use crate::advisor::config::*;
use crate::advisor::utils::*;
use crate::JitResult;
use std::time::Duration;
pub struct RecommendationEngine {
config: AdvisorConfig,
}
impl RecommendationEngine {
pub fn new(config: AdvisorConfig) -> Self {
Self { config }
}
pub fn generate_from_opportunity(
&self,
opportunity: &OptimizationOpportunity,
input: &AnalysisInput,
performance_analysis: &PerformanceAnalysis,
cost_analysis: &CostBenefitAnalysis,
) -> JitResult<Option<OptimizationRecommendation>> {
if opportunity.estimated_benefit < self.config.min_benefit_threshold {
return Ok(None);
}
let optimization_type = opportunity.opportunity_type.to_optimization_type();
let recommendation = OptimizationRecommendation {
id: generate_recommendation_id(),
optimization_type: optimization_type.clone(),
title: self.generate_title(&optimization_type),
description: opportunity.description.clone(),
expected_speedup: opportunity.estimated_benefit,
expected_memory_reduction: opportunity.estimated_benefit * 0.5,
implementation_complexity: opportunity.implementation_complexity,
confidence: self.calculate_recommendation_confidence(opportunity, performance_analysis),
risk_level: opportunity.implementation_complexity * 0.8,
priority_score: 0.0, affected_components: self.identify_affected_components(opportunity, input),
prerequisites: opportunity.prerequisites.clone(),
estimated_implementation_time: self.estimate_implementation_time(opportunity),
};
Ok(Some(recommendation))
}
pub fn generate_from_bottleneck(
&self,
bottleneck: &PerformanceBottleneck,
input: &AnalysisInput,
_pattern_analysis: &PatternAnalysis,
_cost_analysis: &CostBenefitAnalysis,
) -> JitResult<Option<OptimizationRecommendation>> {
let optimization_type = match bottleneck.bottleneck_type {
BottleneckType::Memory => OptimizationType::MemoryOptimization,
BottleneckType::Computation => OptimizationType::ComputationOptimization,
BottleneckType::IO => OptimizationType::IOOptimization,
BottleneckType::Synchronization => OptimizationType::ParallelizationOptimization,
};
let recommendation = OptimizationRecommendation {
id: generate_recommendation_id(),
optimization_type: optimization_type.clone(),
title: format!(
"Address {} Bottleneck",
bottleneck.bottleneck_type.description()
),
description: format!(
"Optimize {} bottleneck: {}",
bottleneck.bottleneck_type.description(),
bottleneck.description
),
expected_speedup: bottleneck.severity * 0.4,
expected_memory_reduction: if matches!(
bottleneck.bottleneck_type,
BottleneckType::Memory
) {
bottleneck.severity * 0.3
} else {
0.0
},
implementation_complexity: 0.5,
confidence: 0.8,
risk_level: 0.3,
priority_score: 0.0,
affected_components: vec![bottleneck.location.clone()],
prerequisites: self.identify_prerequisites(&optimization_type),
estimated_implementation_time: Duration::from_secs(20 * 3600),
};
Ok(Some(recommendation))
}
pub fn generate_from_antipattern(
&self,
antipattern: &DetectedAntipattern,
_input: &AnalysisInput,
_cost_analysis: &CostBenefitAnalysis,
) -> JitResult<Option<OptimizationRecommendation>> {
let optimization_type = match antipattern.antipattern_type {
AntipatternType::RedundantComputation => {
OptimizationType::DeadCodeEliminationOptimization
}
AntipatternType::PoorMemoryLocality => OptimizationType::MemoryOptimization,
AntipatternType::InefficientAlgorithm => OptimizationType::ComputationOptimization,
AntipatternType::ExcessiveAllocation => OptimizationType::MemoryOptimization,
};
let recommendation = OptimizationRecommendation {
id: generate_recommendation_id(),
optimization_type: optimization_type.clone(),
title: format!("Fix {}", antipattern.antipattern_type.description()),
description: format!("Address antipattern: {}", antipattern.description),
expected_speedup: antipattern.severity * 0.3,
expected_memory_reduction: if matches!(
antipattern.antipattern_type,
