CFM64 — Celeres-Feistel Mix 64
Cryptography-Based Index Permutation with Block-Sequential Access for Textual Data Loading Optimization and Its Impact on Text Classification Accuracy
Undergraduate thesis · Anthony L. Celeres · Visayas State University (2026)
The Problem
Training large models requires shuffling billions of samples every epoch. Traditional shuffling creates two scaling bottlenecks:
| Fisher-Yates Shuffle | CFM64 | |
|---|---|---|
| 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
CFM64 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 shuffled both across blocks and within each block every epoch. This is block-level shuffling, not a global permutation — see Scope & Status.
Quick Start
Install
pip install cfm64
Basic Usage
from cfm64 import CFM64Shuffle
shuffler = CFM64Shuffle(dataset_size=100_000, block_size=1024, seed=42)
for epoch in range(100):
shuffler.set_epoch(epoch) # O(1) — 24-byte state change
for block_id, offset, global_idx in shuffler:
sample = dataset[global_idx]
Full I/O-Optimised Loader
from cfm64 import CFM64Loader, TextBlockDataset
dataset = TextBlockDataset("train.csv", has_header=True)
loader = CFM64Loader(dataset, batch_size=64, seed=42)
for epoch in range(100):
loader.set_epoch(epoch)
for batch in loader:
train(batch)
Tuning the block size to your storage
The block size is the unit of both transfer and shuffle, and its optimum depends
on the storage device. It defaults to BLOCK_SIZE_BYTES (7 MB), but you can
override it per dataset. Measure the right value once per machine with the
companion auto-fio tool and pass
it in — no runtime coupling, fully reproducible:
import auto_fio
from cfm64 import TextBlockDataset
bs = auto_fio.optimal_block_size("/data/train") # bytes, measured once
dataset = TextBlockDataset("/data/train.csv", block_size_bytes=bs)
Scope & Status
CFM64 is an active undergraduate thesis; this repository is the implementation. Experimental results (throughput, accuracy, memory) are produced separately and are not included in this repository.
What CFM64 targets: text data loading on a single machine with NVMe/SSD
storage — the only modality this version ships (TextBlockDataset). Image,
network (S3/NFS), distributed, and billion-scale scenarios are design goals
reserved for future versions, not part of the current release or evaluation.
Shuffle scope. CFM64 performs block-level shuffling: Level-1 randomises block visit order and Level-2 shuffles within each ~7 MB block. Most batches therefore draw from a single block, so per-batch diversity is lower than a global Fisher–Yates shuffle. This is sound when the on-disk data is not ordered by label; a dataset sorted by class should be shuffled once on disk first.
Architecture
src/cfm64/
├── __init__.py # Public API surface
├── shuffle.py # Core: SplitMix64, FeistelPermutation, CFM64Shuffle
├── loader.py # Full I/O-optimised loader
├── datasets.py # Block-aware dataset adapters
└── ext/ # C++ native extension (pybind11)
├── __init__.py
├── bindings.cpp
└── include/cfm64/
├── cfm64.hpp # Umbrella header
├── splitmix64.hpp # SplitMix64 PRNG
├── feistel.hpp # Feistel network permutation
└── shuffle.hpp # Two-level block shuffler
Algorithm
Input index X
├── Split into (Left, Right) halves
├── For r = 0 to 3: (4 Feistel rounds)
│ ├── 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
Citation
@thesis{cfm642026,
title = {Cryptography-Based Index Permutation with Block-Sequential
Access for Textual Data Loading Optimization and Its Impact
on Text Classification Accuracy},
author = {Celeres, Anthony L.},
year = {2026},
school = {Visayas State 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.
1 maintainer
Metadata
1 maintainer
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