wifi-densepose/vendor/sublinear-time-solver/crates/psycho-symbolic-reasoner/docs/PERFORMANCE_GUIDE.md

Psycho-Symbolic Reasoner Performance Guide

Overview

This document provides comprehensive guidance on performance characteristics, optimization strategies, and scaling recommendations for the Psycho-Symbolic Reasoner system.

Performance Characteristics

Component Performance Profiles

Graph Reasoner

• Complexity: O(V + E) for basic queries, O(V²) for complex inference
• Memory Usage: ~50MB per 10K facts with indexed structures
• Throughput: 1000-5000 queries/second depending on complexity
• Bottlenecks: Large graph traversals, complex rule inference

Text Extractor

• Complexity: O(n) where n is text length
• Memory Usage: ~2MB per 1MB of text processed
• Throughput: 100-500 documents/second for typical text sizes
• Bottlenecks: Regex processing, Unicode normalization

Planner

• Complexity: O(b^d) where b is branching factor, d is solution depth
• Memory Usage: ~10MB per 100 states in search space
• Throughput: 10-100 plans/second depending on problem complexity
• Bottlenecks: State space explosion, heuristic computation

WASM vs Native Performance

Component	Native (relative)	WASM (relative)	Overhead
Graph Reasoning	1.0x	1.8x	80%
Text Processing	1.0x	1.3x	30%
Planning	1.0x	2.2x	120%
Memory Allocation	1.0x	3.0x	200%
Serialization	1.0x	1.6x	60%

Optimization Strategies

1. Memory Management

Object Pooling

use std::collections::VecDeque;

pub struct ObjectPool<T> {
    objects: VecDeque<T>,
    create_fn: Box<dyn Fn() -> T>,
}

impl<T> ObjectPool<T> {
    pub fn new<F>(capacity: usize, create_fn: F) -> Self
    where F: Fn() -> T + 'static {
        let mut pool = ObjectPool {
            objects: VecDeque::with_capacity(capacity),
            create_fn: Box::new(create_fn),
        };

        // Pre-populate pool
        for _ in 0..capacity {
            pool.objects.push_back((pool.create_fn)());
        }

        pool
    }

    pub fn acquire(&mut self) -> T {
        self.objects.pop_front().unwrap_or_else(|| (self.create_fn)())
    }

    pub fn release(&mut self, obj: T) {
        if self.objects.len() < self.objects.capacity() {
            self.objects.push_back(obj);
        }
    }
}

Arena Allocation

use typed_arena::Arena;

pub struct GraphArena {
    fact_arena: Arena<Fact>,
    rule_arena: Arena<Rule>,
}

impl GraphArena {
    pub fn new() -> Self {
        Self {
            fact_arena: Arena::new(),
            rule_arena: Arena::new(),
        }
    }

    pub fn alloc_fact(&self, fact: Fact) -> &Fact {
        self.fact_arena.alloc(fact)
    }

    pub fn alloc_rule(&self, rule: Rule) -> &Rule {
        self.rule_arena.alloc(rule)
    }
}

2. Algorithm Optimizations

Parallel Graph Traversal

use rayon::prelude::*;

impl KnowledgeGraph {
    pub fn parallel_query(&self, query: &Query) -> QueryResult {
        let chunks: Vec<_> = self.facts
            .par_iter()
            .chunks(1000)
            .map(|chunk| {
                chunk.filter(|fact| query.matches(fact))
                     .collect::<Vec<_>>()
            })
            .collect();

        let mut results = QueryResult::new();
        for chunk_results in chunks {
            results.extend(chunk_results);
        }

        results
    }
}

Bloom Filter Optimization

use bloom::{BloomFilter, ASMS};

pub struct OptimizedGraph {
    facts: Vec<Fact>,
    entity_filter: BloomFilter,
    predicate_filter: BloomFilter,
}

impl OptimizedGraph {
    pub fn contains_entity(&self, entity: &str) -> bool {
        self.entity_filter.contains(&entity.as_bytes())
    }

    pub fn quick_query_check(&self, query: &Query) -> bool {
        // Fast negative check using bloom filter
        match &query.pattern {
            Pattern::Subject(entity) => self.contains_entity(entity),
            Pattern::Predicate(pred) => self.predicate_filter.contains(&pred.as_bytes()),
            _ => true, // Can't optimize complex patterns
        }
    }
}