AntipatternType::ExcessiveAllocation
) {
antipattern.severity * 0.4
} else {
antipattern.severity * 0.1
},
implementation_complexity: 0.4,
confidence: 0.7,
risk_level: 0.2,
priority_score: 0.0,
affected_components: self.extract_components_from_location(&antipattern.location),
prerequisites: self.identify_prerequisites(&optimization_type),
estimated_implementation_time: Duration::from_secs(12 * 3600),
};
Ok(Some(recommendation))
}
pub fn generate_holistic_recommendations(
&self,
input: &AnalysisInput,
pattern_analysis: &PatternAnalysis,
performance_analysis: &PerformanceAnalysis,
_cost_analysis: &CostBenefitAnalysis,
) -> JitResult<Vec<OptimizationRecommendation>> {
let mut recommendations = Vec::new();
if self.should_recommend_compilation_optimization(input, performance_analysis) {
recommendations.push(self.create_compilation_optimization_recommendation()?);
}
if self.should_recommend_architecture_optimization(input, pattern_analysis) {
recommendations.push(self.create_architecture_optimization_recommendation()?);
}
if pattern_analysis.detected_patterns.len() > 5 {
recommendations.push(self.create_comprehensive_optimization_recommendation()?);
}
Ok(recommendations)
}
pub fn calculate_confidence(&self) -> f64 {
0.8
}
fn generate_title(&self, optimization_type: &OptimizationType) -> String {
match optimization_type {
OptimizationType::FusionOptimization => "Implement Operation Fusion".to_string(),
OptimizationType::MemoryOptimization => "Optimize Memory Access Patterns".to_string(),
OptimizationType::ParallelizationOptimization => "Enable Parallelization".to_string(),
OptimizationType::VectorizationOptimization => "Apply Vectorization".to_string(),
OptimizationType::ConstantFoldingOptimization => "Perform Constant Folding".to_string(),
OptimizationType::DeadCodeEliminationOptimization => "Eliminate Dead Code".to_string(),
OptimizationType::ComputationOptimization => "Optimize Computation".to_string(),
OptimizationType::IOOptimization => "Optimize I/O Operations".to_string(),
OptimizationType::CompilationOptimization => "Improve Compilation".to_string(),
OptimizationType::ArchitectureOptimization => "Optimize Architecture".to_string(),
}
}
fn calculate_recommendation_confidence(
&self,
opportunity: &OptimizationOpportunity,
_performance_analysis: &PerformanceAnalysis,
) -> f64 {
let base_confidence = 0.7;
let complexity_penalty = opportunity.implementation_complexity * 0.2;
let benefit_boost = opportunity.estimated_benefit * 0.2;
(base_confidence - complexity_penalty + benefit_boost)
.max(0.1)
.min(1.0)
}
fn identify_affected_components(
&self,
opportunity: &OptimizationOpportunity,
_input: &AnalysisInput,
) -> Vec<String> {
match &opportunity.location {
PatternLocation::Node(node_id) => vec![format!("Node_{:?}", node_id)],
PatternLocation::Nodes(node_ids) => {
node_ids.iter().map(|id| format!("Node_{:?}", id)).collect()
}
PatternLocation::Subgraph(subgraphs) => subgraphs
.iter()
.enumerate()
.map(|(i, _)| format!("Subgraph_{}", i))
.collect(),
PatternLocation::Global => vec!["Global".to_string()],
}
}
fn identify_prerequisites(&self, optimization_type: &OptimizationType) -> Vec<String> {
match optimization_type {
OptimizationType::FusionOptimization => vec![
"Data dependency analysis".to_string(),
"Memory access pattern analysis".to_string(),
],
OptimizationType::MemoryOptimization => {
vec!["Memory profiling".to_string(), "Cache analysis".to_string()]
}
OptimizationType::ParallelizationOptimization => vec![
"Thread safety analysis".to_string(),
"Load balancing analysis".to_string(),
],
OptimizationType::VectorizationOptimization => vec![
"SIMD capability detection".to_string(),
"Data alignment verification".to_string(),
],
OptimizationType::ConstantFoldingOptimization => {
vec!["Constant propagation analysis".to_string()]
}
OptimizationType::DeadCodeEliminationOptimization => vec![
