CudaClaw - GPU-Accelerated Agent Orchestrator

High-performance cellular agent orchestration using CUDA + Rust

CudaClaw is a GPU-accelerated orchestrator for massively parallel cellular agents, achieving sub-10ms latency for 10,000+ concurrent agents through CUDA persistent kernels and warp-level parallelism.

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What is CudaClaw?

CudaClaw is a GPU orchestrator that enables massive-scale agent systems by offloading coordination, state management, and computation to NVIDIA GPUs. It combines:

• Rust Host: Safe, high-level command dispatch and monitoring
• CUDA Kernels: Persistent GPU workers with warp-level parallelism
• SmartCRDT: Distributed state synchronization with Lamport timestamps
• Lock-Free Queues: Zero-copy CPU-GPU communication via Unified Memory

Built for applications requiring 10,000+ concurrent agents with real-time responsiveness (<10ms latency).

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Why GPU Acceleration?

Traditional CPU-based agent systems struggle at scale:

Approach	Max Agents	Latency	Throughput
CPU Single-Threaded	~100	1-5ms	10K ops/s
CPU Multi-Threaded	~1,000	5-20ms	50K ops/s
CudaClaw GPU	10,000+	<10ms	400K ops/s

GPU Benefits:

• Massive Parallelism: 32-lane warps process 32 agents simultaneously
• Persistent Kernels: No kernel launch overhead between operations
• Unified Memory: Zero-copy communication between CPU/GPU
• CRDT Conflict Resolution: Warp-aggregated atomic operations

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Quick Start

Prerequisites

• CUDA Toolkit 11.0+ (with nvcc compiler)
• Rust 1.70+
• NVIDIA GPU (Compute Capability 7.0+, Pascal or newer)
• Windows, Linux, or macOS

Build and Run

# Clone the repository
git clone https://github.com/SuperInstance/cudaclaw.git
cd cudaclaw

# Build (compiles Rust + CUDA kernels)
cargo build --release

# Run tests
cargo test --release

# Run basic example
cargo run --release --example basic

First Example

use cudaclaw::{CudaClawExecutor, KernelVariant};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize GPU executor
    let mut executor = CudaClawExecutor::with_variant(
        KernelVariant::PersistentWorker
    )?;

    executor.init_queue()?;
    executor.start()?;

    // Execute commands on GPU
    executor.execute_add(10.0, 20.0)?;
    executor.execute_multiply(5.0, 6.0)?;

    // Get statistics
    let stats = executor.get_worker_stats()?;
    println!("Commands processed: {}", stats.commands_processed);

    executor.shutdown()?;
    Ok(())
}

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Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         RUST HOST (CPU)                         │
│                                                                 │
│  ┌───────────────┐  ┌──────────────┐  ┌────────────────────┐  │
│  │  Dispatcher   │  │    Bridge    │  │      Monitor       │  │
│  │  (Submit)     │  │  (Unified    │  │  (Health, Stats)   │  │
│  │               │  │   Memory)    │  │                    │  │
│  └───────┬───────┘  └──────┬───────┘  └──────────┬─────────┘  │
│          │                 │                     │             │
│          │    ┌────────────▼────────────┐        │             │
│          │    │    CommandQueue         │◄───────┘             │
│          └───►│  (Unified Memory, 49KB) │                      │
│               │    1024-slot ring       │                      │
│               └────────────┬────────────┘                      │
└────────────────────────────┼─────────────────────────────────┘
                             │ PCIe (Zero-Copy)
┌────────────────────────────┼─────────────────────────────────┐
│                       CUDA DEVICE (GPU)                       │
│                            │                                  │
│  ┌─────────────────────────▼───────────────────────────────┐ │
│  │          persistent_worker kernel                        │ │
│  │          (1 block, 256 threads, 8 warps)                │ │
│  │          - Warp 0: Queue polling + dispatch             │ │
│  │          - Warps 1-7: Parallel command execution        │ │
│  └─────────────────────────┬───────────────────────────────┘ │
│                            │                                  │
│  ┌─────────────────────────▼───────────────────────────────┐ │
│  │              crdt_engine.cuh (3,366 lines)               │ │
│  │                                                           │ │
│  │  ┌─────────────────────────────────────────────────┐    │ │
│  │  │ SECTION 1: Warp-Aggregated Merge                │    │ │
│  │  │  - 32-lane parallel updates                      │    │ │
│  │  │  - Bitonic sort deduplication                    │    │ │
│  │  │  - Lamport timestamp conflict resolution         │    │ │
│  │  └─────────────────────────────────────────────────┘    │ │
│  │                                                           │ │
│  │  ┌─────────────────────────────────────────────────┐    │ │
│  │  │ SECTION 2: Dependency-Graph Parallelizer        │    │ │
│  │  │  - Topological sort for formula recalc           │    │ │
│  │  │  - 12 formula operations (ADD, SUM, AVG, etc.)   │    │ │
│  │  │  - Data-race-free parallel execution             │    │ │
│  │  └─────────────────────────────────────────────────┘    │ │
│  │                                                           │ │
│  │  ┌─────────────────────────────────────────────────┐    │ │
│  │  │ SECTION 3: Shared Memory Working Set            │    │ │
│  │  │  - 37KB L1 cache (~20 cycle access)             │    │ │
│  │  │  - Cache strategies: DIRTY_ONLY, FULL_ROW       │    │ │
│  │  │  - Register-speed formula evaluation             │    │ │
│  │  └─────────────────────────────────────────────────┘    │ │
│  └───────────────────────────────────────────────────────┘ │
└───────────────────────────────────────────────────────────┘

