mirror of
https://github.com/allaunthefox/Research-Stack.git
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236 lines
10 KiB
Python
236 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Motherboard Computational Analysis
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Analyzes motherboard travel paths, IRQ controller, and chipset for computation.
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"""
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import json
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from pathlib import Path
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from typing import Dict, List, Optional
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# Paths
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OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")
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class MotherboardComputational:
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"""Analyzes motherboard components for computation."""
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def __init__(self):
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self.motherboard_components = {
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"host_bridges": {
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"root_complex": "Raphael/Granite Ridge Root Complex (00:00.0)",
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"dummy_bridges": "Multiple Dummy Host Bridges (00:01.0, 00:02.0, 00:03.0, etc.)",
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"data_fabric": "Data Fabric Functions 0-7 (00:18.0-00:18.7)",
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"computational_potential": "HIGH (data fabric for memory access)"
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},
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"pci_bridges": {
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"gpp_bridges": "GPP Bridges (00:01.1, 00:01.2, 00:02.1, 00:08.1, 00:08.3)",
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"pcie_switch": "PCIe Switch Upstream/Downstream Ports (03:00.0, 04:00.0-04:0d.0)",
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"total_bridges": 15,
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"computational_potential": "MEDIUM (PCIe lane switching)"
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},
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"isa_bridge": {
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"lpc_bridge": "FCH LPC Bridge (00:14.3)",
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"computational_potential": "MEDIUM (legacy I/O access)"
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},
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"irq_controller": {
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"local_apic": "Local APIC (LOC interrupts: 16.4M/sec)",
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"io_apic": "I/O APIC (device interrupts)",
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"msi": "MSI/MSI-X (PCIe device interrupts)",
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"computational_potential": "HIGH (interrupt-driven computation)"
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},
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"data_fabric": {
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"functions": "8 functions (0-7)",
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"purpose": "Memory access and interconnect",
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"computational_potential": "HIGH (memory-based computation)"
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}
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}
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self.computational_modes = {
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"interrupt_driven": "Use interrupt patterns for computation",
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"data_fabric": "Use data fabric for memory-based computation",
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"pcie_lane_switching": "Use PCIe bridge switching for computation",
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"isa_bridge": "Use legacy I/O for computation",
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"host_bridge": "Use root complex for routing computation"
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}
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def analyze_computational_potential(self) -> Dict:
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"""Analyze computational potential of motherboard components."""
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analysis = {
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"interrupt_controller": {
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"feasible": True,
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"mode": "Interrupt-driven computation",
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"description": "Use interrupt patterns (LOC, CAL, TLB) for computation",
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"throughput": "16.4M interrupts/sec (LOC)",
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"latency": "<1µs (interrupt)",
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"power": "5-10W (chipset)"
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},
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"data_fabric": {
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"feasible": True,
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"mode": "Data fabric computation",
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"description": "Use data fabric for memory-based computation",
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"throughput": "Memory bandwidth limited",
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"latency": "<100ns (memory access)",
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"power": "10-20W (memory controller)"
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},
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"pcie_bridges": {
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"feasible": True,
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"mode": "PCIe lane switching computation",
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"description": "Use PCIe bridge switching for computation",
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"throughput": "PCIe bandwidth limited",
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"latency": "100-1000ns (bridge traversal)",
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"power": "5-15W (PCIe controller)"
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},
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"isa_bridge": {
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"feasible": True,
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"mode": "Legacy I/O computation",
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"description": "Use ISA bridge for legacy I/O computation",
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"throughput": "I/O port limited",
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"latency": "1-10µs (I/O access)",
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"power": "1-5W (LPC bridge)"
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}
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}
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return analysis
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def design_computational_approach(self) -> Dict:
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"""Design motherboard-based computational approach."""
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approach = {
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"interrupt_pattern_computation": {
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"concept": "Use interrupt patterns for computation",
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"implementation": "Trigger computation on specific interrupt patterns",
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"operations": ["LOC pattern analysis", "CAL pattern analysis", "TLB pattern analysis"],
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"throughput": "16.4M interrupts/sec (LOC)",
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"latency": "<1µs",
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"power": "5-10W"
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},
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"data_fabric_computation": {
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"concept": "Use data fabric for memory-based computation",
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"implementation": "Access memory via data fabric for computation",
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"operations": ["memory access patterns", "interconnect computation"],
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"throughput": "Memory bandwidth limited",
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"latency": "<100ns",
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"power": "10-20W"
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},
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"pcie_switching_computation": {
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"concept": "Use PCIe bridge switching for computation",
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"implementation": "Switch PCIe lanes for computational routing",
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"operations": ["lane switching", "routing computation"],
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"throughput": "PCIe bandwidth limited",
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"latency": "100-1000ns",
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"power": "5-15W"
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},
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"isa_io_computation": {
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"concept": "Use ISA bridge for I/O computation",
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"implementation": "Access I/O ports via ISA bridge",
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"operations": ["I/O port access", "legacy device access"],
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"throughput": "I/O port limited",
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"latency": "1-10µs",
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"power": "1-5W"
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}
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}
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return approach
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def estimate_performance(self) -> Dict:
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"""Estimate performance of motherboard computation."""
