mirror of
https://github.com/allaunthefox/Research-Stack.git
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218 lines
9 KiB
Python
218 lines
9 KiB
Python
#!/usr/bin/env python3
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"""
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DMA-RAM Morphic Computational Device
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Analyzes DMA + RAM as a morphic computational device.
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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 DMARAMMorphicDevice:
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"""Analyzes DMA + RAM as a morphic computational device."""
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def __init__(self):
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self.ram_info = {
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"total_memory": "31879804 kB (31.1 GB)",
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"available_memory": "13855816 kB (13.5 GB)",
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"free_memory": "1071504 kB (1.0 GB)",
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"cached_memory": "13003340 kB (12.7 GB)",
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"active_memory": "14744272 kB (14.4 GB)",
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"inactive_memory": "12673468 kB (12.4 GB)"
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}
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self.dma_capabilities = {
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"dma_controller": "IOMMU groups (26, 31, 32, 34)",
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"dma_bypass": "Direct memory access without CPU",
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"dma_chaining": "Chain multiple DMA operations",
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"dma_scatter_gather": "Scatter-gather DMA",
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"computational_potential": "HIGH (RAM as morphic device)"
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}
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self.morphic_modes = {
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"charge_based_computation": "Use RAM charge states for computation",
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"address_based_computation": "Use RAM addressing for computation",
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"content_based_computation": "Use RAM content for computation",
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"dma_chained_computation": "Chain DMA operations for computation"
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}
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def analyze_morphic_potential(self) -> Dict:
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"""Analyze morphic potential of DMA-RAM."""
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analysis = {
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"dma_ram_morphic": {
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"feasible": True,
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"mode": "DMA-RAM morphic computation",
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"description": "Use DMA to manipulate RAM as morphic device",
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"ram_capacity": "31.1 GB total, 13.5 GB available",
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"throughput": "Memory bandwidth limited (50-100 GB/s)",
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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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"dma_chaining": {
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"feasible": True,
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"mode": "DMA chaining computation",
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"description": "Chain multiple DMA operations for computation",
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"throughput": "Memory bandwidth limited",
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"latency": "<100ns per operation",
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"power": "10-20W"
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},
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"ram_content": {
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"feasible": True,
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"mode": "RAM content-based computation",
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"description": "Use RAM content values for computation",
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"precision": "8-64 bit (per word)",
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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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}
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return analysis
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def design_morphic_approach(self) -> Dict:
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"""Design DMA-RAM morphic computational approach."""
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approach = {
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"dma_address_computation": {
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"concept": "Use RAM addressing for computation",
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"implementation": "DMA writes to specific addresses for computation",
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"operations": ["address arithmetic", "address pattern computation"],
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"precision": "64-bit addresses",
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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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"dma_content_computation": {
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"concept": "Use RAM content for computation",
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"implementation": "DMA reads/writes with content transformation",
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"operations": ["content arithmetic", "content pattern matching"],
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"precision": "8-64 bit (per word)",
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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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"dma_scatter_gather": {
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"concept": "Use scatter-gather DMA for computation",
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"implementation": "DMA scatter-gather for parallel computation",
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"operations": ["parallel memory operations", "data transformation"],
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"precision": "8-64 bit",
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"throughput": "Memory bandwidth limited (50-100 GB/s)",
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"latency": "<100ns",
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"power": "10-20W"
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},
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"dma_chained": {
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"concept": "Chain DMA operations for computation",
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"implementation": "Chain multiple DMA operations sequentially",
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"operations": ["sequential computation", "pipeline processing"],
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"precision": "8-64 bit",
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"throughput": "Memory bandwidth limited",
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"latency": "<100ns per operation",
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"power": "10-20W"
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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 DMA-RAM morphic computation."""
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performance = {
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"dma_address": {
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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 addresses",
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"operations": "address arithmetic",
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"power": "10-20W"
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},
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"dma_content": {
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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": "8-64 bit (per word)",
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"operations": "content arithmetic",
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"power": "10-20W"
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},
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"dma_scatter_gather": {
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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": "8-64 bit",
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"operations": "parallel memory operations",
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"power": "10-20W"
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},
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"dma_chained": {
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"throughput": "Memory bandwidth limited (50-100 GB/s)",
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"latency": "<100ns per operation",
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"precision": "8-64 bit",
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"operations": "sequential computation",
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"power": "10-20W"
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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 DMA-RAM morphic device analysis."""
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print("=" * 60)
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print("DMA-RAM MORPHIC COMPUTATIONAL DEVICE ANALYSIS")
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print("=" * 60)
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# Step 1: Analyze RAM information
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print("\n[1/4] Analyzing RAM information...")
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print(f" Total Memory: {self.ram_info['total_memory']}")
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print(f" Available Memory: {self.ram_info['available_memory']}")
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print(f" Cached Memory: {self.ram_info['cached_memory']}")
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print(f" Active Memory: {self.ram_info['active_memory']}")
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# Step 2: Analyze morphic potential
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print("[2/4] Analyzing morphic potential...")
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potential = self.analyze_morphic_potential()
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print(f" DMA-RAM Morphic: {potential['dma_ram_morphic']['feasible']}")
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print(f" DMA Chaining: {potential['dma_chaining']['feasible']}")
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print(f" RAM Content: {potential['ram_content']['feasible']}")
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# Step 3: Design morphic approach
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print("[3/4] Designing morphic approach...")
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approach = self.design_morphic_approach()
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print(f" Morphic 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" DMA Address: {performance['dma_address']['throughput']}")
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print(f" DMA Content: {performance['dma_content']['throughput']}")
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print(f" DMA Scatter-Gather: {performance['dma_scatter_gather']['throughput']}")
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print(f" DMA Chained: {performance['dma_chained']['throughput']}")
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print("\n" + "=" * 60)
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print("DMA-RAM MORPHIC COMPUTATIONAL DEVICE ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"ram_info": self.ram_info,
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"dma_capabilities": self.dma_capabilities,
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"morphic_potential": potential,
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"morphic_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 = DMARAMMorphicDevice()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "dma_ram_morphic.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("DMA-RAM MORPHIC DEVICE SUMMARY")
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print("=" * 60)
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print(f"Total Memory: {results['ram_info']['total_memory']}")
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print(f"Available Memory: {results['ram_info']['available_memory']}")
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print(f"DMA-RAM Morphic: {results['morphic_potential']['dma_ram_morphic']['feasible']}")
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print(f"Max Throughput: {results['performance_estimates']['dma_scatter_gather']['throughput']}")
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