Pipelines},
author = {Your Name},
year = {2026},
school = {Your University},
}
License
MIT — see LICENSE.
References
- Steele, G. L., Lea, D., & Flood, C. H. (2014). Fast splittable pseudorandom number generators. OOPSLA 2014.
- Black, J., & Rogaway, P. (2002). Ciphers with arbitrary finite domains. CT-RSA 2002.
- Morris, B., Rogaway, P., & Stegers, T. (2009). How to encipher messages on a small domain. CRYPTO 2009.
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