# RusTorch Performance
## 📊 Performance Overview
RusTorch delivers high-performance tensor operations with comprehensive benchmarks and optimizations across multiple dimensions.
## 🔥 Core Performance Metrics
### **Phase 2 Optimizer Performance** ⚡ **NEW**
**フェーズ２完成**: 革新的最適化フレームワーク実装済み (2025年9月2日)
| **Adamax** | **33,632 steps/sec** | GenericAdamOptimizer | ✅ Phase 2 完成 |
| **NAdam** | 30,245 steps/sec | GenericAdamOptimizer | ✅ Nesterov加速 |
| **RAdam** | 28,891 steps/sec | GenericAdamOptimizer | ✅ 適応学習率 |
| **Enhanced L-BFGS** | 15,678 steps/sec | モジュラー設計 | ✅ 準ニュートン最適化 |
**🏗️ Phase 2 Architecture Benefits:**
- **50%+ コード削減**: GenericAdamOptimizer統一アーキテクチャ
- **65% PyTorch互換性**: API互換性大幅向上
- **零コンパイルエラー**: 159/159テスト成功 (100%)
- **工場設計パターン**: OptimizerFactory with parameter suggestions
### Tensor Operations Performance
| **Tensor Addition** | 34K - 2.3M ops/sec | Variable by size | ✅ Broadcasting support |
| **Tensor Sum** | 52M+ ops/sec | Consistently high | ✅ SIMD optimized |
| **Matrix Multiplication** | 0.71 - 0.77 GFLOPS | Stable scaling | ✅ Blocked algorithms |
| **Neural Network Inference** | 15 - 60 inferences/sec | Batch dependent | ✅ Parallel processing |
### Matrix Decomposition Performance
**実測ベンチマーク結果 (2025年9月1日実行):**
| **4×4** | ~100μs | 6μs | 67μs | ✅ svd(), qr(), eigh() |
| **8×8** | ~1ms | 24μs | 165μs | ✅ All working |
| **16×16** | ~7ms | 107μs | 473μs | ✅ Stable performance |
| **12×12** | ~133μs | <1μs | ~260μs | ✅ All APIs fixed |
**✅ API変更**:
- `svd(false)` → `svd()` (パラメータ削除)
- `lu()` → `qr()` (LU非対応、QRで代替)
- `symeig(false, false)` → `eigh()` (統合)
- `eig(false)` → `eigh()` (統合)
### Detailed Performance Breakdown
| 64×64 | 0.77 GFLOPS | 32 | 59.86 inferences/sec |
| 128×128 | 0.76 GFLOPS | 64 | 29.35 inferences/sec |
| 256×256 | 0.76 GFLOPS | 128 | 15.09 inferences/sec |
| 512×512 | 0.71 GFLOPS | - | - |
## ⚡ Optimization Technologies
### SIMD Vectorization
- **AVX2 Support**: 256-bit vector operations
- **SSE4.1 Support**: 128-bit vector operations
- **Automatic Detection**: Runtime CPU capability detection
- **Fallback Strategy**: Graceful degradation to scalar operations
### Parallel Processing
- **Rayon Integration**: Work-stealing thread pool
- **Intelligent Scheduling**: Automatic workload balancing
- **Thread Scaling**: Optimal thread count selection
- **NUMA Awareness**: Non-uniform memory access optimization
### Memory Optimization
- **Zero-Copy Operations**: Eliminate unnecessary data movement
- **SIMD-Aligned Allocation**: Optimal memory layout for vectorization
- **Memory Pools**: Reduce allocation overhead
- **Cache-Friendly Access**: Improved locality of reference
### GPU Acceleration
- **Multi-Backend Support**: CUDA, Metal, OpenCL
- **Automatic Device Selection**: Best available GPU detection
- **Memory Transfer Optimization**: Minimize host-device transfers
- **Kernel Fusion**: Combine operations for efficiency
## 📈 Benchmark Suites
### **Phase 2 Optimizer Benchmarks** ⚡ **NEW**
```bash
# Phase 2 advanced optimizer benchmarks
cargo bench --bench advanced_optimizer_benchmark # LAMB, AdaBound, L-BFGS comprehensive tests
# Quick optimizer performance testing
cargo run --bin quick_optimizer_bench # Rapid performance validation
# Optimizer factory benchmarks
cargo test test_optimizer_factory # Parameter suggestion testing
```
### Comprehensive Benchmarking
```bash
# Run all performance benchmarks
cargo bench
# Core performance suites
cargo bench --bench parallel_performance # Parallel vs sequential
cargo bench --bench simd_performance # SIMD vectorization
cargo bench --bench memory_strategy_performance # Memory optimization
cargo bench --bench gpu_cpu_performance # GPU acceleration
cargo bench --bench integrated_performance # End-to-end validation
```
### Matrix Decomposition Benchmarks
```bash
# Comprehensive matrix operations
cargo bench --bench matrix_decomposition_benchmark
# Optimized matrix benchmarks (timeout-resistant)
cargo bench --bench optimized_matrix_benchmark
# Quick development benchmarks
cargo bench --bench quick_matrix_benchmark
```
### Specialized Benchmarks
```bash
# Legacy benchmark categories
cargo bench --bench tensor_ops # Basic tensor operations
cargo bench --bench autograd_ops # Automatic differentiation
cargo bench --bench neural_networks # Neural network layers
cargo bench --bench optimized_ops # SIMD optimizations
cargo bench --bench memory_pool # Memory management
```
## 🎯 Performance Analysis
### Scaling Characteristics
#### Tensor Operations
- **Small tensors (< 1K elements)**: CPU overhead dominates
- **Medium tensors (1K - 100K)**: Linear scaling with SIMD
- **Large tensors (> 100K)**: Memory bandwidth limited
- **GPU threshold**: > 10K elements for efficiency
#### Matrix Multiplication
- **Block size optimization**: 64x64 optimal for L1 cache
- **Memory hierarchy**: L1 > L2 > L3 > RAM access patterns
- **Parallel scaling**: Near-linear up to physical cores
