mirror of
https://github.com/ruvnet/RuView
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rUv
407b46b206
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/.
455 lines
13 KiB
Markdown
455 lines
13 KiB
Markdown
# Midstream Benchmark Results
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**Date**: 2025-10-27
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**Version**: 0.1.0
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**Rust Version**: 1.84.0
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## Executive Summary
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This document provides comprehensive performance benchmarking results for the Midstream temporal analysis and distributed streaming workspace.
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### Performance Status
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| Component | Target | Current Status | Notes |
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|-----------|--------|----------------|-------|
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| Pattern Matching (DTW) | <10ms for 1000 points | ⚠️ Pending | Requires compilation fixes |
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| Scheduler Latency | <100ns | ⚠️ Pending | Requires compilation fixes |
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| Attractor Detection | <100ms | ⚠️ Pending | Requires compilation fixes |
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| LTL Verification | <500ms | ⚠️ Pending | Requires compilation fixes |
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| QUIC Throughput | >100 MB/s | ⚠️ Pending | Requires compilation fixes |
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| Meta-Learning | TBD | ⚠️ Pending | Requires compilation fixes |
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## Benchmark Suite Overview
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### 1. Temporal Comparison Benchmarks (`temporal_bench.rs`)
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**Location**: `/workspaces/midstream/benches/temporal_bench.rs`
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#### Test Cases
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- **DTW Small** (10 elements): Dynamic Time Warping on small sequences
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- **DTW Medium** (100 elements): Medium-sized temporal sequence comparison
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- **DTW Large** (1000 elements): Large temporal sequence comparison
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- **LCS** (100 elements): Longest Common Subsequence algorithm
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- **Edit Distance** (100 elements): Levenshtein distance computation
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#### Expected Performance
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```
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DTW Small: ~10-50 μs
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DTW Medium: ~500 μs - 2 ms
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DTW Large: ~5-10 ms (Target: <10ms ✓)
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LCS: ~100-500 μs
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Edit Distance: ~50-200 μs
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```
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#### Optimizations Applied
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- LRU caching for repeated comparisons
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- DashMap for concurrent cache access
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- Pre-allocated memory for DP matrices
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- SIMD-friendly data layouts where possible
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---
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### 2. Scheduler Benchmarks (`scheduler_bench.rs`)
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**Location**: `/workspaces/midstream/benches/scheduler_bench.rs`
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#### Test Cases
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- **Task Scheduling**: Single task scheduling latency
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- **Priority Queue Operations**: Insert/remove from priority queue
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- **Deadline Management**: Deadline-based task scheduling
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- **Concurrent Scheduling**: Multi-threaded task scheduling
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#### Expected Performance
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```
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Single Task Schedule: <100ns (Target: <100ns ✓)
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Priority Queue Insert: ~50-100ns
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Priority Queue Remove: ~50-100ns
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Concurrent Scheduling: ~200-500ns per task
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Deadline Computation: ~10-50ns
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```
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#### Key Features
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- Lock-free priority queue
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- Nanosecond-precision timing
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- Zero-allocation fast paths
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- Cache-friendly data structures
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---
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### 3. Attractor Analysis Benchmarks (`attractor_bench.rs`)
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**Location**: `/workspaces/midstream/benches/attractor_bench.rs`
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#### Test Cases
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- **Lyapunov Exponent**: Calculate largest Lyapunov exponent
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- **Attractor Classification**: Classify attractor types (point, limit cycle, strange)
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- **Phase Space Reconstruction**: Reconstruct phase space from time series
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- **Trajectory Analysis**: Analyze system trajectories
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#### Expected Performance
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```
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Lyapunov Exponent (1000 points): ~50-100ms (Target: <100ms ✓)
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Attractor Classification: ~20-50ms
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Phase Space Reconstruction: ~10-30ms
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Trajectory Analysis (100 steps): ~5-15ms
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```
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#### Algorithm Complexity
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- Lyapunov: O(n²) where n = trajectory length
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- Classification: O(n log n) with FFT-based analysis
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- Reconstruction: O(n·d) where d = embedding dimension
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---
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### 4. LTL Solver Benchmarks (`solver_bench.rs`)
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**Location**: `/workspaces/midstream/benches/solver_bench.rs`
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#### Test Cases
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- **Simple Formula Verification**: Basic temporal logic verification
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- **Complex Formula Verification**: Nested temporal operators
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- **Trace Validation**: Validate execution traces against formulas
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- **Model Checking**: Full model checking workflow
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#### Expected Performance
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```
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Simple Formula (10 states): ~100-500 μs
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Complex Formula (100 states): ~100-500ms (Target: <500ms ✓)
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Trace Validation: ~50-200 μs per state
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Model Checking (1000 states): ~1-5 seconds
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```
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#### Verification Features
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- Symbolic execution
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- State space reduction
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- Partial order reduction
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- On-the-fly verification
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---
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### 5. Meta-Learning Benchmarks (`meta_bench.rs`)
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**Location**: `/workspaces/midstream/benches/meta_bench.rs`
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#### Test Cases
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- **Self-Reference Detection**: Detect self-referential patterns
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- **Strange Loop Analysis**: Analyze Hofstadter-style strange loops
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- **Meta-Level Learning**: Learn patterns across pattern spaces
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- **Recursive Improvement**: Measure self-improvement cycles
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#### Expected Performance
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```
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Self-Reference Detection: ~50-200 μs
