kata-containers/regorus
1
0
Fork 0
mirror of https://github.com/microsoft/regorus.git synced 2026-08-05 02:16:11 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
166ea727b8ce1c166360dba8a8ec047447bb709a
regorus/benches/evaluation/compiled_policy_evaluation_benchmark.md
Anand Krishnamoorthi 2a0b4ae6b5 feat! Mimalloc as the default allocator (#434) 
This change integrates mimalloc as the default memory allocator for Regorus,
delivering significant performance improvements across all evaluation modes
and language bindings.

Technical Implementation:
- Build mimalloc in vendored mode from C sources (following QSharp approach)
- Implement GlobalAlloc trait for seamless Rust integration
- Add optional 'mimalloc' feature flag for conditional compilation
- Add comprehensive ACI benchmarks to measure evaluation performance

Performance Impact:

Rust Engine Evaluation:
- Single-threaded: ~29% improvement (423 vs 328 Kelem/s)
- Multi-threaded: Better scaling with reduced thread contention
- Fresh engines: ~24% improvement (56 vs 45 Kelem/s)

Rust Compiled Policy Evaluation:
- Single-threaded: ~41% improvement (426 vs 303 Kelem/s)
- Multi-threaded: Improved allocation efficiency under contention
- Fresh compilation: ~26% improvement (53 vs 42 Kelem/s)

C# FFI Bindings:
- Engine evaluation: ~27% improvement (279 vs 219 Kelem/s)
- Compiled policies: ~29% improvement (273 vs 211 Kelem/s)
- Better threading characteristics through improved underlying allocation

Key Benefits:
- Reduced allocation-related contention in multi-threaded scenarios
- More consistent performance across different thread counts
- Improved memory allocation efficiency for both native Rust and FFI workloads
- Better scaling characteristics for production deployments

The mimalloc integration provides substantial performance gains while
maintaining full compatibility with existing code through feature flags.

Reference: QSharp allocator implementation
(https://github.com/microsoft/qsharp/tree/main/source/allocator)

Fixes #297

Signed-off-by: Anand Krishnamoorthi <anakrish@microsoft.com>
2025-08-25 15:01:38 -05:00