"Live variable analysis".to_string(),
"Reachability analysis".to_string(),
],
_ => vec!["Performance profiling".to_string()],
}
}
fn estimate_implementation_time(&self, opportunity: &OptimizationOpportunity) -> Duration {
let base_hours = match opportunity.opportunity_type {
OpportunityType::ConstantFolding => 4,
OpportunityType::DeadCodeElimination => 6,
OpportunityType::FusionOptimization => 16,
OpportunityType::VectorizationOptimization => 20,
OpportunityType::MemoryOptimization => 24,
OpportunityType::ComputationOptimization => 32,
OpportunityType::ParallelizationOptimization => 40,
};
let complexity_multiplier = 1.0 + opportunity.implementation_complexity;
let total_hours = (base_hours as f64 * complexity_multiplier) as u64;
Duration::from_secs(total_hours * 3600)
}
fn extract_components_from_location(&self, location: &PatternLocation) -> Vec<String> {
match location {
PatternLocation::Node(node_id) => vec![format!("Node_{:?}", node_id)],
PatternLocation::Nodes(node_ids) => {
node_ids.iter().map(|id| format!("Node_{:?}", id)).collect()
}
PatternLocation::Subgraph(subgraphs) => subgraphs
.iter()
.enumerate()
.map(|(i, _)| format!("Subgraph_{}", i))
.collect(),
PatternLocation::Global => vec!["Global".to_string()],
}
}
fn should_recommend_compilation_optimization(
&self,
input: &AnalysisInput,
performance_analysis: &PerformanceAnalysis,
) -> bool {
performance_analysis.execution_profile.total_execution_time > Duration::from_secs(10)
&& input.system_constraints.target_platform == TargetPlatform::Desktop
}
fn should_recommend_architecture_optimization(
&self,
input: &AnalysisInput,
pattern_analysis: &PatternAnalysis,
) -> bool {
input
.computation_graph
.as_ref()
.map_or(false, |g| g.node_count() > 100)
&& pattern_analysis.detected_patterns.len() > 3
}
fn create_compilation_optimization_recommendation(
&self,
) -> JitResult<OptimizationRecommendation> {
Ok(OptimizationRecommendation {
id: generate_recommendation_id(),
optimization_type: OptimizationType::CompilationOptimization,
title: "Optimize Compilation Process".to_string(),
description: "Improve compilation speed and output quality".to_string(),
expected_speedup: 0.2,
expected_memory_reduction: 0.1,
implementation_complexity: 0.6,
confidence: 0.7,
risk_level: 0.3,
priority_score: 0.0,
affected_components: vec!["Compiler".to_string()],
prerequisites: vec!["Compilation profiling".to_string()],
estimated_implementation_time: Duration::from_secs(30 * 3600),
})
}
fn create_architecture_optimization_recommendation(
&self,
) -> JitResult<OptimizationRecommendation> {
Ok(OptimizationRecommendation {
id: generate_recommendation_id(),
optimization_type: OptimizationType::ArchitectureOptimization,
title: "Optimize System Architecture".to_string(),
description: "Improve overall system design for better performance".to_string(),
expected_speedup: 0.3,
expected_memory_reduction: 0.2,
implementation_complexity: 0.8,
confidence: 0.6,
risk_level: 0.5,
priority_score: 0.0,
affected_components: vec!["Architecture".to_string()],
prerequisites: vec!["Architecture analysis".to_string()],
estimated_implementation_time: Duration::from_secs(80 * 3600),
})
}
fn create_comprehensive_optimization_recommendation(
&self,
) -> JitResult<OptimizationRecommendation> {
Ok(OptimizationRecommendation {
id: generate_recommendation_id(),
optimization_type: OptimizationType::ComputationOptimization,
title: "Comprehensive Performance Optimization".to_string(),
description: "Apply multiple optimization techniques systematically".to_string(),
expected_speedup: 0.4,
expected_memory_reduction: 0.3,
implementation_complexity: 0.9,
confidence: 0.8,
risk_level: 0.4,
priority_score: 0.0,
affected_components: vec!["Entire System".to_string()],
prerequisites: vec!["Comprehensive analysis".to_string()],
estimated_implementation_time: Duration::from_secs(120 * 3600),
})
}
}