Splendor Blockchain

Traditional blockchain networks face fundamental scalability limitations, with Bitcoin processing ~7 TPS and Ethereum ~15 TPS. Even advanced Layer 1 solutions struggle to exceed 100,000 TPS while maintaining decentralization and security. The primary bottlenecks include:

• Sequential transaction processing limiting parallelization
• CPU-only computation underutilizing modern hardware
• Static resource allocation failing to adapt to varying workloads
• Fixed consensus parameters preventing dynamic optimization

Splendor introduces a revolutionary approach combining:

Scalability Trilemma:

Traditional blockchains face the impossible choice between scalability, security, and decentralization. Current solutions compromise one aspect to improve others.

Resource Underutilization:

Modern servers with 16+ CPU cores, 64GB+ RAM, and enterprise GPUs are severely underutilized by traditional blockchain software designed for single-threaded execution.

Static Optimization:

Existing blockchains use fixed parameters that cannot adapt to varying workloads, leading to suboptimal performance under different conditions.

The AI receives performance data and generates optimization recommendations every 500ms by default (configurable down to 250ms):

type PerformanceMetrics struct {
    Timestamp       time.Time
    TotalTPS        uint64    // Current transactions per second
    CPUUtilization  float64   // CPU usage percentage (0-1)
    GPUUtilization  float64   // GPU usage percentage (0-1)
    MemoryUsage     uint64    // System memory usage
    GPUMemoryUsage  uint64    // GPU memory usage
    AvgLatency      float64   // Average processing latency (ms)
    BatchSize       int       // Current batch size
    CurrentStrategy string    // Current processing strategy
    QueueDepth      int       // Transaction queue depth
}

CUDA kernels (e.g., for Keccak-256, signature verification) are integrated via cgo and loaded from a project-supplied shared library (e.g., libcuda_kernels). The repository declares the CUDA entry points; production builds provide optimized implementations tailored to the target GPU.

// CUDA kernel integration via cgo
/*
#cgo LDFLAGS: -L. -lcuda_kernels
#include "cuda_kernels.h"
*/
import "C"

// GPU acceleration entry points
func (p *Processor) ProcessBatchGPU(batch []Transaction) error {
    if !p.cudaAvailable {
        return p.ProcessBatchCPU(batch) // Automatic fallback
    }
    
    // Load kernels from shared library
    return C.process_batch_cuda(batch)
}

Default GPU configuration (OpenCL preferred on 20 GiB GPUs). Performance metrics are hardware-dependent and configurable:

Splendor implements a sophisticated DPoS consensus mechanism called "Congress" that combines the benefits of Proof-of-Stake with enterprise-grade performance and security.

Baseline stress tests achieved 80K–100K TPS, with peak block throughput at 824K and sustained performance up to 2.35M TPS on RTX 4000 Ada hardware.

Performance Multipliers with AI Load Balancing:

Hardware Performance Scaling (Based on RTX 4000 SFF Ada Baseline):