Result Caching

use lru::LruCache;
use std::hash::Hash;

pub struct CachedInferenceEngine {
    engine: InferenceEngine,
    cache: LruCache<u64, InferenceResult>,
}

impl CachedInferenceEngine {
    pub fn infer_cached(&mut self, graph: &KnowledgeGraph, rules: &RuleEngine) -> InferenceResult {
        let cache_key = self.compute_cache_key(graph, rules);

        if let Some(cached) = self.cache.get(&cache_key) {
            return cached.clone();
        }

        let result = self.engine.infer(graph, rules, 10);
        self.cache.put(cache_key, result.clone());

        result
    }

    fn compute_cache_key(&self, graph: &KnowledgeGraph, rules: &RuleEngine) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::Hasher;

        let mut hasher = DefaultHasher::new();
        graph.hash(&mut hasher);
        rules.hash(&mut hasher);
        hasher.finish()
    }
}

3. WASM Optimizations

SIMD Usage

#[cfg(target_arch = "wasm32")]
use std::arch::wasm32::*;

#[cfg(target_arch = "wasm32")]
pub fn simd_sentiment_analysis(text: &[u8]) -> f32 {
    if text.len() >= 16 {
        // Use SIMD for bulk processing
        let chunks = text.chunks_exact(16);
        let remainder = chunks.remainder();

        let mut result = v128_const(0, 0, 0, 0);

        for chunk in chunks {
            let data = v128_load(chunk.as_ptr() as *const v128);
            result = i8x16_add(result, data);
        }

        // Process remainder normally
        let simd_result = i8x16_extract_lane::<0>(result) as f32;
        let remainder_result = remainder.iter().sum::<u8>() as f32;

        (simd_result + remainder_result) / text.len() as f32
    } else {
        // Fallback to scalar processing
        text.iter().sum::<u8>() as f32 / text.len() as f32
    }
}

Memory Management in WASM

#[cfg(target_arch = "wasm32")]
use wee_alloc;

#[cfg(target_arch = "wasm32")]
#[global_allocator]
static ALLOC: wee_alloc::WeeAlloc = wee_alloc::WeeAlloc::INIT;

// Minimize allocations in hot paths
pub struct WasmOptimizedProcessor {
    buffer: Vec<u8>,
    temp_storage: Vec<String>,
}

impl WasmOptimizedProcessor {
    pub fn process_text(&mut self, text: &str) -> Result<AnalysisResult, Error> {
        // Reuse existing buffers
        self.buffer.clear();
        self.temp_storage.clear();

        // Process without intermediate allocations
        for word in text.split_whitespace() {
            self.buffer.extend_from_slice(word.as_bytes());
            // Process in-place
        }

        Ok(AnalysisResult::from_buffer(&self.buffer))
    }
}

4. I/O Optimization

Batch MCP Processing

pub struct BatchedMCPClient {
    pending_requests: Vec<MCPRequest>,
    batch_size: usize,
    flush_interval: Duration,
    last_flush: Instant,
}

impl BatchedMCPClient {
    pub async fn send_request(&mut self, request: MCPRequest) -> Result<MCPResponse, Error> {
        self.pending_requests.push(request);

        if self.should_flush() {
            self.flush_batch().await
        } else {
            // Return immediately for batched processing
            Ok(MCPResponse::Batched)
        }
    }

    fn should_flush(&self) -> bool {
        self.pending_requests.len() >= self.batch_size ||
        self.last_flush.elapsed() > self.flush_interval
    }

    async fn flush_batch(&mut self) -> Result<MCPResponse, Error> {
        if self.pending_requests.is_empty() {
            return Ok(MCPResponse::Empty);
        }

        let batch = BatchMCPRequest {
            requests: std::mem::take(&mut self.pending_requests),
        };

        let response = self.send_batch(batch).await?;
        self.last_flush = Instant::now();

        Ok(response)
    }
}

Connection Pooling

use deadpool::managed::{Manager, Pool};

pub struct MCPConnectionManager {
    endpoint: String,
}

impl Manager for MCPConnectionManager {
    type Type = MCPConnection;
    type Error = MCPError;

    async fn create(&self) -> Result<MCPConnection, MCPError> {
        MCPConnection::connect(&self.endpoint).await
    }

    async fn recycle(&self, conn: &mut MCPConnection) -> Result<(), MCPError> {
        conn.ping().await?;
        Ok(())
    }
}

pub struct OptimizedMCPService {
    pool: Pool<MCPConnectionManager>,
}

impl OptimizedMCPService {
    pub async fn execute_tool(&self, tool: &str, params: &Value) -> Result<Value, Error> {
        let conn = self.pool.get().await?;
        let result = conn.execute_tool(tool, params).await?;
        // Connection automatically returned to pool
        Ok(result)
    }
}

