SLAF vs h5ad Performance Benchmarks¶

This document provides a comprehensive performance comparison between SLAF and the traditional h5ad (AnnData) format across bioinformatics and machine learning workflows.

Overview¶

SLAF provides dramatic performance improvements over h5ad across all benchmark categories, demonstrating the advantages of modern columnar storage and optimized data access patterns.

Key Performance Summary¶

Bioinformatics Benchmarks¶

Input Dataset: synthetic_50k_processed (49,955 cells × 25,000 genes, 722MB h5ad file)

Cell Filtering Performance¶

Cell filtering operations are fundamental to single-cell analysis workflows, used for quality control, cell type selection, and data subsetting.

Average Performance:

• SLAF vs h5ad: 92.3x faster
• Memory Usage: SLAF uses 115.7x less memory than h5ad

Gene Filtering Performance¶

Gene filtering operations are essential for feature selection, quality control, and dimensionality reduction.

Average Performance:

• SLAF vs h5ad: 17.3x faster
• Memory Usage: SLAF uses 2.2x less memory than h5ad

Expression Queries Performance¶

Expression queries retrieve specific expression data for cells or genes, supporting targeted analysis workflows.

Average Performance:

• SLAF vs h5ad: 9.5x faster
• Memory Usage: SLAF uses 154.6x less memory than h5ad

Machine Learning Benchmarks¶

Input Dataset: Tahoe-100M (5,481,420 cells × 62,710 genes, ~8B non-zero values)

Raw Data Loading Performance¶

Raw data loading measures the base throughput for machine learning workflows without tokenization overhead.

Performance Comparison:

• SLAF vs AnnDataLoader: 58.3x faster
• SLAF vs AnnLoader: 102.9x faster
• Memory Efficiency: SLAF uses 2.3x less memory

GPU-Ready Output Performance¶

SLAF provides pre-tokenized sequences ready for GPU training, while h5ad-based loaders only provide raw data.

Technical Implementation Comparison¶

Use Case Recommendations¶

Choose SLAF for:¶

• High-throughput bioinformatics workflows requiring fast filtering and querying
• Machine learning training on large single-cell datasets
• Cloud-based analysis requiring scalable, multi-user access
• Foundation model training requiring GPU-ready tokenized sequences
• Memory-constrained environments where efficiency is critical

Consider h5ad for:¶

• Legacy workflows that cannot be easily migrated
• Small-scale analysis where performance differences are negligible
• Educational purposes where traditional formats are more familiar
• Tool compatibility with systems that only support h5ad

Conclusion¶

The benchmarks demonstrate that SLAF's modern architecture, optimized data access patterns, and streaming capabilities provide massive advantages for both bioinformatics and machine learning workflows. For users looking to improve performance and scalability, migrating from h5ad to SLAF offers dramatic benefits with minimal workflow changes.

For detailed migration guidance, see Migrating to SLAF. For comprehensive benchmark results, see Bioinformatics Benchmarks and ML Benchmarks.