optirs-tpu 0.3.1

//! # OptiRS TPU - TPU Coordination and Pod Management
//!
//! **Version:** 0.3.1
//! **Status:** Coming Soon (Framework Only)
//!
//! ⚠️ **Warning:** This crate is under active development. No functional implementation yet.
//! Type definitions and architecture planning only.
//!
//! `optirs-tpu` provides TPU coordination, pod management, and XLA integration for OptiRS,
//! built on [SciRS2](https://github.com/cool-japan/scirs)'s distributed computing abstractions.
//!
//! ## Dependencies
//!
//! - `scirs2-core` 0.1.1 - Required foundation
//! - `optirs-core` 0.1.0 - Core optimizers
//!
//! ## Implementation Status (v0.1.0)
//!
//! - 📝 Type definitions only
//! - 📝 Architecture planning
//! - 📝 Module structure defined
//! - 🚧 Implementation coming in future releases
//! - 🚧 TPU pod coordination (planned)
//! - 🚧 XLA integration (planned)
//!
//! ## Status: Coming Soon
//!
//! This crate is under active development for large-scale distributed training.
//!
//! ## Planned Features
//!
//! ### TPU Pod Coordination
//! - **Pod Management** - Coordinate TPU pods (v2, v3, v4, v5)
//! - **Synchronization** - Efficient all-reduce and parameter averaging
//! - **Fault Tolerance** - Automatic recovery from TPU failures
//! - **Load Balancing** - Optimal workload distribution
//!
//! ### XLA Integration
//! - **XLA Compilation** - Just-in-time compilation for TPUs
//! - **Optimization Passes** - Advanced compiler optimizations
//! - **Kernel Fusion** - Fused operations for maximum throughput
//! - **Memory Layout** - Optimal memory access patterns
//!
//! ### Distributed Training
//! - **Data Parallelism** - Distribute data across TPU cores
//! - **Model Parallelism** - Partition large models across TPUs
//! - **Pipeline Parallelism** - Layer-wise parallel execution
//! - **Hybrid Parallelism** - Combine all strategies
//!
//! ### Performance
//! - **Linear Scaling** - Near-perfect scaling to thousands of cores
//! - **Ultra-Low Latency** - Sub-millisecond synchronization
//! - **High Throughput** - Process millions of examples per second
//! - **Fault Tolerance** - Automatic checkpoint and resume
//!
//! ## Example Usage (Future)
//!
//! ```rust,ignore
//! use optirs_tpu::{TpuPodCoordinator, TpuConfig};
//! use optirs::prelude::*;
//!
//! // Initialize TPU pod
//! let config = TpuConfig {
//!     pod_size: 8,  // 8 TPU cores
//!     use_xla: true,
//!     fault_tolerance: true,
//! };
//!
//! let mut coordinator = TpuPodCoordinator::new(config)?;
//!
//! // Create distributed optimizer
//! let optimizer = Adam::new(0.001);
//! let mut tpu_opt = coordinator.wrap_optimizer(optimizer)?;
//!
//! // Training automatically distributed across TPU pod
//! let params = coordinator.distribute_parameters(&params)?;
//! let grads = coordinator.compute_gradients(&data)?;
//! let updated = tpu_opt.step(&params, &grads)?;
//! ```
//!
//! ## Architecture
//!
//! Built exclusively on SciRS2:
//! - **Distributed**: `scirs2_core::distributed::ClusterManager`
//! - **AllReduce**: `scirs2_core::advanced_distributed_computing::AllReduce`
//! - **Scheduler**: `scirs2_core::distributed::JobScheduler`
//! - **JIT**: `scirs2_core::jit::JitCompiler` for XLA
//! - **Arrays**: `scirs2_core::array_protocol::DistributedArray`
//!
//! ## Use Cases
//!
//! - **Foundation Models** - Train 100B+ parameter models
//! - **Large-Scale RL** - Distributed reinforcement learning
//! - **Scientific Computing** - Massive-scale simulations
//! - **Research** - State-of-the-art model training
//!
//! ## Contributing
//!
//! TPU development follows SciRS2 integration guidelines.
//! All distributed operations must use `scirs2_core::distributed` abstractions.

pub mod coordination;
pub mod error;
pub mod fault_tolerance;
pub mod monitoring;
pub mod pod_coordination;
pub mod synchronization;
pub mod tpu_backend;
pub mod xla;
pub mod xla_compilation;

// Re-export main types from mod.rs
mod main_types;
pub use main_types::*;

pub use coordination::PodCoordinator;
pub use tpu_backend::DeviceId;