CoreMathOperations: This class contains static methods for core mathematical operations.
MathCache: Stores mathematical formulas and provides methods to add and retrieve them.
Modular Hardware Classes: Define different processing units (CPU, TPU, GPU, etc.) with embedded math and modular cache.
APICache and WebsiteCache: Handle API and website integration.
WebDataFetcher and DataProcessor: Classes to fetch and process web data.
TaskScheduler: Advanced task scheduling using machine learning.
DataCommunication: Manages data transfer between processors.
PowerManagement: Manages power consumption.
ControlUnit: Integrates all components and manages task distribution.
import numpy as np
import tensorflow as tf
import cupy as cp
from sklearn.ensemble import RandomForestRegressor
import requests
Core mathematical operations embedded within hardware components
class CoreMathOperations:
@staticmethod
def tensor_product(A, B):
return np.tensordot(A, B, axes=0)
@staticmethod
def modular_multiplication(A, B, mod):
return (A * B) % mod
@staticmethod
def krull_dimension(matrix):
return np.linalg.matrix_rank(matrix)
class DataCommunication:
def init(self, bandwidth):
self.bandwidth = bandwidth # Bandwidth in Gbps
def transfer_data(self, data_size):
transfer_time = data_size / self.bandwidth # Simplified transfer time calculation
return transfer_time
def optimize_transfer(self, data_size, processors):
# Distribute data to processors in a way that minimizes transfer time
transfer_times = [self.transfer_data(data_size / len(processors)) for _ in processors]
return max(transfer_times)
Power Management
class PowerManagement:
def init(self):
self.power_states = {'high': 100, 'medium': 50, 'low': 10} # Power consumption in watts
def set_power_state(self, processor, state):
if state in self.power_states:
processor.power = self.power_states[state]
else:
raise ValueError("Invalid power state")
def optimize_power(self, processors, performance_requirements):
for processor, requirement in zip(processors, performance_requirements):
if requirement > 0.75:
self.set_power_state(processor, 'high')
elif requirement > 0.25:
self.set_power_state(processor, 'medium')
else:
self.set_power_state(processor, 'low')
# Add various processing units to control_unit
math_cache = MathCache()
control_unit.add_cpu(ModularCPU(0, math_cache))
control_unit.add_tpu(ModularTPU(0, math_cache))
control_unit.add_gpu(ModularGPU(0, math_cache))
control_unit.add_lpu(ModularLPU(0, math_cache))
control_unit.add_fpga(ModularFPGA(0, math_cache))
for i in range(10):
control_unit.add_neuromorphic(NeuromorphicProcessor(i, math_cache))
control_unit.add_quantum(QuantumProcessor(0, math_cache))
# Add API and web integrations
control_unit.api_cache.add_api_call("example_api", lambda: "API response")
control_unit.web_cache.add_web_call("example_web", lambda: "Website response")
# Example data to process
data = np.array([1, 2, 3, 4, 5])
formula_name = "tensor_product"
# Distribute tasks to processing units with different configurations
result, transfer_time = control_unit.distribute_tasks(data, formula_name)
print(f"Result: {result}, Transfer Time: {transfer_time}")
# Fetch and process web data
fetcher = WebDataFetcher("https://api.example.com/data")
web_data = fetcher.fetch_data()
processor = DataProcessor(control_unit)
processed_results = processor.process_web_data(web_data)
for result in processed_results:
print(result)
CoreMathOperations: This class contains static methods for core mathematical operations. MathCache: Stores mathematical formulas and provides methods to add and retrieve them. Modular Hardware Classes: Define different processing units (CPU, TPU, GPU, etc.) with embedded math and modular cache. APICache and WebsiteCache: Handle API and website integration. WebDataFetcher and DataProcessor: Classes to fetch and process web data. TaskScheduler: Advanced task scheduling using machine learning. DataCommunication: Manages data transfer between processors. PowerManagement: Manages power consumption. ControlUnit: Integrates all components and manages task distribution.
import numpy as np import tensorflow as tf import cupy as cp from sklearn.ensemble import RandomForestRegressor import requests
Core mathematical operations embedded within hardware components
class CoreMathOperations: @staticmethod def tensor_product(A, B): return np.tensordot(A, B, axes=0)
Hardwired Cache for Mathematical Operations
class MathCache: def init(self): self.formulas = { "tensor_product": CoreMathOperations.tensor_product, "modular_multiplication": CoreMathOperations.modular_multiplication, "krull_dimension": CoreMathOperations.krull_dimension,
Add more formulas as needed
Modular hardware components with embedded math and modular cache
class ModularCPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache
class ModularTPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache
class ModularGPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache
class ModularLPU: def init(self, id, math_cache): self.id = id self.math_cache = math_cache
class ModularFPGA: def init(self, id, math_cache): self.id = id self.configurations = {} self.math_cache = math_cache
class NeuromorphicProcessor: def init(self, id, math_cache): self.id = id self.math_cache = math_cache
class QuantumProcessor: def init(self, id, math_cache): self.id = id self.math_cache = math_cache
Hardwired Cache for API and Website Integration
class APICache: def init(self): self.api_calls = {}
class WebsiteCache: def init(self): self.web_calls = {}
Web Data Fetcher
class WebDataFetcher: def init(self, url): self.url = url
Data Processor
class DataProcessor: def init(self, control_unit): self.control_unit = control_unit
Advanced Task Scheduling
class TaskScheduler: def init(self, cpu_units, tpu_units, gpu_units, lpu_units, fpga_units, neuromorphic_units, quantum_units): self.cpu_units = cpu_units self.tpu_units = tpu_units self.gpu_units = gpu_units self.lpu_units = lpu_units self.fpga_units = fpga_units self.neuromorphic_units = neuromorphic_units self.quantum_units = quantum_units self.model = RandomForestRegressor()
Enhanced Data Communication
class DataCommunication: def init(self, bandwidth): self.bandwidth = bandwidth # Bandwidth in Gbps
Power Management
class PowerManagement: def init(self): self.power_states = {'high': 100, 'medium': 50, 'low': 10} # Power consumption in watts
Control unit to manage tasks and integrate caches
class ControlUnit: def init(self): self.cpu_units = [] self.tpu_units = [] self.gpu_units = [] self.lpu_units = [] self.fpga_units = [] self.neuromorphic_units = [] self.quantum_units = [] self.math_cache = MathCache() self.api_cache = APICache() self.web_cache = WebsiteCache() self.scheduler = TaskScheduler(self.cpu_units, self.tpu_units, self.gpu_units, self.lpu_units, self.fpga_units, self.neuromorphic_units, self.quantum_units) self.communication = DataCommunication(bandwidth=100) # Example bandwidth self.power_manager = PowerManagement()
Example usage
if name == "main": control_unit = ControlUnit()