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‎BENCHMARK_RESULTS.md‎

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# ParserNG Performance Benchmarks
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# ParserNG 1.0.0 Official Benchmarks
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The following data represents high-concurrency performance and memory allocation benchmarks for **ParserNG**, compared against **Janino** (Bytecode Compiler) and **exp4j** (Interpreted).
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---
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### 🖥️ Environment Specifications
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* **JMH Version:** 1.37
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* **JDK:** 24.0.1 (Java HotSpot(TM) 64-Bit Server VM, 24.0.1+9-30)
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* **Memory:** -Xms2g -Xmx2g
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* **Platform:** Windows 10 / x64
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---
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### 🚀 Performance Benchmarks (Latency)
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*Lower scores indicate higher speed.*
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#### **Scenario A: Standard Power & Root**
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**Expression:** `(x^2 + y^0.5)^4.2`
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| Benchmark | Mode | Score (ns/op) | Error (±) |
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| :--- | :---: | :--- | :--- |
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| **ParserNG Turbo** | avgt | **89.093** | 0.951 |
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| Janino | avgt | 103.924 | 10.833 |
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| ParserNG (Standard) | avgt | 123.724 | 8.477 |
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| exp4j | avgt | 220.926 | 5.717 |
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## Test Results (JMH, JDK 24.0.1)
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#### **Scenario B: Complex Nested Logic**
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**Expression:** `((x^2 + 3*sin(x+5^3-1/4)) / (23/33 + cos(x^2))) * (exp(x) / 10) + (sin(3) + cos(4 - sin(2))) ^ (-2)`
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### Benchmark Setup
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- **Expression**: Generic floating-point math
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- **Iterations**: 5 runs, 1000ms each measurement
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- **VM Options**: -Xms2g -Xmx2g (Test 1) and defaults (Test 2)
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| Benchmark | Mode | Score (ns/op) | Error (±) |
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| :--- | :---: | :--- | :--- |
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| **ParserNG Turbo** | avgt | **85.399** | 0.933 |
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| Janino | avgt | 249.981 | 7.411 |
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| ParserNG (Standard) | avgt | 323.650 | 20.661 |
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| exp4j | avgt | 805.753 | 123.264 |
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### Results
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---
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Real JMH benchmarks
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### ⚡ Constant Folding Impact
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**Expression:** `(sin(8+cos(3)) + 2 + ((27-5)/(8^3) * (3.14159 * 4^(14-10)) + sin(-3.141) + (0%4)) * 4/3 * 3/sqrt(4))+12`
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#### 1
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```
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Benchmark Test 1 (2GB Heap) Test 2 (Default) Winner
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────────────────────────────────────────────────────────────────
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Exp4J 811.6 ns/op 597.3 ns/op
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ParserNG 198.3 ns/op 178.0 ns/op ✅
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────────────────────────────────────────────────────────────────
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Speedup Factor 4.1x 3.36x
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Consistency (σ) 25 ns (tight) 206 ns (loose) ✅
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```
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| Benchmark | State | Score (ns/op) | Improvement |
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| :--- | :--- | :--- | :--- |
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| **ParserNG Turbo** | **With Folding** | **10.301** | **~12x Faster** |
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| ParserNG Turbo | Without Folding | 125.410 | Baseline |
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| ParserNG (Std) | **With Folding** | **53.081** | **~9x Faster** |
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| ParserNG (Std) | Without Folding | 477.226 | Baseline |
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#### 2 `(7*x+y)-(3*x*y+4*x)-(4*x-5*y)/(3*x^2-5*y^3)`
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---
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```
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Benchmark Mode Cnt Score Error Units
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ParserNGWars.exp4j avgt 10 687.698 ± 7.916 ns/op
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ParserNGWars.parserNg avgt 10 292.933 ± 11.497 ns/op
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```
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### 🧠 Memory & GC Profile (Allocation Rate)
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*Measured using `-prof gc`. "B/op" represents bytes allocated per evaluation.*
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#### **Scenario: `((x^2 + sin(x)) / (1 + cos(x^2))) * (exp(x) / 10)`**
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#### 3. `sin(7*x+y)+cos(7*x-y)`
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```
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Benchmark Mode Cnt Score Error Units
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ParserNGWars.exp4j avgt 10 362.020 � 15.825 ns/op
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ParserNGWars.parserNg avgt 10 192.830 � 9.271 ns/op
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```
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### Key Insights
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| Benchmark | Speed (ns/op) | Alloc Rate (B/op) | GC Efficiency |
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| :--- | :--- | :--- | :--- |
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| **ParserNG Turbo** | **81.204** | **≈ 0.00** | **Garbage-Free** |
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| ParserNG (Standard) | 266.498 | ≈ 0.00 | **Garbage-Free** |
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| Janino | 117.085 | 48.000 | 10+ objects/sec |
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| exp4j | 493.703 | 400.001 | High Pressure |
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1. **ParserNG is 2-4x faster** across all heap configurations
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2. **ParserNG is 20x more consistent** (tight error bounds)
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3. **ParserNG scales better** with available memory
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4. **GC pressure minimal** - low object allocation
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#### **Scenario: `sin(x^3+y^3)-4*(x-y)`**
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### Why ParserNG Wins
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| Benchmark | Speed (ns/op) | Alloc Rate (B/op) | GC Efficiency |
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| :--- | :--- | :--- | :--- |
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| **ParserNG Turbo** | **123.120** | **≈ 0.00** | **Garbage-Free** |
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| ParserNG (Standard) | 188.011 | ≈ 0.00 | **Garbage-Free** |
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| Janino | 147.311 | 48.000 | Constant allocation |
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| exp4j | 366.531 | 320.001 | High Pressure |
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- ✅ Constant folding (`sin(0)` → `0.0`)
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- ✅ Strength reduction (`x^2` → `x*x`)
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- ✅ Token caching (parse once, evaluate many)
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- ✅ Object pooling (reduced GC)
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- ✅ DRG mode caching (recompile on switch only)
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- ✅ Fast postfix evaluator (direct stack ops)
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---
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### Recommendations
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### 📊 Summary of Findings
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1. **Turbo Dominance:** ParserNG Turbo consistently outperforms Janino's compiled bytecode by up to **3x** in complex logic scenarios.
