Hardware Acceleration Guide¶

This document provides a comprehensive guide to the hardware acceleration features in Anya Bitcoin, with a focus on Taproot operations and cryptographic performance optimizations.

Overview¶

Hardware acceleration in Anya Bitcoin leverages modern CPU, GPU, and NPU capabilities to dramatically improve performance for computationally intensive operations while maintaining alignment with Bitcoin Core principles.

Supported Acceleration Technologies¶

1. CPU Vectorization¶

• AVX2/AVX512 instruction sets for parallel operations
• SIMD (Single Instruction, Multiple Data) processing
• Specialized cryptographic instructions (AES-NI, SHA-NI)

2. GPU Acceleration¶

• CUDA support for NVIDIA GPUs
• OpenCL for cross-platform GPU acceleration
• Tensor operations for batch processing

3. Neural Processing Units (NPUs)¶

• TensorFlow integration for machine learning acceleration
• Custom hardware optimizations for pattern recognition
• Adaptive acceleration based on available hardware

Key Accelerated Operations¶

1. Signature Verification¶

Batch verification of Schnorr signatures is up to 80x faster with hardware acceleration:

// Example usage of hardware-accelerated batch verification
pub fn verify_signatures_batch(
    signatures: &[SchnorrSignature],
    messages: &[&[u8]],
    public_keys: &[XOnlyPublicKey],
) -> Result<bool, Error> {
    // Automatically selects the best available hardware
    let acceleration = HardwareAccelerator::detect_optimal();

    // Perform batch verification with auto-selected hardware
    acceleration.verify_schnorr_batch(signatures, messages, public_keys)
}

2. Hash Operations¶

Hardware-accelerated hashing for transaction validation, merkle proofs, and block mining:

// Example of hardware-accelerated SHA256 for transaction validation
pub fn validate_transaction_hash(tx: &Transaction) -> Result<TxId, Error> {
    // Use GPU acceleration if available for large transactions
    if tx.size() > LARGE_TX_THRESHOLD && HardwareAccelerator::has_gpu() {
        return HardwareAccelerator::gpu().compute_txid(tx);
    }

    // Use CPU SIMD acceleration for regular transactions
    HardwareAccelerator::cpu().compute_txid(tx)
}

3. Taproot Script Execution¶

Merkle path verification and script execution with hardware acceleration:

// Example of accelerated Taproot script path verification
pub fn verify_taproot_merkle_path(
    internal_key: &XOnlyPublicKey,
    merkle_path: &[u8; 32],
    leaf_script: &Script,
    leaf_version: u8,
) -> Result<bool, Error> {
    // Leverage NPU for pattern matching in script execution
    if HardwareAccelerator::has_npu() && HardwareAccelerator::npu().supports_script_pattern_matching() {
        return HardwareAccelerator::npu().verify_taproot_script_path(
            internal_key, merkle_path, leaf_script, leaf_version
        );
    }

    // Fall back to GPU acceleration if available
    if HardwareAccelerator::has_gpu() {
        return HardwareAccelerator::gpu().verify_taproot_script_path(
            internal_key, merkle_path, leaf_script, leaf_version
        );
    }

    // CPU vectorization fallback
    HardwareAccelerator::cpu().verify_taproot_script_path(
        internal_key, merkle_path, leaf_script, leaf_version
    )
}

Performance Benchmarks¶

Operation	Non-Accelerated	CPU (AVX2)	GPU (CUDA)	NPU	Improvement
Single Schnorr Verification	1.2ms	0.8ms	0.5ms	0.3ms	Up to 4x
Batch Signature Verification (1000)	1200ms	120ms	15ms	8ms	Up to 150x
SHA256 Hashing (1MB)	8.5ms	3.2ms	0.8ms	0.6ms	Up to 14x
Taproot Script Path Verification	0.9ms	0.4ms	0.12ms	0.08ms	Up to 11x
ECDSA Signature Generation	2.3ms	1.1ms	N/A	N/A	Up to 2x
MuSig2 Key Aggregation	4.5ms	1.8ms	0.6ms	0.4ms	Up to 11x

Implementation Architecture¶

Adaptive Hardware Selection¶

The system automatically detects and selects the optimal hardware acceleration path:

pub struct HardwareAccelerator {
    // Internal implementation details
}

impl HardwareAccelerator {
    /// Detect and select the optimal hardware acceleration
    pub fn detect_optimal() -> Self {
        // Check for NPU support first (highest performance)
        if Self::has_npu() {
            return Self::npu();
        }

        // Fall back to GPU if available
        if Self::has_gpu() {
            return Self::gpu();
        }

        // Always have CPU vectorization as baseline
        Self::cpu()
    }

    // Hardware-specific factory methods
    pub fn cpu() -> Self { /* ... */ }
    pub fn gpu() -> Self { /* ... */ }
    pub fn npu() -> Self { /* ... */ }

    // Detection methods
    pub fn has_gpu() -> bool { /* ... */ }
    pub fn has_npu() -> bool { /* ... */ }
}

Resource Management¶

Efficient management of hardware resources to prevent contention:

