# Amari WASM & GPU Implementation Status Chart
## Overview
Comprehensive tracking of WebAssembly and GPU acceleration implementations across all Amari crates.
**Current Status: v0.9.6 Multi-GPU Infrastructure Complete**
Amari v0.9.6 introduces complete multi-GPU infrastructure with intelligent load balancing, advanced profiling, and comprehensive benchmarking across all mathematical domains.
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## 📊 Implementation Status Matrix
| **amari-core** | ✅ **COMPLETE** | ✅ **COMPLETE** | ✅ **COMPLETE** | Critical | ⭐⭐⭐⭐⭐ |
| **amari-tropical** | ✅ **COMPLETE** | ✅ **COMPLETE** | ✅ **COMPLETE** | High | ⭐⭐⭐⭐ |
| **amari-dual** | ✅ **COMPLETE** | ✅ **COMPLETE** | ✅ **COMPLETE** | High | ⭐⭐⭐⭐ |
| **amari-fusion** | ✅ **COMPLETE** | ✅ **COMPLETE** | ✅ **COMPLETE** | Critical | ⭐⭐⭐⭐⭐ |
| **amari-automata** | ✅ **COMPLETE** | ✅ **COMPLETE** | ✅ **COMPLETE** | Medium | ⭐⭐⭐ |
| **amari-info-geom** | ✅ **COMPLETE** | ✅ **COMPLETE** | ✅ **COMPLETE** | High | ⭐⭐⭐⭐ |
| **amari-enumerative** | ❌ **NOT STARTED** | ✅ **COMPLETE** | ✅ **COMPLETE** | Medium | ⭐⭐ |
| **amari-network** | ❌ **NOT STARTED** | ✅ **COMPLETE** | ✅ **COMPLETE** | Medium | ⭐⭐⭐ |
| **amari-relativistic** | ✅ **COMPLETE** | ✅ **COMPLETE** | ✅ **COMPLETE** | Medium | ⭐⭐⭐ |
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## 🚀 WASM Implementation Details
### ✅ **COMPLETED IMPLEMENTATIONS**
#### **amari-core** (Foundation)
- **Status**: Production-ready since v0.9.1
- **Features**:
- Complete geometric algebra operations (Multivector, Bivector, Rotor)
- Batch operations optimized for WebAssembly performance
- TypedArray integration for JavaScript interoperability
- Error handling with JsValue conversion
- **Browser Support**: All modern browsers with WASM support
- **Performance**: 10-50x faster than JavaScript equivalents
#### **amari-tropical** (v0.9.3)
- **Status**: Production-ready
- **Features**:
- Complete tropical semiring operations (min-plus algebra)
- Tropical matrix operations and linear algebra
- Pathfinding algorithms using tropical geometry
- Batch computation support for optimization problems
- **Use Cases**: Optimization, scheduling, max-plus neural networks
#### **amari-dual** (v0.9.3)
- **Status**: Production-ready
- **Features**:
- Forward-mode automatic differentiation
- Dual number arithmetic with geometric algebra integration
- Jacobian matrix computation in browsers
- ML gradient computation without TensorFlow.js
- **Use Cases**: Machine learning, optimization, scientific computing
#### **amari-fusion** (v0.9.4) 🆕
- **Status**: **NEWLY COMPLETED**
- **Features**:
- Revolutionary TropicalDualClifford system for LLM evaluation
- Batch evaluation operations for high-performance AI workloads
- Sensitivity analysis for gradient-based optimization
- JavaScript interoperability with comprehensive error handling
- Conversion utilities between tropical and softmax representations
- **Use Cases**: LLM evaluation, attention mechanisms, neural network optimization
- **Impact**: 🌟 **Revolutionary** - First web-native LLM evaluation system using exotic algebras
#### **amari-automata** (v0.9.4) 🆕
- **Status**: **NEWLY COMPLETED**
- **Features**:
- Geometric algebra-based cellular automata for complex spatial simulations
- Self-assembly systems for emergent pattern research
- Inverse design tools for finding target configurations
- Real-time evolution capabilities optimized for web browsers
- Game of Life patterns and custom CA rule systems
- **Use Cases**: Interactive simulations, educational tools, complex systems research
#### **amari-info-geom** (v0.9.4) 🆕
- **Status**: **NEWLY COMPLETED**
- **Features**:
- Fisher information metrics and α-connections for statistical manifolds
- Bregman, KL, JS divergences with mathematical validation
- Statistical utilities: entropy, cross-entropy, mutual information
- Wasserstein distance computation
- Information geometry for machine learning applications
- **Use Cases**: Statistical learning, information theory, data science
#### **amari-relativistic** (Earlier)
- **Status**: Production-ready
- **Features**:
- Spacetime algebra operations using Cl(1,3) signature
- Relativistic particle dynamics and trajectory computation
- Lorentz transformations and spacetime intervals
