tahamajs's picture
download
raw
5.08 kB
#!/usr/bin/env python3
import sys
import os
import logging
from pathlib import Path
sys.path.append(str(Path(__file__).parent / "src"))
def main():
print("๐Ÿ”ง CA20: Advanced Memory Systems in AI - Starting Analysis")
print("=" * 60)
try:
from memory_profiler import AdvancedMemoryProfiler
from cache_algorithms import (
CacheAwareAlgorithms,
benchmark_matrix_algorithms,
benchmark_convolution_algorithms,
)
from neural_networks import (
analyze_model_memory_efficiency,
demonstrate_memory_optimizations,
)
from benchmarking import MemoryAccessAnalyzer, run_comprehensive_benchmarks
from visualization import create_comprehensive_analysis, generate_final_report
from utils import setup_logging, load_config, create_output_directories
from advanced_memory import benchmark_advanced_memory_systems
from distributed_memory import benchmark_distributed_memory_systems
from advanced_simulators import benchmark_advanced_simulators
from advanced_visualization import (
create_advanced_visualizations,
generate_interactive_report,
)
from ai_memory_systems import benchmark_advanced_ai_memory_systems
from ai_reporting import benchmark_advanced_reporting_system
from workflow_manager import benchmark_workflow_system
print("๐Ÿ“‹ Loading configuration...")
config = load_config("config.yaml")
logger = setup_logging(config)
create_output_directories(config)
print("๐Ÿ”ง Environment Setup Complete!")
print(f"Python version: {sys.version}")
print(f"Available CPU cores: {os.cpu_count()}")
results = {}
print("\n๐Ÿš€ Starting Memory Access Pattern Analysis...")
print("-" * 50)
results["spatial_results"] = MemoryAccessAnalyzer.analyze_spatial_locality(
matrix_size=1024
)
results["temporal_results"] = MemoryAccessAnalyzer.analyze_temporal_locality(
data_size=512 * 1024
)
results["cache_simulation"] = MemoryAccessAnalyzer.cache_simulation_analysis()
print("\n๐Ÿš€ Starting Cache-Aware Algorithm Benchmarks...")
print("-" * 50)
results["matrix_results"] = benchmark_matrix_algorithms(
matrix_sizes=[128, 256, 512]
)
results["convolution_results"] = benchmark_convolution_algorithms()
print("\n๐Ÿš€ Starting Memory-Efficient Neural Network Analysis...")
print("-" * 50)
results["model_analysis"] = analyze_model_memory_efficiency()
results["optimization_results"] = demonstrate_memory_optimizations()
print("\n๐Ÿš€ Starting Advanced Memory Systems Analysis...")
print("-" * 50)
results["advanced_memory"] = benchmark_advanced_memory_systems()
print("\n๐Ÿš€ Starting Distributed Memory Systems Analysis...")
print("-" * 50)
results["distributed_memory"] = benchmark_distributed_memory_systems()
print("\n๐Ÿš€ Starting Advanced Simulators Analysis...")
print("-" * 50)
results["advanced_simulators"] = benchmark_advanced_simulators()
print("\n๐Ÿš€ Creating Comprehensive Memory Analysis Visualization...")
print("-" * 50)
create_comprehensive_analysis(results, "visualizations")
print("\n๐Ÿš€ Creating Advanced Interactive Visualizations...")
print("-" * 50)
create_advanced_visualizations(results, "visualizations")
print("\n๐Ÿ“Š Generating Final Report...")
print("-" * 50)
generate_final_report(results, "visualizations")
print("\n๐Ÿ“Š Generating Interactive Report...")
print("-" * 50)
generate_interactive_report(results, "visualizations")
print("\n๐Ÿค– Starting AI-Powered Memory Analysis...")
print("-" * 50)
results["ai_memory_analysis"] = benchmark_advanced_ai_memory_systems()
print("\n๐Ÿค– Starting AI-Powered Reporting...")
print("-" * 50)
results["ai_reporting"] = benchmark_advanced_reporting_system()
print("\n๐Ÿ”„ Starting Workflow Management Analysis...")
print("-" * 50)
results["workflow_analysis"] = benchmark_workflow_system()
print("\n" + "=" * 60)
print("โœ… CA20 Analysis Complete!")
print("๐Ÿ“‹ All results saved to visualizations/ directory")
print("๐Ÿ“Š Check visualizations/final_report.txt for detailed summary")
print("=" * 60)
return True
except ImportError as e:
print(f"โŒ Import Error: {e}")
print("Please ensure all dependencies are installed:")
print("pip install -r requirements.txt")
return False
except Exception as e:
print(f"โŒ Error during execution: {e}")
logging.exception("Unexpected error occurred")
return False
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

Xet Storage Details

Size:
5.08 kB
ยท
Xet hash:
1469f189802e056e04d911c1053ad9b30a72fb0309f258a623bf3dc07b36ba50

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.