- **BLAS integration**: Leverage optimized libraries when available
#### Neural Networks
- **Batch size impact**: Larger batches improve throughput
- **Layer fusion**: Combine operations to reduce memory traffic
- **Pipeline optimization**: Overlap computation and data transfer
- **Gradient computation**: Automatic differentiation overhead < 20%
### Memory Usage Patterns
#### Tensor Storage
- **Contiguous layout**: Row-major storage for cache efficiency
- **Alignment requirements**: 32-byte alignment for AVX2
- **Reference counting**: Efficient memory sharing
- **Copy-on-write**: Minimize unnecessary duplication
#### GPU Memory Management
- **Unified memory**: Automatic host-device synchronization
- **Memory pools**: Reduce allocation/deallocation overhead
- **Bandwidth optimization**: Coalesced memory access patterns
- **Transfer scheduling**: Overlap computation with transfers
## 🚀 Performance Best Practices
### Tensor Operations
```rust
// Efficient: Use in-place operations when possible
let mut a = Tensor::ones([1000, 1000]);
a.add_(&b); // In-place addition
// Efficient: Chain operations to minimize temporaries
let result = a.relu().sigmoid().mean(None);
// Avoid: Unnecessary copies
let result = a.clone() + b.clone(); // Creates extra copies
```
### Memory Management
```rust
// Efficient: Reuse tensor storage
let mut workspace = Tensor::zeros([1000, 1000]);
for batch in data_loader {
workspace.copy_(&batch.process());
let output = model.forward(&workspace);
}
// Efficient: Use views instead of copies
let view = tensor.slice(0, 0..100); // No data copy
```
### GPU Operations
```rust
// Efficient: Batch GPU operations
let inputs = vec![tensor1, tensor2, tensor3];
let outputs = gpu_batch_process(inputs); // Single kernel launch
// Avoid: Frequent CPU-GPU transfers
for tensor in tensors {
let gpu_result = tensor.to_gpu().process().to_cpu(); // Inefficient
}
```
## 📊 System Status Indicators
### Quality Metrics
- ✅ **159 Tests Passing** - 100% test success rate (Phase 2 updated)
- ✅ **Zero Compilation Errors** - Clean build across platforms
- ✅ **Phase 2 Optimizer Framework** - 33,632+ steps/sec performance
- ✅ **65% PyTorch Compatibility** - Enhanced API compatibility
- ✅ **GenericAdamOptimizer Architecture** - 50%+ code reduction
- ✅ **OptimizerFactory Pattern** - Parameter suggestion system
- ✅ **Unified Error Handling** - RusTorchResult<T> consistency
- ✅ **Broadcasting Support** - Automatic shape compatibility
- ✅ **Matrix Decomposition** - Complete linear algebra suite
- ✅ **Production Ready** - Enterprise-grade reliability
### Performance Monitoring
#### Runtime Metrics
```rust
use rustorch::profiler::{Profiler, ProfilerConfig};
// Enable performance profiling
let config = ProfilerConfig::default().with_memory_tracking(true);
let profiler = Profiler::new(config);
profiler.start("tensor_operations");
let result = tensor.matmul(&other);
let stats = profiler.end("tensor_operations");
println!("Operation took: {:.2}ms", stats.duration_ms());
println!("Memory allocated: {} bytes", stats.memory_allocated());
```
#### Benchmark Integration
```bash
# Generate performance reports
cargo bench --bench integrated_performance -- --output-format json > results.json
# Compare against baseline
cargo bench --bench matrix_decomposition_benchmark -- --baseline previous
```
## 🔬 Advanced Performance Features
### Adaptive Optimization
- **Runtime profiling**: Automatic performance tuning
- **Algorithm selection**: Choose optimal implementation based on data size
- **Hardware detection**: Adapt to available CPU/GPU capabilities
- **Learning optimization**: Improve performance over time
### Memory Pool Management
```rust
use rustorch::memory::{MemoryPool, PoolConfig};
// Create optimized memory pool
let pool_config = PoolConfig::default()
.with_chunk_size(1024 * 1024) // 1MB chunks
.with_alignment(32); // AVX2 alignment
let pool = MemoryPool::new(pool_config);
let tensor = Tensor::with_pool(&pool, &[1000, 1000]);
```
### SIMD Integration
```rust
use rustorch::simd::{SimdOps, VectorizedOps};
// Automatic SIMD acceleration
let result = tensor.vectorized_add(&other); // Uses AVX2 when available
// Manual SIMD control
if SimdOps::is_avx2_supported() {
let result = tensor.avx2_multiply(&other);
} else {
let result = tensor.scalar_multiply(&other);
}
```
## 🎯 Performance Roadmap
### Near-term Optimizations
- **Enhanced GPU kernels**: More efficient CUDA/Metal implementations
- **Improved memory management**: Better allocation strategies
- **Extended SIMD support**: AVX-512 and ARM NEON
- **Async operations**: Non-blocking tensor operations
### Future Performance Goals
- **Distributed computing**: Multi-node tensor operations
- **Quantization support**: INT8/FP16 precision modes
- **Graph optimization**: Automatic operation fusion
- **JIT compilation**: Runtime code generation
For implementation details and API usage, see [API Documentation](https://docs.rs/rustorch).