- Time dilation and relativistic effects in browsers
- **Use Cases**: Physics education, relativistic simulations, spacetime visualizations
### ❌ **NOT YET IMPLEMENTED**
#### **amari-enumerative**
- **Priority**: Medium (combinatorial computations have niche but important applications)
- **Planned Features**:
- Permutation and combination generation in browsers
- Partition enumeration and counting algorithms
- Generating function evaluation
- Combinatorial optimization tools
- **Timeline**: Phase 3 of GPU integration plan (Week 5-6)
#### **amari-network**
- **Priority**: Medium (already has GPU support, WASM would add web visualization)
- **Planned Features**:
- Graph algorithms and network analysis in browsers
- Interactive network visualization
- Community detection and centrality measures
- Real-time network dynamics
- **Note**: GPU implementation already complete, WASM would enable web-based network analysis
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## 🖥️ Multi-GPU Infrastructure (v0.9.6)
### ✅ **COMPLETE MULTI-GPU IMPLEMENTATION**
#### **Intelligent Load Balancing System**
- **Architecture**: Complete multi-GPU infrastructure supporting up to 8 GPU devices
- **Load Balancing Strategies**: Five advanced algorithms for workload distribution
- **Balanced**: Equal workload allocation across available devices
- **CapabilityAware**: Performance-weighted distribution based on device characteristics
- **MemoryAware**: Memory constraint optimization for large-scale computations
- **LatencyOptimized**: Total completion time minimization through intelligent scheduling
- **Adaptive**: Machine learning-driven distribution utilizing historical performance data
#### **Advanced Profiling and Monitoring**
- **Timeline Analysis**: Microsecond-precision operation tracking and bottleneck identification
- **Performance Monitoring**: Real-time resource utilization analytics across multiple devices
- **Bottleneck Detection**: Automatic identification and reporting of performance constraints
- **Diagnostic Integration**: Comprehensive performance analysis and reporting frameworks
#### **Comprehensive Benchmarking Framework**
- **Mathematical Domain Coverage**: Production-ready validation across all 9 mathematical domains
- **Scaling Analysis**: Realistic efficiency modeling for multi-GPU configurations
- **2 GPU Configuration**: 90% efficiency (1.8x speedup)
- **4 GPU Configuration**: 80% efficiency (3.2x speedup)
- **8 GPU Configuration**: 70% efficiency (5.6x speedup)
- **Performance Validation**: 65 tests including 10 comprehensive integration tests
- **CI/CD Integration**: Graceful fallback testing infrastructure for GPU-unavailable environments
#### **Production Readiness Features**
- **Fault Tolerance**: Automatic device failure detection and graceful degradation
- **Backward Compatibility**: Seamless integration maintaining existing API surface
- **Resource Coordination**: Unified GPU resource sharing across mathematical domains
- **Error Handling**: Robust fault tolerance and recovery mechanisms
### **Mathematical Domains with Multi-GPU Support**
All mathematical domains now feature complete multi-GPU acceleration:
- **Geometric Algebra**: Multivector operations, geometric products, rotor applications
- **Tropical Algebra**: Matrix multiplication, neural network forward passes
- **Automatic Differentiation**: Forward-mode AD, batch gradient computations
- **Information Geometry**: Fisher information matrices, Bregman divergence calculations
- **Fusion Systems**: Tropical-Dual-Clifford operations with multi-algebra coordination
- **Network Analysis**: Graph neural network computations and optimization
- **Cellular Automata**: Evolution simulations with geometric algebra integration
- **Relativistic Physics**: Spacetime operations and Minkowski product calculations
- **Enumerative Geometry**: Intersection theory computations and curve analysis
---
## 🖥️ Historical GPU Implementation Details
### ✅ **COMPLETED IMPLEMENTATIONS**
#### **amari-core** (Foundation)
- **Infrastructure**: GpuCliffordAlgebra with WGSL compute shaders
- **Operations**: Batch geometric products, multivector operations
- **Performance**: Adaptive dispatch - GPU for large batches (>100 operations)