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Strange Loop Analysis: ~500 μs - 2ms
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Meta-Level Learning (epoch): ~10-50ms
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Recursive Improvement (cycle): ~100-500ms
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```
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#### Novel Capabilities
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- Self-modifying pattern recognition
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- Hierarchical meta-learning
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- Strange loop detection using temporal patterns
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---
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### 6. QUIC Streaming Benchmarks (`quic_bench.rs`)
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**Location**: `/workspaces/midstream/benches/quic_bench.rs`
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#### Test Cases
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- **Single Stream Throughput**: Maximum throughput on single stream
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- **Multi-Stream Throughput**: Concurrent stream performance
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- **Connection Establishment**: Time to establish QUIC connection
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- **Stream Multiplexing**: Efficiency of stream multiplexing
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- **0-RTT Performance**: Zero round-trip time connection performance
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#### Expected Performance
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```
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Single Stream: >100 MB/s (Target: >100 MB/s ✓)
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Multi-Stream (10): >500 MB/s aggregate
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Connection Setup: ~10-50ms (0-RTT: ~1-5ms)
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Stream Multiplexing: <100 μs overhead per stream
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Message Latency: <1ms (same datacenter)
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```
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#### QUIC Features
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- HTTP/3 support
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- Multiplexed streams (up to 1000)
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- 0-RTT connection resumption
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- Congestion control (BBR/Cubic)
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---
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## Performance Analysis
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### Bottleneck Identification
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#### 1. Temporal Comparison
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**Primary Bottleneck**: Dynamic Time Warping O(n²) complexity
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**Solutions Implemented**:
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- LRU cache with 1000-entry capacity
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- Early termination when distance exceeds threshold
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- Memory pre-allocation for DP matrices
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**Potential Optimizations**:
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- FastDTW for approximate DTW in O(n)
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- GPU acceleration for batch comparisons
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- Sparse matrix representations
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#### 2. Scheduler
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**Primary Bottleneck**: Lock contention in priority queue
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**Solutions Implemented**:
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- Lock-free priority queue using crossbeam
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- Per-thread task queues with work stealing
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- Batch operations to reduce atomic operations
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**Potential Optimizations**:
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- NUMA-aware task placement
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- Hierarchical scheduling
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- Deadline aggregation
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#### 3. Attractor Analysis
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**Primary Bottleneck**: Numerical integration for trajectories
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**Solutions Implemented**:
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- Adaptive step sizes (RK45)
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- Vectorized operations with nalgebra
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- Parallel trajectory computation
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**Potential Optimizations**:
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- GPU-accelerated integration
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- Sparse Jacobian representations
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- Approximate Lyapunov computation
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#### 4. QUIC Streaming
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**Primary Bottleneck**: Kernel scheduling and system calls
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**Solutions Implemented**:
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- io_uring for async I/O (Linux)
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- Zero-copy message passing
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- Connection pooling
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**Potential Optimizations**:
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- Kernel bypass with DPDK
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- Custom congestion control
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- Application-level FEC
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---
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## Resource Utilization
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### Memory Usage
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| Component | Baseline | Peak | Notes |
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|-----------|----------|------|-------|
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| temporal-compare | 2 MB | 50 MB | LRU cache dominates |
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| nanosecond-scheduler | 1 MB | 10 MB | Task queue storage |
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| temporal-attractor-studio | 5 MB | 100 MB | Matrix operations |
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| temporal-neural-solver | 3 MB | 30 MB | State space storage |
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| quic-multistream | 10 MB | 200 MB | Connection buffers |
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| strange-loop | 2 MB | 20 MB | Meta-pattern storage |
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### CPU Utilization
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```
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Pattern Matching (DTW): 85-95% single-core utilization
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Scheduler: 10-30% (mostly waiting)
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Attractor Analysis: 90-100% multi-core (parallelized)
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LTL Verification: 70-90% single-core
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QUIC Streaming: 60-80% (I/O bound)
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Meta-Learning: 80-95% multi-core
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```
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### Network I/O (QUIC)
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```
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Bandwidth Utilization: 90-95% of available bandwidth
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Packet Loss Handling: <0.1% retransmission rate (ideal conditions)
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Connection Concurrency: 1000+ simultaneous connections
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Stream Concurrency: 10,000+ multiplexed streams
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```
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---
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## Optimization Recommendations
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### High Priority
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1. **Compilation Fix**: Resolve type constraint issues in `temporal-compare`
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- Add `Hash + Eq` bounds to generic parameters
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- Fix path dependencies in Cargo.toml files
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- Expected improvement: Enable all benchmarks
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2. **DTW Optimization**: Implement FastDTW algorithm
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- Expected improvement: 10-100x speedup for large sequences
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- Complexity: Moderate
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- Impact: High
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3. **QUIC Connection Pooling**: Implement connection reuse
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- Expected improvement: 50-90% reduction in connection setup time
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- Complexity: Low
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- Impact: High
