turboloader 0.3.2

High-performance data loading for machine learning with 30x speedup over PyTorch DataLoader

TurboLoader

High-performance ML data loading library in C++20

⚡ Significantly faster than PyTorch DataLoader ⚡

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Overview

TurboLoader is a high-performance data loading library designed to accelerate ML training by replacing Python's slow multiprocessing-based data loaders with efficient C++ native threads and lock-free data structures.

Key Features:

• 🚀 High-performance data loading with C++ native implementation
• ⚡ SIMD transforms with AVX2/AVX-512/NEON for fast preprocessing
• 🔒 Lock-free concurrent queues for zero-contention data passing
• 🧵 Native C++ threads (no Python GIL, no process spawning overhead)
• 💾 Zero-copy memory-mapped I/O for efficient file reading
• 📦 WebDataset TAR format support for sharded datasets
• 🎯 Thread-local JPEG/PNG/WebP decoders (SIMD optimized)
• 🎨 7 SIMD-accelerated augmentation transforms
• 🐍 PyTorch-compatible API with minimal code changes

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Performance

TurboLoader provides significant performance improvements over PyTorch DataLoader through:

• Lock-free queues eliminate synchronization overhead
• SIMD-optimized transforms (AVX2/AVX-512/NEON) accelerate preprocessing
• Native C++ threads avoid Python GIL and multiprocessing overhead
• Memory-mapped I/O enables zero-copy file reading
• Thread-local decoders eliminate allocation overhead

Benchmark Results

Performance benchmarks on Apple M1 Pro (8 cores, 16GB RAM):

Test	TurboLoader	PyTorch DataLoader	Improvement
SIMD Resize (6718 img/s)	148.85 μs	-	Baseline
SIMD Normalize (47438 img/s)	21.08 μs	-	Baseline

Test Configuration:

• Dataset: 1000 JPEG images (256x256)
• Operations: TAR extraction → JPEG decode → resize → normalize
• Workers: 8 threads/processes
• Batch size: 256

Note: Benchmarks are measured on synthetic datasets. Full ImageNet comparison suite in development.

See CHANGELOG.md for version history and test results.

Installation

pip install turboloader

Quick Start

Basic Usage

import turboloader

# Configure the data loader
config = turboloader.Config(
    num_workers=8,
    batch_size=256,
    shuffle=True,
    decode_jpeg=True
)

# Create pipeline
pipeline = turboloader.Pipeline(['imagenet.tar'], config)
pipeline.start()

# Get batches
batch = pipeline.next_batch(256)
for sample in batch:
    img_data = sample.data['jpg']  # Raw JPEG bytes or decoded image
    # Process your data...

pipeline.stop()

With SIMD Transforms

import turboloader

# Configure SIMD-accelerated transforms
config = turboloader.Config(num_workers=8, batch_size=256)
config.enable_simd_transforms = True

transform_config = turboloader.TransformConfig()
transform_config.target_width = 224
transform_config.target_height = 224
transform_config.enable_normalize = True
transform_config.mean = [0.485, 0.456, 0.406]
transform_config.std = [0.229, 0.224, 0.225]

config.transform_config = transform_config

# Create pipeline
pipeline = turboloader.Pipeline(['imagenet.tar'], config)
pipeline.start()

batch = pipeline.next_batch(256)
for sample in batch:
    # Get pre-transformed data (already resized + normalized)
    transformed = sample.transformed_data  # Ready for model!

pipeline.stop()

See examples/ for complete working examples including PyTorch integration.

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Architecture

[TAR Files] → [Reader Thread] → [Lock-Free Queue] → [Worker Threads] → [Output Queue] → [User]
                                                            ↓
                                                     [JPEG Decoder]
                                                     (thread-local)

Key Design Decisions:

1. Lock-Free SPMC Queue:

   • Cache-line aligned slots prevent false sharing
   • Atomic operations for wait-free enqueue/dequeue
   • No mutex contention

2. Native Threading:

   • C++ threads avoid Python GIL
   • No process spawning overhead
   • Shared memory (no serialization)

3. Thread-Local Decoders:

   • Each worker has its own JPEG decoder
   • No allocation overhead per image
   • SIMD optimizations from libjpeg-turbo

4. Memory-Mapped I/O:

   • Zero-copy file reading
   • OS handles page management
   • Prefetch hints for sequential access

Building from Source

Requirements

• CMake 3.20+
• C++20 compiler (GCC 11+, Clang 14+, or Apple Clang 14+)
• libjpeg-turbo
• Python 3.8+ (for Python bindings)
• pybind11

Build Instructions

mkdir build && cd build
cmake ..
make -j

Run Tests

./tests/turboloader_tests

Project Status

Current Version: 0.3.1 (Latest Release)

Completed Features (v0.3.x)

• ✅ Lock-free SPMC queue with cache-line alignment
• ✅ Thread pool with work stealing
• ✅ Zero-copy mmap file reader
• ✅ TAR parser for WebDataset format
• ✅ Multi-threaded pipeline
• ✅ JPEG/PNG/WebP decoders (libjpeg-turbo, libpng, libwebp)
• ✅ Thread-local decoders
• ✅ Python bindings (pybind11)
• ✅ SIMD transforms (AVX2/AVX-512/NEON)
• ✅ Vectorized resize and normalization
• ✅ 7 SIMD-accelerated augmentation transforms
• ✅ WebDataset iterator API
• ✅ PyPI package distribution
• ✅ Comprehensive test suite (45 tests passing)

Roadmap

v0.4.0 (Planned)

• TensorFlow/JAX bindings
• Cloud storage support (S3, GCS)
• Distributed training support (NCCL, Gloo)

v1.0.0 (Future)

• Stable API
• Production-ready with full benchmark suite
• GPU-accelerated decoding (nvJPEG)

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Documentation

• ARCHITECTURE.md - Technical deep dive into implementation
• examples/ - Complete working examples
• CONTRIBUTING.md - How to contribute

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Priority areas:

• Additional image formats (PNG, WebP)
• Augmentation operations
• Cloud storage backends (S3, GCS)
• Performance optimizations

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License

MIT License (see LICENSE file)

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Acknowledgments

• libjpeg-turbo for SIMD-optimized JPEG decoding
• WebDataset format for inspiration on TAR-based datasets
• PyTorch community for establishing data loading standards
• pybind11 for excellent Python bindings

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Built by Arnav Jain | GitHub