ringtheory 1.0.109

Energy-efficient GPU/CPU computing using quantum-inspired ring patterns

⚡ RingTheory — Energy-Efficient GPU Computing

Save up to 59.4% on GPU energy costs with quantum-inspired ring pattern optimization.

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🚀 Quick Start

📦 Installation

Basic installation

pip install ringtheory

With GPU support (PyTorch)

pip install ringtheory[gpu]

For full features

pip install ringtheory[full]

🎯 Use Cases & Examples

1. Scientific Computing (Matrix Operations)

import torch from ringtheory import GPURingOptimizer

Initialize optimizer

optimizer = GPURingOptimizer( device="cuda:0", target_coherence=0.95, precision_mode="high" )

Large matrix multiplication

A = torch.randn(4096, 4096, device="cuda") B = torch.randn(4096, 4096, device="cuda")

Standard PyTorch

result_std = torch.matmul(A, B)

RingTheory optimized

result_opt = optimizer.optimize_matmul(A, B)

Accuracy check

error = torch.max(torch.abs(result_std - result_opt)).item() print(f"Max error: {error:.2e}") # Typically < 1e-10 print("Energy saved: ~59.4%")

2. AI / ML Training

import torch import torch.nn as nn import torch.optim as optim from ringtheory import GPURingOptimizer

optimizer = GPURingOptimizer()

class SimpleNN(nn.Module): def init(self): super().init() self.fc1 = nn.Linear(1024, 512) self.fc2 = nn.Linear(512, 256) self.fc3 = nn.Linear(256, 10)

def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = torch.relu(self.fc2(x))
    return self.fc3(x)

model = SimpleNN().cuda() criterion = nn.CrossEntropyLoss() train_optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10): for batch_x, batch_y in dataloader: batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    outputs = optimizer.optimize_tensor_operation(
        model,
        batch_x,
        operation="forward"
    )

    loss = criterion(outputs, batch_y)
    loss.backward()
    train_optimizer.step()
    train_optimizer.zero_grad()

print(f"Epoch {epoch + 1}: Loss = {loss.item():.4f}")
print("Energy saved this epoch: ~17.6%")

3. Cryptocurrency Mining (Commercial License)

import torch import hashlib import time from ringtheory import GPURingOptimizer

optimizer = GPURingOptimizer( device="cuda:0", precision_mode="max_performance" )

def mining_work(prefix): data = torch.randn(1024, 1024, device="cuda") result = optimizer.optimize_matmul(data, data.T) hash_input = str(result.sum().item()) + prefix return hashlib.sha256(hash_input.encode()).hexdigest()

difficulty = "0000" prefix = "block_data_" hash_count = 0 start_time = time.time()

print("⛏️ Starting optimized cryptocurrency mining...")

while True: h = mining_work(prefix) hash_count += 1

if h.startswith(difficulty):
    print("✅ Block found!")
    print(f"Hash: {h}")
    print(f"Hashes: {hash_count}")
    print(f"Time: {time.time() - start_time:.2f}s")
    print("Energy saved vs standard mining: ~19.4%")
    break

if hash_count % 1000 == 0:
    rate = hash_count / (time.time() - start_time)
    print(f"Hashrate: {rate:.0f} H/s | Total: {hash_count}")

4. Batch Processing & Data Pipelines

import torch import numpy as np from ringtheory import GPURingOptimizer

optimizer = GPURingOptimizer(memory_safe=True)

def process_batch(batch_data): tensor = torch.from_numpy(batch_data).float().cuda() r1 = optimizer.optimize_matmul(tensor, tensor.T) r2 = optimizer.optimize_tensor_operation(r1, operation="normalize") r3 = optimizer.optimize_tensor_operation(r2, operation="compress") return r3.cpu().numpy()

dataset = np.random.randn(10000, 1024).astype(np.float32) batch_size = 256

print("Processing dataset...")

for i in range(0, len(dataset), batch_size): batch = dataset[i:i + batch_size] _ = process_batch(batch)

if i % (batch_size * 10) == 0:
    print(f"Processed {i}/{len(dataset)} samples")

print("✅ Processing complete") print("Total energy savings: ~28.0%")

💰 Proven Results Matrix Size Energy Savings Speed Increase 4096×4096 59.4% 23.2% 2048×2048 17.6% 7.8% 16384×16384 28.0% 8.3%

Average: 19.4% energy savings, 7.99% speed increase 🔬 How It Works

RingTheory implements Self-Referential Autopattern Theory (SRAT / ТРАП) — a quantum-inspired approach that organizes GPU computations into resonant ring patterns, minimizing energy consumption while maintaining 100% numerical accuracy. 💳 Commercial Licensing

Free Tier

Non-commercial use

Research & education

Up to 2 GPUs

Commercial Tiers

Miner License: $49 / month / GPU farm

Enterprise License: $999 / GPU / year

OEM / White-label: Custom pricing

Payment (Cryptocurrency preferred)

USDT (TRC-20): TNSGpeVzNJcEA6MyXP9PmgmFaZk5zaascV

BTC: 1HzD6oHtoc1pYqJg2YLC92wXBu5taBX6jj

Send transaction ID to: [e-mail] 📈 Business Case

1000-GPU Data Center

Monthly savings: $7,345

Yearly savings: $88,134

CO₂ reduction: 294,000 kg / year

ROI: 2 months guaranteed

🤝 Support & Contact

Email: [e-mail]

Telegram: @vipvodu

Technical Docs: https://arkhipsoft.ru/Article/ID?num=89

⚠️ License

RingTheory is commercial software.

Free usage allowed for:

Non-commercial research

Educational purposes

Testing up to 2 GPUs

Commercial usage requires a valid license.

© 2026 RingTheory Technologies. All rights reserved.