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Crate optirs_tpu

Crate optirs_tpu 

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§OptiRS TPU - TPU Coordination and Pod Management

Version: 0.1.0 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’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)

ⓘ
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.

Re-exports§

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

Modules§

coordination
TPU Pod Coordination and Management
error
fault_tolerance
monitoring
pod_coordination
TPU Pod Coordination Module
synchronization
TPU Synchronization and Communication Primitives
tpu_backend
xla
xla_compilation

Structs§

CompilationMetrics
XLA compilation metrics
MemoryUsageStats
Memory usage statistics
TPUConfig
TPU configuration for optimization
TPUOptimizer
TPU-optimized optimizer wrapper
TPUPerformanceMetrics
TPU performance metrics
TPUTopologyInfo
TPU topology information
UtilizationMetrics
TPU utilization metrics
XLAShape
XLA tensor shape

Enums§

PodTopology
TPU pod topologies
TPUMemoryOptimization
TPU memory optimization strategies
TPUVersion
TPU versions with different capabilities
XLAOptimizationLevel
XLA optimization levels