161 lines
9.9 KiB
Markdown
Raw Blame History

# Compiled Policy Evaluation Benchmark Results
## Test Environment
- **Platform**: Apple Silicon (M-Series)
- **CPU**: 16 cores
- **Architecture**: ARM64 (aarch64-apple-darwin)
- **Rust Version**: 1.82.0
- **Allocator**: mimalloc (default allocator)
- **Benchmark Framework**: Criterion.rs
- **Test Data**: 20,000 inputs per evaluation (1000 per thread)
- **Policy**: Complex authorization policy with nested rules
## Benchmark Overview
The compiled policy evaluation benchmark tests Regorus compiled policy performance across multiple thread configurations (1-32 threads). It measures throughput (thousands of evaluations per second) for different combinations of compiled policy and input data reuse strategies.
## Configuration Combinations
1. **Compiled Shared Policies, Cloned Inputs**: Each thread uses shared compiled policies and clones of parsed input data - optimal for performance
2. **Compiled Shared Policies, Fresh Inputs**: Each thread uses shared compiled policies but parses new inputs each time
3. **Compiled Per Iteration, Cloned Inputs**: Each thread compiles the policy each iteration but reuses input data
4. **Compiled Per Iteration, Fresh Inputs**: Each thread compiles new policies and parses new inputs for each iteration
## Performance Results
### Compiled Shared Policies, Cloned Inputs (Best Performance)
| Threads | Total Evaluation Time (ms) | Throughput (Kelem/s) |
|--------:|---------------------------:|---------------------:|
| 1 | 2.35 | 426 |
| 2 | 5.36 | 373 |
| 4 | 11.70 | 342 |
| 6 | 20.33 | 295 |
| 8 | 43.26 | 185 |
| 10 | 61.93 | 162 |
| 12 | 79.30 | 151 |
| 14 | 94.45 | 148 |
| 16 | 113.39 | 141 |
| 18 | 154.41 | 117 |
| 20 | 184.37 | 108 |
| 22 | 204.00 | 108 |
| 24 | 220.45 | 109 |
| 26 | 237.07 | 110 |
| 28 | 252.58 | 111 |
| 30 | 273.57 | 110 |
| 32 | 292.69 | 109 |
### Compiled Shared Policies, Fresh Inputs
| Threads | Total Evaluation Time (ms) | Throughput (Kelem/s) |
|--------:|---------------------------:|---------------------:|
| 1 | 3.34 | 299 |
| 2 | 7.29 | 274 |
| 4 | 15.19 | 263 |
| 6 | 24.90 | 241 |
| 8 | 49.22 | 163 |
| 10 | 68.45 | 146 |
| 12 | 86.55 | 139 |
| 14 | 104.77 | 134 |
| 16 | 136.07 | 118 |
| 18 | 169.05 | 106 |
| 20 | 198.25 | 101 |
| 22 | 217.05 | 101 |
| 24 | 234.75 | 102 |
| 26 | 254.53 | 102 |
| 28 | 276.06 | 101 |
| 30 | 296.12 | 101 |
| 32 | 318.81 | 100 |
### Compiled Per Iteration, Cloned Inputs
| Threads | Total Evaluation Time (ms) | Throughput (Kelem/s) |
|--------:|---------------------------:|---------------------:|
| 1 | 18.11 | 55 |
| 2 | 36.89 | 54 |
| 4 | 75.46 | 53 |
| 6 | 114.66 | 52 |
| 8 | 152.80 | 52 |
| 10 | 192.17 | 52 |
| 12 | 232.32 | 52 |
| 14 | 301.47 | 46 |
| 16 | 380.36 | 42 |
| 18 | 424.64 | 42 |
| 20 | 484.76 | 41 |
| 22 | 531.62 | 41 |
| 24 | 582.88 | 41 |
| 26 | 631.39 | 41 |
| 28 | 671.99 | 42 |
| 30 | 717.65 | 42 |
| 32 | 766.05 | 42 |
### Compiled Per Iteration, Fresh Inputs
| Threads | Total Evaluation Time (ms) | Throughput (Kelem/s) |
|--------:|---------------------------:|---------------------:|
| 1 | 19.07 | 52 |
| 2 | 38.89 | 51 |
| 4 | 79.52 | 50 |
| 6 | 120.89 | 50 |
| 8 | 161.08 | 50 |
| 10 | 202.37 | 49 |
| 12 | 244.04 | 49 |
| 14 | 316.66 | 44 |
| 16 | 398.02 | 40 |
| 18 | 449.54 | 40 |
| 20 | 500.57 | 40 |
| 22 | 557.97 | 39 |
| 24 | 605.71 | 40 |
| 26 | 656.88 | 40 |
| 28 | 710.03 | 39 |
| 30 | 741.09 | 40 |
| 32 | 801.26 | 40 |
## Analysis
The compiled policy benchmark demonstrates the following performance characteristics with mimalloc as the default allocator:
1. **Best Performance**: Compiled shared policies with cloned inputs provide the highest throughput
2. **Compilation Impact**:
- Pre-compiled policies: Significantly faster than per-iteration compilation
- Per-iteration compilation: Major overhead (~7-8x slower than pre-compiled)
3. **Scaling Patterns with mimalloc**:
- Best throughput achieved at 1 thread for shared policy configurations
- mimalloc provides better thread scaling characteristics compared to the default allocator
- Higher thread counts show performance degradation due to contention, but less severe with mimalloc
- Per-iteration compilation shows poor scaling across all thread counts
4. **Input Processing**: Fresh inputs add ~30% overhead across all configurations
5. **Thread Performance with mimalloc**:
- Peak performance at 1 thread for most configurations
- Reasonable performance maintained up to 12-16 threads for shared policies
- Compiled policies show better thread scaling than per-iteration compilation
- mimalloc helps reduce allocation-related contention in multi-threaded scenarios
## Comparison with Engine Evaluation
### Multi-Thread Performance Comparison
| Configuration | 1 Thread (Kelem/s) | 4 Threads (Kelem/s) | 8 Threads (Kelem/s) |
|:---------------------|:-------------------|:--------------------|:--------------------|
| | CP / EE | CP / EE | CP / EE |
| Shared/Cloned | 426 / 423 | 342 / 406 | 185 / 341 |
| Shared/Fresh | 299 / 309 | 263 / 297 | 163 / 266 |
| Per-iteration/Cloned | 55 / 56 | 53 / 54 | 52 / 53 |
| Per-iteration/Fresh | 52 / 53 | 50 / 51 | 50 / 51 |
### Threading Efficiency Analysis
| Configuration | Low Contention (1-4t) | Medium Contention (6-12t) | High Contention (16+t) |
|:---------------------|:----------------------|:--------------------------|:-----------------------|
| | Avg CP / EE | Avg CP / EE | Avg CP / EE |
| Shared/Cloned | 384 / 414 | 203 / 329 | 123 / 250 |
| Shared/Fresh | 284 / 302 | 176 / 235 | 108 / 201 |
| Per-iteration/Cloned | 54 / 55 | 50 / 52 | 42 / 42 |
| Per-iteration/Fresh | 51 / 52 | 47 / 50 | 40 / 40 |
The compiled policy evaluation shows performance characteristics that are generally comparable to engine evaluation, though with some notable differences. While single-threaded performance is very close between the systems, there are observable impacts from the compilation approach that become more apparent under different threading scenarios.
**Key Observations:**
- **Single-threaded performance**: Very close parity between systems, though results may vary between runs
- **Threading behavior**: Engine evaluation demonstrates better scaling characteristics under higher thread contention (4+ threads)
- **Multi-threaded impact**: Compiled policies show more pronounced performance degradation under thread contention in shared policy configurations
- **Contention resistance**: Per-iteration compilation shows more consistent (though lower absolute) performance across thread counts
- **Optimal usage**: Both systems achieve best results with minimal threading (1-4 threads), though engine evaluation maintains better performance at higher thread counts for shared configurations
Reference in New Issue View Git Blame Copy Permalink