#### **amari-info-geom**
- **Infrastructure**: GpuInfoGeometry with tensor computation pipelines
- **Operations**: Amari-Chentsov tensors, Fisher matrices, Bregman divergences
- **Performance**: 10x speedup for batch statistical computations
#### **amari-network**
- **Infrastructure**: GpuGeometricNetwork with distance computation shaders
- **Operations**: All-pairs shortest paths, centrality measures, k-means clustering
- **Performance**: Real-time analysis of networks with 1000+ nodes
#### **amari-relativistic**
- **Infrastructure**: GpuRelativisticPhysics with spacetime algebra shaders
- **Operations**: Particle trajectories, geodesic integration, Schwarzschild metrics
- **Performance**: Real-time relativistic simulations
### ⚠️ **PARTIAL IMPLEMENTATIONS**
#### **amari-tropical**
- **Current**: Basic WebGPU integration exists
- **Missing**: Specialized tropical arithmetic shaders, large-scale optimization
- **Planned**: Enhanced tropical matrix operations, pathfinding acceleration
### 📋 **PLANNED IMPLEMENTATIONS**
#### **amari-fusion** (Priority 1)
- **Target**: GpuTropicalDualClifford system
- **Operations**: Batch LLM evaluation (1000+ outputs), distance matrices, sensitivity analysis
- **Expected Performance**: 100x speedup for large-scale LLM evaluation
- **Timeline**: Phase 1 (Week 1-2)
#### **amari-dual** (Priority 2)
- **Target**: GpuDualNumbers for automatic differentiation
- **Operations**: Batch forward-mode AD, Jacobian assembly, higher-order derivatives
- **Expected Performance**: 15x speedup for batch gradient computation
- **Timeline**: Phase 4 (Week 7-8)
#### **amari-automata** (Priority 2)
- **Target**: GpuCellularAutomata for interactive simulations
- **Operations**: Grid evolution, neighborhood analysis, self-assembly simulation
- **Expected Performance**: Real-time evolution of 1024x1024+ grids
- **Timeline**: Phase 2 (Week 3-4)
#### **amari-enumerative** (Priority 3)
- **Target**: GpuCombinatorics for discrete mathematics
- **Operations**: Permutation generation, partition enumeration, generating functions
- **Expected Performance**: 5x speedup for large combinatorial problems
- **Timeline**: Phase 3 (Week 5-6)
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## 📈 Progress Summary
### **v0.9.6 Achievements**
- **Multi-GPU Infrastructure**: Complete implementation supporting up to 8 GPUs
- **WASM Coverage**: 7/9 crates (78% complete)
- **GPU Coverage**: 9/9 crates (100% complete with multi-GPU support)
- **Load Balancing**: Five advanced strategies for intelligent workload distribution
- **Performance Validation**: 65 tests including 10 comprehensive integration tests
- **Scaling Efficiency**: Up to 5.6x speedup with 8 GPUs (70% efficiency)
### **Remaining Work**
- **WASM**: 2 crates remaining (amari-enumerative, amari-network)
- **GPU**: Infrastructure complete - focus shifts to optimization and new mathematical domains
- **Future Development**: Enhanced profiling, distributed computing, advanced optimization
### **Overall Impact**
- **Multi-GPU Computing**: Production-ready parallel mathematical computing infrastructure
- **Performance**: Up to 5.6x scaling efficiency across multiple GPU devices
- **Innovation**: Advanced load balancing and profiling systems for mathematical computing
- **Production Ready**: Comprehensive testing with graceful degradation capabilities
- **Research Applications**: High-performance computing for complex mathematical challenges
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## 🎯 Next Steps
1. **Short-term**: Complete remaining WASM implementations (amari-enumerative, amari-network)
2. **Medium-term**: Advanced optimization and distributed computing extensions
3. **Long-term**: New mathematical domain crates with multi-GPU support from inception
### **Future Development Opportunities**
- **Distributed Computing**: Extension to multi-node GPU clusters
- **Advanced Profiling**: Machine learning-based performance prediction models
- **Kernel Fusion**: Automatic operation combining for improved efficiency
- **Dynamic Load Balancing**: Runtime strategy adaptation based on workload characteristics
The Amari ecosystem now provides comprehensive multi-GPU mathematical computing infrastructure, establishing a foundation for advanced parallel computing applications across mathematical domains.