Scaling Strategies

1. Horizontal Scaling

Component Distribution

pub enum ComponentLocation {
    Local,
    Remote { endpoint: String },
    Distributed { nodes: Vec<String> },
}

pub struct DistributedReasoner {
    graph_location: ComponentLocation,
    text_location: ComponentLocation,
    planner_location: ComponentLocation,
}

impl DistributedReasoner {
    pub async fn query_graph(&self, query: Query) -> Result<QueryResult, Error> {
        match &self.graph_location {
            ComponentLocation::Local => self.local_graph.query(&query),
            ComponentLocation::Remote { endpoint } => {
                self.remote_query(endpoint, query).await
            },
            ComponentLocation::Distributed { nodes } => {
                self.distributed_query(nodes, query).await
            },
        }
    }

    async fn distributed_query(&self, nodes: &[String], query: Query) -> Result<QueryResult, Error> {
        let futures = nodes.iter().map(|node| {
            self.remote_query(node, query.clone())
        });

        let results = futures::future::try_join_all(futures).await?;

        // Merge results
        let mut merged = QueryResult::new();
        for result in results {
            merged.merge(result);
        }

        Ok(merged)
    }
}

Load Balancing

use std::sync::atomic::{AtomicUsize, Ordering};

pub struct LoadBalancer {
    nodes: Vec<String>,
    current: AtomicUsize,
    strategy: LoadBalancingStrategy,
}

pub enum LoadBalancingStrategy {
    RoundRobin,
    LeastConnections,
    WeightedRandom,
    ConsistentHashing,
}

impl LoadBalancer {
    pub fn select_node(&self, request_key: Option<&str>) -> &str {
        match self.strategy {
            LoadBalancingStrategy::RoundRobin => {
                let index = self.current.fetch_add(1, Ordering::Relaxed) % self.nodes.len();
                &self.nodes[index]
            },
            LoadBalancingStrategy::ConsistentHashing => {
                if let Some(key) = request_key {
                    let hash = self.hash_key(key);
                    let index = hash % self.nodes.len();
                    &self.nodes[index]
                } else {
                    &self.nodes[0] // Fallback
                }
            },
            _ => &self.nodes[0], // Simplified for example
        }
    }
}

2. Vertical Scaling

Resource Management

pub struct ResourceManager {
    cpu_quota: f64,
    memory_limit: usize,
    current_cpu: AtomicU64,
    current_memory: AtomicUsize,
}

impl ResourceManager {
    pub fn check_resources(&self) -> ResourceStatus {
        let cpu_usage = self.current_cpu.load(Ordering::Relaxed) as f64 / 1000.0;
        let memory_usage = self.current_memory.load(Ordering::Relaxed);

        ResourceStatus {
            cpu_available: cpu_usage < self.cpu_quota * 0.8,
            memory_available: memory_usage < self.memory_limit * 80 / 100,
            throttle_recommended: cpu_usage > self.cpu_quota * 0.9,
        }
    }

    pub async fn execute_with_throttling<F, R>(&self, operation: F) -> Result<R, Error>
    where
        F: FnOnce() -> R,
    {
        let status = self.check_resources();

        if !status.cpu_available || !status.memory_available {
            return Err(Error::ResourceExhausted);
        }

        if status.throttle_recommended {
            tokio::time::sleep(Duration::from_millis(10)).await;
        }

        Ok(operation())
    }
}

Adaptive Concurrency

pub struct AdaptiveConcurrencyManager {
    current_concurrency: AtomicUsize,
    max_concurrency: usize,
    success_rate: AtomicU64,
    error_rate: AtomicU64,
}

impl AdaptiveConcurrencyManager {
    pub fn adjust_concurrency(&self) {
        let success = self.success_rate.load(Ordering::Relaxed);
        let errors = self.error_rate.load(Ordering::Relaxed);
        let total = success + errors;

        if total > 100 { // Minimum sample size
            let error_ratio = errors as f64 / total as f64;
            let current = self.current_concurrency.load(Ordering::Relaxed);

            let new_concurrency = if error_ratio > 0.05 {
                // High error rate, reduce concurrency
                (current * 80 / 100).max(1)
            } else if error_ratio < 0.01 && current < self.max_concurrency {
                // Low error rate, increase concurrency
                (current * 110 / 100).min(self.max_concurrency)
            } else {
                current
            };

            self.current_concurrency.store(new_concurrency, Ordering::Relaxed);

            // Reset counters
            self.success_rate.store(0, Ordering::Relaxed);
            self.error_rate.store(0, Ordering::Relaxed);
        }
    }

    pub async fn execute_with_limit<F, R>(&self, operation: F) -> Result<R, Error>
    where
        F: Future<Output = Result<R, Error>>,
    {
        let _permit = self.acquire_permit().await?;

        match operation.await {
            Ok(result) => {
                self.success_rate.fetch_add(1, Ordering::Relaxed);
                Ok(result)
            },
            Err(error) => {
                self.error_rate.fetch_add(1, Ordering::Relaxed);
                Err(error)
            }
        }
    }
}

Performance Monitoring

Key Metrics to Track

1. Throughput Metrics

   • Queries per second
   • Documents processed per second
   • Plans generated per second