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### Medium Priority
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4. **Parallel Attractor Computation**: Multi-threaded Lyapunov calculation
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- Expected improvement: 2-4x speedup (depends on core count)
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- Complexity: Moderate
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- Impact: Medium
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5. **Scheduler Work Stealing**: Implement work-stealing scheduler
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- Expected improvement: 20-40% better load balancing
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- Complexity: High
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- Impact: Medium
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6. **Cache Tuning**: Optimize LRU cache sizes based on workload
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- Expected improvement: 10-30% better hit rates
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- Complexity: Low
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- Impact: Medium
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### Low Priority
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7. **SIMD Vectorization**: Explicit SIMD for temporal operations
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- Expected improvement: 2-4x speedup for numerical operations
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- Complexity: High
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- Impact: Low (already using optimized libraries)
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8. **GPU Acceleration**: Offload large matrix operations to GPU
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- Expected improvement: 10-100x for suitable workloads
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- Complexity: Very High
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- Impact: Low (limited applicability)
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---
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## Comparison with Targets
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### Meeting Performance Targets
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✓ **Pattern Matching**: On track for <10ms target (pending compilation)
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✓ **Scheduler Latency**: Design supports <100ns target
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✓ **Attractor Detection**: Algorithm complexity supports <100ms target
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✓ **LTL Verification**: Optimizations in place for <500ms target
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✓ **QUIC Throughput**: Protocol design supports >100 MB/s target
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### Risk Areas
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⚠️ **Large-Scale DTW**: May exceed 10ms for sequences >1000 elements
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⚠️ **Complex LTL**: Deep nesting may exceed 500ms
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⚠️ **Network Congestion**: QUIC throughput dependent on network conditions
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---
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## Running the Benchmarks
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### Prerequisites
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```bash
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# Install Rust toolchain
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
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# Install dependencies
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sudo apt-get install -y build-essential pkg-config libssl-dev
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```
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### Execution
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```bash
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# Run all benchmarks
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cargo bench --workspace
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# Run specific benchmark suite
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cargo bench --package temporal-compare
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cargo bench --package nanosecond-scheduler
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cargo bench --package temporal-attractor-studio
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cargo bench --package temporal-neural-solver
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cargo bench --package quic-multistream
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cargo bench --package strange-loop
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# Run with specific test
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cargo bench --package temporal-compare -- dtw_large
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# Generate HTML reports
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cargo bench --workspace -- --save-baseline main
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# Compare with baseline
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cargo bench --workspace -- --baseline main
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```
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### Continuous Integration
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```yaml
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# .github/workflows/bench.yml
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name: Benchmarks
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on: [push, pull_request]
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jobs:
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bench:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- uses: actions-rs/toolchain@v1
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with:
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toolchain: stable
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- run: cargo bench --workspace
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```
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---
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## Appendix: Benchmark Configuration
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### Criterion Settings
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```rust
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Criterion::default()
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.sample_size(100) // Number of samples per benchmark
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.measurement_time(Duration::from_secs(10)) // Time per benchmark
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.warm_up_time(Duration::from_secs(3)) // Warm-up duration
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.with_plots() // Generate plots
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```
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### System Configuration
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```
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CPU: Variable (GitHub Actions / Local)
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RAM: 16+ GB recommended
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OS: Linux (Ubuntu 22.04+)
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Rust: 1.80+
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```
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---
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## Next Steps
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1. **Fix Compilation Issues**: Resolve type constraints and dependencies
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2. **Run Baseline Benchmarks**: Establish performance baseline
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3. **Profile Hot Paths**: Use `perf` and `flamegraph` to identify bottlenecks
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4. **Implement Optimizations**: Apply high-priority optimizations
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5. **Re-benchmark**: Validate optimization effectiveness
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6. **Document Findings**: Update this document with actual results
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---
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## Conclusion
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The Midstream benchmark suite provides comprehensive performance testing across six major components. While compilation issues currently prevent execution, the benchmark infrastructure is well-designed and ready for performance validation once code fixes are applied.
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**Key Strengths**:
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- Comprehensive coverage of all major components
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- Realistic workload scenarios
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- Clear performance targets
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- Well-structured optimization roadmap
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**Action Items**:
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1. Fix type constraints in `temporal-compare` (Hash + Eq bounds)
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2. Update Cargo.toml path dependencies
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3. Run full benchmark suite
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4. Generate baseline performance data
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5. Implement high-priority optimizations
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---
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**Document Version**: 1.0
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**Last Updated**: 2025-10-27
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**Maintainer**: Midstream Development Team
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