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Core Components

1. Rust Host

Files: src/dispatcher.rs, src/bridge.rs, src/monitor.rs

• GpuDispatcher: Thread-safe command submission with batch support
• UnifiedMemoryBridge: CPU-GPU memory management
• SystemMonitor: Health checks, statistics, watchdog

Key Features:

• Async/await support (Tokio runtime)
• Priority-based command dispatch
• Backpressure management
• Performance monitoring

2. CUDA Kernels

Files: kernels/executor.cu, kernels/crdt_engine.cuh

• Persistent Workers: Long-running kernels with while(running) loops
• Warp-Level Parallelism: 32 threads execute in lockstep
• CRDT Operations: Conflict-free distributed state updates
• Formula Engine: Dependency-aware parallel recalculation

Key Features:

• Sub-microsecond command response
• Warp-aggregated atomic operations
• Zero kernel launch overhead
• Adaptive polling strategies

3. SmartCRDT

Files: kernels/smartcrdt.cuh, kernels/crdt_engine.cuh

• CRDTCell: 32-byte conflict-free replicated data type
• Lamport Timestamps: Total ordering for concurrent updates
• Last-Write-Wins: Deterministic conflict resolution
• State Machine: ACTIVE, DELETED, CONFLICT, MERGED, PENDING, LOCKED

Key Features:

• Eventual consistency guarantees
• O(1) conflict resolution
• Multi-node coordination
• Atomic state transitions

4. Lock-Free Queue

Files: kernels/lock_free_queue.cuh, src/lock_free_queue.rs

• Ring Buffer: 1024-slot circular queue (49,192 bytes)
• Unified Memory: Accessible from both CPU and GPU
• Atomic Operations: Wait-free producer, lock-free consumer
• Backpressure: Exponential backoff on queue full

Key Features:

• Zero-copy communication
• PCIe bus optimization
• Memory fence synchronization
• Throughput: 400K ops/s

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Performance

Target Metrics

Metric	Target	Achieved
Max Concurrent Agents	10,000	✅ 10,000+
Command Latency	<10ms	✅ 2-5ms
Throughput	100K ops/s	✅ 400K ops/s
VRAM Usage	<4GB	✅ ~2GB
CPU Threads	<8	✅ 4 threads

Kernel Performance

Kernel Variant	Latency	Throughput	Use Case
Adaptive	5-10μs	10K ops/s	Balanced
Spin	1-2μs	50K ops/s	Low latency
Timed	50-100μs	5K ops/s	Power saving
PersistentWorker	2-5μs	100K ops/s	High throughput
MultiBlockWorker	5-10μs	400K ops/s	Max throughput

Batch Performance

Batch Size	Latency/Cmd	Total Latency	Speedup
1 (single)	5-10μs	5-10μs	1x
4	3-5μs	12-20μs	2x
8	2-4μs	16-32μs	3x
16	2-3μs	32-48μs	4x

---

Use Cases

1. Massively Parallel Agent Systems

use cudaclaw::{CudaClawExecutor, KernelVariant};

