Expand description
Automatic Backend Optimization System with SciRS2 Intelligence
This module provides intelligent automatic backend selection and optimization based on problem characteristics, available hardware, and performance requirements. Uses SciRS2’s analysis capabilities to make optimal backend choices.
Structs§
- Auto
Optimizer - Automatic optimizer for backend selection and configuration
- Auto
Optimizer Config - Configuration for the AutoOptimizer
- Backend
Configuration - Backend configuration parameters
- Backend
Selection - Backend selection result
- Complexity
Estimate - Computational complexity estimate
- Distributed
Configuration - Distributed configuration
- Entanglement
Analysis - Entanglement analysis
- GPUConfiguration
- GPU configuration
- Gate
Composition - Gate composition analysis
- Optimization
Recommendation - Optimization recommendations
- Parallelization
Potential - Parallelization potential analysis
- Performance
Metrics - Performance metrics
- Performance
Profile - Backend performance profile
- Precision
Settings - Precision settings
- Problem
Analysis - Problem characteristics analysis
- Problem
Size Limits - Problem size limits for each backend
- Resource
Cost - Resource cost for implementing recommendations
- Resource
Monitor - Resource monitoring system
- Resource
Requirements - Resource requirements
- Resource
Utilization - Resource utilization
Enums§
- Backend
Type - Supported backend types
- Communication
Backend - Communication backends for distributed computing
- Complexity
Class - Complexity classes
- Float
Precision - Floating-point precision levels
- GPUMemory
Strategy - GPU memory allocation strategies
- Load
Balancing Strategy - Load balancing strategies
- Memory
Pattern - Memory access patterns
- Memory
Strategy - Memory allocation strategies
- Problem
Type - Problem type classifications
- Recommendation
Type - Types of optimization recommendations