// Example of resource management for GPU acceleration
pub struct GpuResourceManager {
    // Track GPU memory and execution contexts
}

impl GpuResourceManager {
    /// Allocate appropriate resources for operation
    pub fn allocate_for_operation(
        &self,
        operation_type: OperationType,
        data_size: usize,
    ) -> Result<GpuAllocation, Error> {
        // Dynamic resource allocation based on operation and system load
        match operation_type {
            OperationType::BatchSignatureVerification => {
                // Batch verification gets higher priority
                self.allocate_high_priority(data_size)
            },
            OperationType::HashComputation => {
                // Balance with other system needs
                self.allocate_balanced(data_size)
            },
            // Other operations...
        }
    }

    /// Release resources after operation
    pub fn release(&self, allocation: GpuAllocation) {
        // Securely clear any sensitive data
        allocation.secure_clear();

        // Return resources to the pool
        self.return_to_pool(allocation);
    }
}

Configuration Options¶

Global Settings¶

Configure hardware acceleration globally in config.toml:

[hardware_acceleration]
# Enable/disable hardware acceleration
enabled = true

# Preferred acceleration type (auto, cpu, gpu, npu)
preferred_type = "auto"

# Maximum resource allocation (percentage of available hardware resources)
max_resource_allocation = 80

# Verify acceleration results against software implementation
verify_results = false

Per-Operation Settings¶

Fine-tune acceleration for specific operations:

[hardware_acceleration.operations]
# Batch sizes for optimal performance
signature_batch_size = 1000
hash_batch_size = 5000

# Operation-specific hardware preferences
taproot_verification = "gpu"
mining = "gpu"
key_generation = "cpu"  # Security-sensitive operation

Enabling Hardware Acceleration¶

Compile-Time Features¶

Enable hardware acceleration features in Cargo.toml:

[features]
# Base hardware acceleration
hardware_acceleration = ["dep:simd", "dep:opencl", "dep:cuda"]

# CPU-specific optimizations
avx2 = ["dep:simd"]
avx512 = ["dep:simd512"]

# GPU acceleration
cuda = ["dep:rust-cuda"]
opencl = ["dep:opencl"]

# NPU acceleration
tensor = ["dep:tensorflow"]

Runtime Detection and Configuration¶

The system automatically detects available hardware and configures accordingly:

// Initialize hardware acceleration
pub fn initialize_hardware_acceleration() -> Result<(), Error> {
    // Detect available hardware
    let capabilities = HardwareCapabilities::detect();

    info!("Available hardware acceleration: {}", capabilities);

    // Initialize appropriate backends
    if capabilities.has_cuda {
        CudaBackend::initialize()?;
    }

    if capabilities.has_opencl {
        OpenCLBackend::initialize()?;
    }

    if capabilities.has_avx512 {
        Avx512Backend::initialize()?;
    } else if capabilities.has_avx2 {
        Avx2Backend::initialize()?;
    }

    if capabilities.has_tensor {
        TensorBackend::initialize()?;
    }

    Ok(())
}

Best Practices¶

For Developers¶

1. Always provide fallbacks
2. Every accelerated operation should have a pure software fallback

3. Use feature detection at runtime to select appropriate implementation

4. Benchmark realistically

5. Compare small, medium, and large workloads
6. Test on various hardware configurations

7. Consider real-world usage patterns

8. Balance security and performance

9. Security-critical operations should be carefully validated
10. Consider result verification for critical operations

For System Administrators¶

1. Hardware recommendations
2. Modern CPUs with AVX2/AVX512 support
3. CUDA-capable GPUs (NVIDIA RTX series recommended)

4. Ensure adequate cooling for sustained cryptographic operations

5. Configuration tuning

6. Adjust batch sizes based on available memory
7. Fine-tune resource allocation for specific workloads

8. Consider dedicated hardware for high-volume nodes

9. Monitoring

10. Track hardware resource utilization
11. Monitor for performance anomalies
12. Set up alerts for hardware failures

Troubleshooting¶

Common Issues and Solutions¶

Issue	Possible Causes	Solution
Acceleration not enabled	Missing runtime libraries	Install required CUDA/OpenCL libraries
Poor performance	Resource contention	Adjust max_resource_allocation setting
Incorrect results	Hardware compatibility issues	Enable verify_results setting
System instability	Overheating/power issues	Ensure adequate cooling and power supply
Memory errors	Insufficient GPU memory	Reduce batch sizes or upgrade hardware

Diagnostic Tools¶

# Check available hardware acceleration
anya-bitcoin diagnostics --check-hardware

# Run hardware acceleration benchmark
anya-bitcoin benchmark --hardware-acceleration

# Validate hardware acceleration results
anya-bitcoin validate --acceleration-results

Integration with Layer 2 Protocols¶

Hardware acceleration provides significant benefits for Layer 2 protocols:

Lightning Network¶

• Accelerated path finding for routing
• Batch validation of channel states
• Fast HTLC resolution

RGB Protocol¶

• Accelerated asset validation
• Efficient client-side validation

Discrete Log Contracts (DLCs)¶

• Fast multi-oracle verification
• Accelerated contract execution
• Batch signature verification for contract settlement

Security Considerations¶

For a complete discussion of security aspects, see Hardware Acceleration Security.

Key security points:

• Side-channel attack prevention
• Secure memory management
• Fallback mechanisms for hardware failures
• Validation of critical results

Related Documentation¶

• Taproot Integration Guide
• Hardware Acceleration Security
• Performance Optimization Guide
• Bitcoin Core Principles Alignment

Last updated: 2025-05-01