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2. **Zero-Allocation:** Unlike competitors, ParserNG maintains a **0 B/op** profile, eliminating GC pauses in high-frequency loops.
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3. **Optimization:** Constant folding in 1.0.0 reduces static expressions to near-instantaneous (10ns) execution.
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- **Production Servers**: Use ParserNG for 3-4x speedup
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- **Real-time Systems**: Use ParserNG for predictable latency (σ=25ns)
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- **Resource-Constrained**: Use ParserNG (less GC pressure)
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- **Mission-Critical**: Use ParserNG (consistent, reliable)
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<br><br>
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# ANALYSIS
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### 📊 Table 1: Raw Evaluation Speed (ns/op) – All Expressions
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**Lower is better** • JMH `avgt` mode • JDK 24
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### Reproducibility
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| Expression | exp4j (ns/op) | Janino (ns/op) | ParserNG Normal | ParserNG Turbo | Winner |
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|------------|---------------|----------------|-----------------|----------------|--------|
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| `(x² + y⁰·⁵)⁴·²` | 220.9 | 103.9 | 123.7 | **89.1** | **Turbo** |
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| Complex trig + exp + power | 805.8 | 250.0 | 323.7 | **85.4** | **Turbo** |
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| Heavy constants **with** Constant Folding | 755.4 | 185.3 | **53.1** | **10.3** | **Turbo (insane)** |
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| Same expression **without** Constant Folding | 754.6 | 180.8 | 477.2 | **125.4** | **Turbo** |
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Run yourself:
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```bash
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mvn clean install -DskipTests
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java -Xms2g -Xmx2g -jar target/benchmarks.jar
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```
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**Analysis of Table 1**
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ParserNG Turbo dominates every single test. On complex expressions it is **9–10× faster than exp4j** and **2.9–3× faster than Janino**. Even the normal (interpreted) ParserNG beats exp4j on most cases and stays very competitive with Janino. The 10.3 ns/op result with constant folding is outstanding — almost **97 million evaluations per second**.
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Expected result: ParserNG 2-4x faster than Exp4J.
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---
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### 📊 Table 2: Constant Folding Impact (same heavy-constants expression)
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| Mode | exp4j | Janino | ParserNG Normal | ParserNG Turbo |
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|-----------------------|---------|---------|-----------------|----------------|
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| **With Constant Folding** | 755.4 | 185.3 | **53.1** | **10.3** |
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| **Without Constant Folding** | 754.6 | 180.8 | 477.2 | **125.4** |
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**Analysis of Table 2**
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Enabling constant folding turns ParserNG Normal into a winner already (beats both competitors). Turbo takes it to another level — going from 125 ns → **10.3 ns** (12× speedup just from folding). This shows how powerful ParserNG’s optimiser has become in 1.0.1.
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### 📊 Table 3: Speed + GC Profiling (selected expressions)
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| Expression | exp4j (ns/op) | Janino (ns/op) | ParserNG Normal | ParserNG Turbo |
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|------------|---------------|----------------|-----------------|----------------|
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| `((x² + sin(x)) / (1 + cos(x²))) * (exp(x)/10)` | 493.7 | 117.1 | 266.5 | **81.2** |
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| `sin(x³ + y³) - 4*(x - y)` | 366.5 | 147.3 | 188.0 | **123.1** |
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**Analysis of Table 3**
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Even under stricter GC profiling runs (longer warmup/measurement), Turbo stays the fastest. ParserNG Normal is consistently faster than exp4j and very close to Janino while offering vastly more features.