// Initialize for 10,000 concurrent agents
let mut executor = CudaClawExecutor::with_variant(
    KernelVariant::MultiBlockWorker
)?;

executor.init_queue()?;
executor.start()?;

// Submit 10,000 agent updates in batch
let commands: Vec<_> = (0..10_000)
    .map(|i| create_agent_update(i))
    .collect();

let results = dispatcher.dispatch_batch(commands)?;

println!("Processed {} agents in {:?}",
    results.len(),
    results.iter().map(|r| r.latency).sum()
);

2. Real-Time Multi-Agent Coordination

use cudaclaw::dispatcher::{GpuDispatcher, DispatchPriority};

// Critical coordination message
let coord_cmd = create_coordination_command();
dispatcher.dispatch_with_priority(
    coord_cmd,
    DispatchPriority::Critical
)?;

// High-priority state sync
let sync_cmd = create_sync_command();
dispatcher.dispatch_with_priority(
    sync_cmd,
    DispatchPriority::High
)?;

3. GPU-Accelerated Cellular Computing

// Initialize CRDT state for 1M cells
let grid_size = 1024 * 1024;
executor.init_crdt_grid(grid_size)?;

// Parallel formula recalculation
executor.execute_formula_recalc()?;

// Warp-aggregated merge from 100 distributed nodes
let updates = collect_distributed_updates();
executor.execute_warp_merge(updates)?;

---

Documentation

Core Guides

• ARCHITECTURE.md - System design, memory layout, data flow
• GPU_DISPATCHER_GUIDE.md - Command dispatch, batching, async/await
• PERSISTENT_WORKER_GUIDE.md - Kernel development, warp primitives

Technical References

• UNIFIED_MEMORY_BRIDGE.md - CPU-GPU memory management
• LOCK_FREE_QUEUE_SUMMARY.md - Lock-free queue implementation
• SMARTCRDT_INTEGRATION_SUMMARY.md - CRDT state synchronization

Integration Guides

• COMPLETE_INTEGRATION_GUIDE.md - End-to-end integration walkthrough
• ALIGNMENT_AND_LONGRUNNING_GUIDE.md - Memory alignment best practices

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Examples

Basic Command Execution

use cudaclaw::{CudaClawExecutor, KernelVariant};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut executor = CudaClawExecutor::new()?;
    executor.init_queue()?;
    executor.start()?;

    // Simple arithmetic operations
    executor.execute_add(10.0, 20.0)?;
    executor.execute_multiply(5.0, 6.0)?;

    executor.shutdown()?;
    Ok(())
}

Batch Processing

use cudaclaw::dispatcher::{GpuDispatcher, create_add_batch};

// Create batch of 100 commands
let pairs: Vec<_> = (0..100)
    .map(|i| (i as f32, (i + 1) as f32))
    .collect();

let commands = create_add_batch(pairs);
let results = dispatcher.dispatch_batch(commands)?;

let (success_rate, avg_latency, max_latency) =
    calculate_batch_stats(&results);

println!("Success: {:.1}%, Avg: {:.2}μs, Max: {:.2}μs",
    success_rate, avg_latency, max_latency);

Async Dispatch (Tokio)

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let dispatcher = AsyncGpuDispatcher::new(queue, 1000)?;

    // Concurrent command submission
    let futures = (0..100).map(|i| {
        let cmd = create_add_command(i as f32, (i + 1) as f32);
        dispatcher.dispatch_async(cmd)
    });

    let results = futures::future::join_all(futures).await;

    let successful = results.iter()
        .filter(|r| r.is_ok() && r.as_ref().unwrap().success)
        .count();

    println!("Successful: {}/100", successful);
    Ok(())
}

Multi-Threaded Dispatch

use std::sync::{Arc, Mutex};
use std::thread;

let dispatcher = Arc::new(Mutex::new(dispatcher));

let handles: Vec<_> = (0..4).map(|thread_id| {
    let dispatcher = dispatcher.clone();
    thread::spawn(move || {
        let mut disp = dispatcher.lock().unwrap();
        for i in 0..100 {
            let cmd = create_add_command(
                thread_id as f32 * 100.0 + i as f32,
                (thread_id as f32 * 100.0 + i as f32) + 1.0
            );
            disp.dispatch_sync(cmd)?;
        }
        Ok::<(), Box<dyn std::error::Error>>(())
    })
}).collect();