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performance = {
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"interrupt_controller": {
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"throughput": "16.4M interrupts/sec (LOC)",
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"latency": "<1µs (interrupt)",
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"precision": "Interrupt pattern",
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"operations": "interrupt pattern analysis",
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"power": "5-10W"
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},
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"data_fabric": {
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"throughput": "Memory bandwidth limited (50-100 GB/s)",
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"latency": "<100ns (memory access)",
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"precision": "64-bit memory",
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"operations": "memory access patterns",
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"power": "10-20W"
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},
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"pcie_bridges": {
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"throughput": "PCIe bandwidth limited (32-64 GB/s)",
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"latency": "100-1000ns (bridge traversal)",
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"precision": "PCIe packet",
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"operations": "lane switching",
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"power": "5-15W"
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},
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"isa_bridge": {
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"throughput": "I/O port limited (1-10 MB/s)",
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"latency": "1-10µs (I/O access)",
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"precision": "8-32 bit I/O",
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"operations": "I/O port access",
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"power": "1-5W"
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}
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}
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return performance
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def run_analysis(self) -> Dict:
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"""Run motherboard computational analysis."""
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print("=" * 60)
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print("MOTHERBOARD COMPUTATIONAL ANALYSIS")
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print("=" * 60)
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# Step 1: Analyze motherboard components
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print("\n[1/4] Analyzing motherboard components...")
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print(f" Host Bridges: {self.motherboard_components['host_bridges']['computational_potential']}")
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print(f" PCI Bridges: {len(self.motherboard_components['pci_bridges'])} bridges")
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print(f" ISA Bridge: {self.motherboard_components['isa_bridge']['computational_potential']}")
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print(f" IRQ Controller: {self.motherboard_components['irq_controller']['computational_potential']}")
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print(f" Data Fabric: {self.motherboard_components['data_fabric']['computational_potential']}")
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# Step 2: Analyze computational potential
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print("[2/4] Analyzing computational potential...")
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potential = self.analyze_computational_potential()
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print(f" Interrupt Controller: {potential['interrupt_controller']['feasible']}")
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print(f" Data Fabric: {potential['data_fabric']['feasible']}")
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print(f" PCIe Bridges: {potential['pcie_bridges']['feasible']}")
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print(f" ISA Bridge: {potential['isa_bridge']['feasible']}")
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# Step 3: Design computational approach
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print("[3/4] Designing computational approach...")
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approach = self.design_computational_approach()
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print(f" Computational modes: {len(approach)}")
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for mode, details in approach.items():
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print(f" {mode}: {details['throughput']}")
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# Step 4: Estimate performance
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print("[4/4] Estimating performance...")
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performance = self.estimate_performance()
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print(f" Interrupt Controller: {performance['interrupt_controller']['throughput']}")
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print(f" Data Fabric: {performance['data_fabric']['throughput']}")
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print(f" PCIe Bridges: {performance['pcie_bridges']['throughput']}")
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print(f" ISA Bridge: {performance['isa_bridge']['throughput']}")
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print("\n" + "=" * 60)
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print("MOTHERBOARD COMPUTATIONAL ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"motherboard_components": self.motherboard_components,
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"computational_potential": potential,
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"computational_approach": approach,
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"performance_estimates": performance
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}
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if __name__ == '__main__':
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analyzer = MotherboardComputational()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "motherboard_computational.json"
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with open(output_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\nAnalysis results saved to {output_file}")
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# Print summary
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print("\n" + "=" * 60)
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print("MOTHERBOARD COMPUTATIONAL SUMMARY")
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print("=" * 60)
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print(f"Host Bridges: {results['motherboard_components']['host_bridges']['computational_potential']}")
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print(f"PCI Bridges: {results['motherboard_components']['pci_bridges']['total_bridges']}")
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print(f"IRQ Controller: {results['motherboard_components']['irq_controller']['computational_potential']}")
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print(f"Data Fabric: {results['motherboard_components']['data_fabric']['computational_potential']}")
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print(f"Max Throughput: {results['performance_estimates']['data_fabric']['throughput']}")
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