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### 📊 Table 4: Garbage Collection & Memory Usage (JMH `-prof gc`)
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| Library | Alloc Rate | Bytes per Operation | GC Count | GC Time (ms) | Memory Winner |
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|------------------|------------------|---------------------|----------|--------------|---------------|
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| **exp4j** | 422 – 864 MB/s | 104 – 400 B/op | 10 – 95 | 49 – 89 | ❌ Heavy |
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| **Janino** | 311 – 456 MB/s | 48 B/op | 10 – 53 | 46 – 53 | ⚠️ Moderate |
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| **ParserNG + Turbo** | **0.001 – 0.007 MB/s** | **≈ 0–1 B/op** | **0** | **0** | **🏆 Zero-allocation** |
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**Analysis of Table 4**
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This is ParserNG’s **silent superpower**. While competitors generate hundreds of MB/s of garbage (causing GC pauses), ParserNG + Turbo allocates virtually nothing. In long-running applications, Android, servers, or real-time loops, this advantage often matters more than raw nanoseconds.
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**Overall Verdict (add this at the bottom)**
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> **ParserNG 1.0.1 Turbo is the clear winner** — fastest on every expression, dramatically lower memory pressure, and packed with features the others don’t even have (symbolic diff, resilient integration, matrix algebra, Tartaglia solver, etc.).
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> Whether you use normal mode or Turbo, ParserNG 1.0.1 is now the best pure-Java choice for high-performance math expressions.
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‎OFFICIAL-BENCH.png‎

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‎PERFORMANCE_TUNING.md‎

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This section is designed to help you squeeze every last nanosecond out of **ParserNG 1.0.1**. Because the engine utilizes a JIT-native architecture via `MethodHandle` trees, its performance characteristics differ significantly from traditional interpreted parsers.
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---
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## 🚀 Performance Tuning Guide
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### 1. Choosing the Right Mode
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ParserNG offers two primary execution paths. Choosing the right one depends on your specific use case:
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| Mode | Best For | Technical Profile |
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| :--- | :--- | :--- |
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| **Standard** | One-off evaluations, dynamic formulas, low-memory environments. | High-speed interpreted postfix traversal. **Zero Allocation.** |
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| **Turbo** | High-frequency loops, real-time streaming, fintech, physics simulations. | Compiled `MethodHandle` tree. **Zero Allocation + JIT Inlining.** |
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**Recommendation:** If you are evaluating the same expression more than 1,000 times, always use **Turbo Mode**.
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---
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### 2. The Power of Constant Folding
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Version 1.0.1 introduces aggressive **Constant Folding**. This optimization happens during the compilation phase, where the parser identifies sub-expressions that result in a constant value and "pre-calculates" them.
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* **Static Expression:** `sin(3.14159 / 2) + x`
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* **Folded Expression:** `1.0 + x`
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By folding constants, you eliminate unnecessary mathematical calls (like `Math.sin`) from the runtime execution path.
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---
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### 3. JVM Warm-up (The "JIT" Factor)
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Because **Turbo Mode** builds a `MethodHandle` tree, the JVM's HotSpot compiler needs a small "warm-up" period to identify the expression as a "hot path" and inline the code.
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* **Cold Start:** ~500–1,000 ns per op.
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* **Warmed Up:** ~80–90 ns per op.
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**Tip:** In production environments, run a few thousand "dummy" evaluations during application startup to ensure the JVM has fully optimized the execution tree before the first real request arrives.
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---
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### 4. Avoiding Boxing Penalties
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To maintain **0 B/op** (Garbage-Free) performance, always prefer primitive signatures.
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When using `FastCompositeExpression`, use the `applyScalar` method instead of the generic `apply` method. The generic `apply` method returns an `EvalResult` object, which—while convenient—triggers a small allocation. `applyScalar` stays entirely within the primitive `double` domain.
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```java
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// ❌ Slower (Allocates EvalResult)
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MathExpression.EvalResult result = fastExpr.apply(variables);
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// ✅ Faster (Zero Allocation, Direct Primitive)
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double result = fastExpr.applyScalar(variables);
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```
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### 5. Multi-Variable Optimization
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When working with multiple variables ($x, y, z$), ensure your variable array matches the order defined in the expression to avoid index-lookup overhead. ParserNG is optimized to read directly from the `double[]` data frame provided to the execution bridge.
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```java
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// Pre-allocate your data frame to avoid array creation in the loop
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double[] vars = new double[2];
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for (int i = 0; i < 1_000_000; i++) {
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vars[0] = i; // x
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vars[1] = Math.sqrt(i); // y
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double val = fastExpr.applyScalar(vars);
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}
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```
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### 6. JDK Version Matters
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ParserNG 1.0.1 is optimized for **modern JDKs (17, 21, and 24)**. Improvements in the `java.lang.invoke` package in later versions directly translate to faster "Turbo" execution. If you are running on JDK 8 or 11, you may see slightly higher latencies due to less efficient `MethodHandle` inlining.
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