#!/usr/bin/env python3 """ GPU Performance Benchmarking Suite Tests GPU acceleration capabilities for AITBC mining and computation """ import pytest import torch import cupy as cp import numpy as np import time import json from typing import Dict, List, Tuple import pynvml # Initialize NVML for GPU monitoring try: pynvml.nvmlInit() NVML_AVAILABLE = True except: NVML_AVAILABLE = False class GPUBenchmarkSuite: """Comprehensive GPU benchmarking suite""" def __init__(self): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.results = {} def get_gpu_info(self) -> Dict: """Get GPU information""" info = { "pytorch_available": torch.cuda.is_available(), "pytorch_version": torch.__version__, "cuda_version": torch.version.cuda if torch.cuda.is_available() else None, "gpu_count": torch.cuda.device_count() if torch.cuda.is_available() else 0, } if torch.cuda.is_available(): info.update({ "gpu_name": torch.cuda.get_device_name(0), "gpu_memory": torch.cuda.get_device_properties(0).total_memory / 1e9, "gpu_compute_capability": torch.cuda.get_device_capability(0), }) if NVML_AVAILABLE: try: handle = pynvml.nvmlDeviceGetHandleByIndex(0) info.update({ "gpu_driver_version": pynvml.nvmlSystemGetDriverVersion().decode(), "gpu_temperature": pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU), "gpu_power_usage": pynvml.nvmlDeviceGetPowerUsage(handle) / 1000, # Watts "gpu_clock": pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_GRAPHICS), }) except: pass return info @pytest.mark.benchmark(group="matrix_operations") def test_matrix_multiplication_pytorch(self, benchmark): """Benchmark PyTorch matrix multiplication""" if not torch.cuda.is_available(): pytest.skip("CUDA not available") def matmul_op(): size = 2048 a = torch.randn(size, size, device=self.device) b = torch.randn(size, size, device=self.device) c = torch.matmul(a, b) return c result = benchmark(matmul_op) self.results['pytorch_matmul'] = { 'ops_per_sec': 1 / benchmark.stats['mean'], 'mean': benchmark.stats['mean'], 'std': benchmark.stats['stddev'] } return result @pytest.mark.benchmark(group="matrix_operations") def test_matrix_multiplication_cupy(self, benchmark): """Benchmark CuPy matrix multiplication""" try: def matmul_op(): size = 2048 a = cp.random.randn(size, size, dtype=cp.float32) b = cp.random.randn(size, size, dtype=cp.float32) c = cp.dot(a, b) return c result = benchmark(matmul_op) self.results['cupy_matmul'] = { 'ops_per_sec': 1 / benchmark.stats['mean'], 'mean': benchmark.stats['mean'], 'std': benchmark.stats['stddev'] } return result except: pytest.skip("CuPy not available") @pytest.mark.benchmark(group="mining_operations") def test_hash_computation_gpu(self, benchmark): """Benchmark GPU hash computation (simulated mining)""" if not torch.cuda.is_available(): pytest.skip("CUDA not available") def hash_op(): # Simulate hash computation workload batch_size = 10000 data = torch.randn(batch_size, 32, device=self.device) # Simple hash simulation hash_result = torch.sum(data, dim=1) hash_result = torch.abs(hash_result) # Additional processing processed = torch.sigmoid(hash_result) return processed result = benchmark(hash_op) self.results['gpu_hash_computation'] = { 'ops_per_sec': 1 / benchmark.stats['mean'], 'mean': benchmark.stats['mean'], 'std': benchmark.stats['stddev'] } return result @pytest.mark.benchmark(group="mining_operations") def test_proof_of_work_simulation(self, benchmark): """Benchmark proof-of-work simulation""" if not torch.cuda.is_available(): pytest.skip("CUDA not available") def pow_op(): # Simulate PoW computation nonce = torch.randint(0, 2**32, (1000,), device=self.device) data = torch.randn(1000, 64, device=self.device) # Hash simulation combined = torch.cat([nonce.float().unsqueeze(1), data], dim=1) hash_result = torch.sum(combined, dim=1) # Difficulty check difficulty = torch.tensor(0.001, device=self.device) valid = hash_result < difficulty return torch.sum(valid.float()).item() result = benchmark(pow_op) self.results['pow_simulation'] = { 'ops_per_sec': 1 / benchmark.stats['mean'], 'mean': benchmark.stats['mean'], 'std': benchmark.stats['stddev'] } return result @pytest.mark.benchmark(group="neural_operations") def test_neural_network_forward(self, benchmark): """Benchmark neural network forward pass""" if not torch.cuda.is_available(): pytest.skip("CUDA not available") # Simple neural network model = torch.nn.Sequential( torch.nn.Linear(784, 256), torch.nn.ReLU(), torch.nn.Linear(256, 128), torch.nn.ReLU(), torch.nn.Linear(128, 10) ).to(self.device) def forward_op(): batch_size = 64 x = torch.randn(batch_size, 784, device=self.device) output = model(x) return output result = benchmark(forward_op) self.results['neural_forward'] = { 'ops_per_sec': 1 / benchmark.stats['mean'], 'mean': benchmark.stats['mean'], 'std': benchmark.stats['stddev'] } return result @pytest.mark.benchmark(group="memory_operations") def test_gpu_memory_bandwidth(self, benchmark): """Benchmark GPU memory bandwidth""" if not torch.cuda.is_available(): pytest.skip("CUDA not available") def memory_op(): size = 100_000_000 # 100M elements # Allocate and copy data a = torch.randn(size, device=self.device) b = torch.randn(size, device=self.device) # Memory operations c = a + b d = c * 2.0 return d result = benchmark(memory_op) self.results['memory_bandwidth'] = { 'ops_per_sec': 1 / benchmark.stats['mean'], 'mean': benchmark.stats['mean'], 'std': benchmark.stats['stddev'] } return result @pytest.mark.benchmark(group="crypto_operations") def test_encryption_operations(self, benchmark): """Benchmark GPU encryption operations""" if not torch.cuda.is_available(): pytest.skip("CUDA not available") def encrypt_op(): # Simulate encryption workload batch_size = 1000 key_size = 256 data_size = 1024 # Generate keys and data keys = torch.randn(batch_size, key_size, device=self.device) data = torch.randn(batch_size, data_size, device=self.device) # Simple encryption simulation encrypted = torch.matmul(data, keys.T) / 1000.0 decrypted = torch.matmul(encrypted, keys) / 1000.0 return torch.mean(torch.abs(data - decrypted)) result = benchmark(encrypt_op) self.results['encryption_ops'] = { 'ops_per_sec': 1 / benchmark.stats['mean'], 'mean': benchmark.stats['mean'], 'std': benchmark.stats['stddev'] } return result def save_results(self, filename: str): """Save benchmark results to file""" gpu_info = self.get_gpu_info() results_data = { "timestamp": time.time(), "gpu_info": gpu_info, "benchmarks": self.results } with open(filename, 'w') as f: json.dump(results_data, f, indent=2) # Test instance benchmark_suite = GPUBenchmarkSuite() # Pytest fixture for setup @pytest.fixture(scope="session") def gpu_benchmark(): return benchmark_suite # Save results after all tests def pytest_sessionfinish(session, exitstatus): """Save benchmark results after test completion""" try: benchmark_suite.save_results('gpu_benchmark_results.json') except Exception as e: print(f"Failed to save benchmark results: {e}") if __name__ == "__main__": # Run benchmarks directly import sys sys.exit(pytest.main([__file__, "-v", "--benchmark-only"]))