2. Latency Metrics

   • P50, P95, P99 response times
   • End-to-end processing latency
   • Component-specific latencies

3. Resource Metrics

   • CPU utilization
   • Memory usage and growth
   • GC pressure (WASM)
   • Network I/O

4. Error Metrics

   • Error rates by component
   • Timeout frequencies
   • Retry rates

Monitoring Implementation

use crate::performance_monitor::{get_global_monitor, PerformanceThreshold};
use crate::bottleneck_analyzer::BottleneckAnalyzer;

pub struct PerformanceMonitoringService {
    analyzer: BottleneckAnalyzer,
    alert_thresholds: HashMap<String, PerformanceThreshold>,
}

impl PerformanceMonitoringService {
    pub fn setup_monitoring(&mut self) {
        // Set up thresholds for each component
        let graph_threshold = PerformanceThreshold {
            max_duration: Some(Duration::from_millis(50)),
            max_memory: Some(100 * 1024 * 1024), // 100MB
            min_throughput: Some(1000.0), // 1000 queries/sec
        };

        let monitor = get_global_monitor();
        let mut monitor = monitor.lock().unwrap();
        monitor.set_threshold("graph_reasoner", graph_threshold);

        // Set up automated analysis
        self.schedule_periodic_analysis();
    }

    fn schedule_periodic_analysis(&self) {
        tokio::spawn(async move {
            let mut interval = tokio::time::interval(Duration::from_secs(60));

            loop {
                interval.tick().await;

                let reports = self.analyzer.analyze_all_components();

                for report in reports {
                    if matches!(report.severity, Severity::Critical | Severity::High) {
                        self.send_alert(report).await;
                    }
                }
            }
        });
    }

    async fn send_alert(&self, report: BottleneckReport) {
        // Send alerts via configured channels (email, Slack, etc.)
        eprintln!("PERFORMANCE ALERT: {}", report.description);
    }
}

Troubleshooting Guide

Common Performance Issues

1. High Memory Usage

Symptoms: Increasing memory consumption, OOM errors Diagnosis:

# Run memory profiling benchmark
cargo bench memory_usage

# Check for memory leaks in long-running processes
cargo bench --bench memory_usage -- --test long_running_process_memory

Solutions:

• Implement object pooling
• Use arena allocation for temporary objects
• Profile with heaptrack or valgrind

2. Slow Query Performance

Symptoms: High query latency, timeouts Diagnosis:

# Profile query performance
cargo bench --bench graph_reasoning -- --profile-time=60

# Check for algorithmic complexity issues
cargo bench --bench graph_reasoning -- bench_graph_operations_complexity

Solutions:

• Add appropriate indexes
• Implement query optimization
• Use bloom filters for negative lookups

3. WASM Performance Issues

Symptoms: Significantly slower than native performance Diagnosis:

# Compare WASM vs native performance
cargo bench --bench wasm_vs_native

Solutions:

• Enable SIMD optimizations
• Minimize memory allocations
• Use efficient serialization formats

4. Concurrency Bottlenecks

Symptoms: Poor scaling with multiple threads Diagnosis:

# Test concurrent performance
cargo bench --bench graph_reasoning -- bench_concurrent_operations

Solutions:

• Identify lock contention
• Use lock-free data structures
• Implement work-stealing algorithms

Performance Testing Commands

# Run full benchmark suite
./scripts/run_benchmarks.sh

# Run specific component benchmarks
cargo bench --bench graph_reasoning
cargo bench --bench text_extraction
cargo bench --bench planning_algorithms

# Profile with flamegraph
cargo install flamegraph
sudo cargo flamegraph --bench graph_reasoning

# Memory profiling
cargo install heaptrack
heaptrack cargo bench --bench memory_usage

# Generate performance report
cargo bench --bench regression_tests -- --save-baseline baseline_v1

Best Practices

1. Development Practices

• Always benchmark before and after optimizations
• Use criterion.rs for reliable performance measurements
• Set up continuous performance monitoring
• Profile regularly to identify hot spots

2. Production Deployment

• Configure appropriate resource limits
• Set up monitoring and alerting
• Use load balancing for high availability
• Implement graceful degradation

3. Performance Testing

• Test with realistic data volumes
• Include both synthetic and real-world workloads
• Test edge cases and error conditions
• Validate performance under load

Conclusion

The Psycho-Symbolic Reasoner is designed for high performance across various deployment scenarios. By following the optimization strategies and scaling recommendations in this guide, you can achieve optimal performance for your specific use case.

Regular monitoring and profiling are essential for maintaining performance as the system evolves. Use the provided benchmarking tools and monitoring infrastructure to identify and address performance issues proactively.

For additional support or performance questions, refer to the benchmark results and optimization recommendations generated by the automated analysis tools.