Performance Guide
This document is under development. The following sections are planned:
Table of Contents
- Memory Requirements
- CPU Optimization
- Parameter Tuning
- Scaling Strategies
- Benchmarking Your Data
- Hardware Recommendations
Memory Requirements
By Dataset Size
TODO: Add memory usage tables for different genome sizes
- Small genomes (< 10 Mb):
- Bacterial genomes (1-10 Mb):
- Large genomes (> 100 Mb):
Memory Optimization
TODO: Add memory optimization strategies
- Using
memory_limitparameters - Streaming vs. in-memory processing
- Graph sparsity optimization
CPU Optimization
Parallel Processing
TODO: Add parallelization guide
- Setting
JULIA_NUM_THREADS - Multi-core assembly strategies
- Distributed computing options
Algorithm Selection
TODO: Add algorithm comparison
- Trade-offs between accuracy and speed
- When to use different assembly methods
- Parameter selection for performance
Parameter Tuning
Assembly Parameters
TODO: Add parameter tuning guide
- K-mer size selection
- Memory limits
- Quality thresholds
Quality vs Speed Trade-offs
TODO: Add trade-off analysis
Scaling Strategies
Large Datasets
TODO: Add scaling strategies
- Chunked processing
- Hierarchical assembly
- Cloud computing integration
HPC Integration
TODO: Add HPC guidance
- SLURM job scripts
- Memory allocation strategies
- Parallel file systems
Benchmarking Your Data
Performance Metrics
TODO: Add benchmarking guide
- Runtime estimation
- Memory profiling
- Quality assessment
Comparison Tools
TODO: Add benchmark comparison
- Against other assemblers
- Quality metrics
- Resource usage
Hardware Recommendations
Recommended Specifications
TODO: Add hardware recommendations
- CPU requirements
- Memory recommendations
- Storage considerations
- Network requirements for HPC
This guide will be expanded with specific recommendations, benchmarks, and optimization strategies. For current performance information, see the benchmarking documentation.