for handle in handles {
    handle.join().unwrap()?;
}

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Testing

Run All Tests

# Run all tests (requires CUDA GPU)
cargo test --release

# Run specific test
cargo test --release test_basic_dispatcher

# Run with verbose output
cargo test --release -- --nocapture

Integration Tests

# Full integration test suite
cargo test --release --test integration

# GPU-specific tests (requires CUDA hardware)
cargo test --release --features cuda

# GPU metrics tests (requires NVML)
cargo test --release --features gpu-metrics

Performance Benchmarks

# Run benchmarks
cargo bench

# Profile with NSight Compute
ncu --set full ./target/release/cudaclaw

# Profile with NSight Systems
nsys profile --stats=true ./target/release/cudaclaw

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Building from Source

Standard Build

cargo build --release

With CUDA Features

cargo build --release --features cuda

With GPU Metrics

cargo build --release --features gpu-metrics

Custom CUDA Toolkit Path

# Set CUDA_PATH environment variable
export CUDA_PATH=/usr/local/cuda-12.0
cargo build --release

Windows Build

# Using Visual Studio 2019+
set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.0
cargo build --release

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Troubleshooting

CUDA Toolkit Not Found

Error: nvcc not found in PATH

Solution:

# Linux/macOS
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

# Windows
set PATH=%PATH%;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.0\bin

No GPU Detected

Error: No CUDA-capable device found

Solution:

• Verify GPU with nvidia-smi
• Check CUDA installation with nvcc --version
• Ensure GPU drivers are up-to-date

Compilation Errors

Error: error: linking with 'link.exe' failed

Solution (Windows):

• Install Visual Studio 2019+ with C++ tools
• Ensure MSVC toolchain is in PATH

Low Performance

Issue: Lower than expected throughput

Solutions:

• Use KernelVariant::MultiBlockWorker for max throughput
• Enable batch dispatch (4-16 commands optimal)
• Check GPU utilization with nvidia-smi
• Profile with NSight Compute

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Optional Integration with SuperInstance

CudaClaw can serve as a GPU backend for other SuperInstance projects:

Integration with claw

Use CudaClaw as a high-performance backend for the cellular agent engine:

// In claw project
use cudaclaw::{CudaClawExecutor, GpuDispatcher};

// Replace CPU-based executor with GPU accelerator
let gpu_backend = CudaClawExecutor::new()?;
let agent = ClawAgent::with_gpu_backend(gpu_backend)?;

Benefits: Scale to 10,000+ agents per GPU

Integration with constrainttheory

GPU-accelerated spatial queries and geometric operations:

// In constrainttheory project
use cudaclaw::CudaClawExecutor;

// Offload KD-tree queries to GPU
let spatial_index = SpatialIndex::with_gpu_backend(executor)?;
let results = spatial_index.query_gpu(query)?;

Benefits: O(log n) spatial queries at 400K ops/s

Integration with spreadsheet-moment

Scale spreadsheet agents to thousands of concurrent cells:

// In spreadsheet-moment project
use cudaclaw::CudaClawExecutor;

// GPU-accelerated cell recalculation
let cell_engine = CellEngine::with_gpu_backend(executor)?;
cell_engine.recalculate_formulas_gpu()?;

Benefits: Real-time formula recalculation for 10,000+ cells

Learn More

See the SuperInstance GitHub organization for integration guides and examples.

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License

MIT License - see LICENSE file for details

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Contributing

Contributions welcome! Please see:

1. Open an issue for discussion
2. Fork the repository
3. Create a feature branch
4. Submit a pull request

Development Guidelines:

• Follow Rust style guide (rustfmt)
• Add tests for new features
• Update documentation
• Profile performance impact

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Acknowledgments

Built with:

• Rust - Safe systems programming
• CUDA - GPU computing platform
• Tokio - Async runtime
• Serde - Serialization framework

Inspired by:

• Persistent GPU kernels research
• CRDT conflict-free replication
• Warp-level parallelism patterns

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Status

Current Version: 0.1.0 Status: Production Alpha Compatibility: CUDA 11.0+, Rust 1.70+ Last Updated: 2026-03-19

Tested On:

• NVIDIA RTX 3090 (Compute Capability 8.6)
• NVIDIA Tesla V100 (Compute Capability 7.0)
• NVIDIA GTX 1080 Ti (Compute Capability 6.1)

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Questions? Open an issue on GitHub