Application you need to install/
CUDNN v9.6/ https://developer.download.nvidia.com/compute/cudnn/9.6.0/local_installers/cudnn_9.6.0_windows.exe
NVIDIA CUDA 12.6 SDK/ https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_561.17_windows.exe
WINDOWS TOOLKIT SDK version 10.0.26100.0/ https://go.microsoft.com/fwlink/?linkid=2305205
NODE/ https://nodejs.org/dist/v22.14.0/node-v22.14.0-x64.msi
OLLAMA.exe https://ollama.com/download/OllamaSetup.exe
Including model qwen2-math:7b
Open PowerShell after download, enter command "qwen2-math-7b" without the quotes, the model will download.
OPEN CV, but it's already migrated to run with the application folder, will be in MEGAUPLOAD, JUST ADD IT TO THE SOURCE FOLDER/
cURL, but it's already migrated to run with the application folder, will be in MEGAUPLOAD, JUST ADD IT TO THE SOURCE FOLDER/
Complete folder will be uploaded to MEGAUPLOAD link soon.
Once you download the Armageddon2 DLLs from MEGAUPLOADS, COPY AND PASTE IT TO YOUR SYSTEM32 FOLDER.
THIS IMPORTANT!!!! BEFORE TRYING TO ATTEMPT TO RUN THE PROGRAM, ITERATE THROUGH ALL YOUR INCLUDE DIRS ETC IN PROJECT SETTINGS. ANY DIR CONTAINING THIS C:\Users\KrunkCiti\Desktop\Armageddon2\Amageddon2\vcpkg\installed\x64-windows\include FOR EXAMPLE CHANGE YOUR DESKTOP NAME AFTER \USER\
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MAKE SURE ALL OF YOUR ENVIRONMENTS ARE SET UP....
This should be in your user variable.
ChocolateyLastPathUpdate=133851217185057825
Path=C:\Program Files\NVIDIA\CUDNN\v9.6\bin\12.6
Path=C:\Users"YOURDESKTOPNAME"\Desktop\Armageddon2\Amageddon2\x64\Debug\opencv\build\bin
Path=C:\Users"YOURDESKTOPNAME"\Desktop\Armageddon2\Amageddon2\packages\Microsoft.Direct3D.D3D12.1.614.1\build\native\bin\x64
Path=C:\Users"YOURDESKTOPNAME"\Desktop\Armageddon2\Amageddon2\Armageddon2 DLLs\node-v22.13.1-win-x64\cli-11.1.0
Path=C:\Users"YOURDESKTOPNAME"\Desktop\Armageddon2\Amageddon2\Armageddon2 DLLs\node-v22.13.1-win-x64\cli-11.1.0\bin
npm_config_prefix=C:\Users"YOURDESKTOPNAME"\Desktop\Armageddon2\Amageddon2\Armageddon2 DLLs\node-v22.13.1-win-x64\cli-11.1.0
Path=%USERPROFILE%.dotnet\tools
////////////////////////////////////////
This should be in your system variables.
ChocolateyInstall=C:\ProgramData\chocolatey
CUDA_CUDNN_PATH=C:\Program Files\NVIDIA\CUDNN\v9.6\bin\12.6
CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6
CUDA_PATH_V12_6=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6
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Project Breakdown: Armageddon2 Overview: Armageddon2 is an advanced AI-accelerated computational framework for real-time CPU and GPU task optimization. It leverages CUDA, cuDNN, and AVX-optimized CPU instructions to enhance parallel computing efficiency. The framework dynamically monitors, analyzes, and predicts workload distribution between CPU and GPU, ensuring optimal resource utilization.
This project focuses on high-performance computing, memory management, AI-driven predictions, and code injection for system-level optimizations.
Key Features
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AI-Driven CPU/GPU Load Balancing Uses real-time CPU and GPU monitoring to detect workload distribution inefficiencies. Implements AI-based predictive models to optimize CPU and GPU task scheduling. Reduces CPU overhead by dynamically shifting tasks between CUDA and AVX-optimized CPU computations.
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CUDA & GPU-Accelerated Parallel Processing Employs CUDA kernels for efficient GPU-based parallel execution. Matrix multiplication, AVX vector operations, and custom CUDA kernels for performance gains. VRAM-allocated thread pools to offload computations from the CPU to the GPU.
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AI-Powered Computation Prediction Linear regression-based AI model predicts future workload behaviors. Real-time thread monitoring adjusts execution strategies dynamically. Reduces computational errors by adjusting workloads based on AI feedback loops.
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Memory Management & Optimization Pinned memory allocation for faster data transfers between CPU and GPU. Memory compaction and defragmentation reduce fragmentation in GPU memory. Process memory monitoring ensures efficient resource allocation.
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System-Level Code Injection & Process Monitoring DLL Injection & Kernel Hooking for deeper process control. DirectX integration for monitoring GPU-intensive applications. Attaches to running processes, injects optimized CUDA/CPU instructions.
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GPU Performance Monitoring & Verification Logs real-time GPU utilization. Tracks CUDA execution time & thread computation metrics. Supervisor Kernel dynamically compares thread execution times to optimize processing.
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Real-Time Image Processing & Enhancement Includes pixel decompression, sharpness enhancement, and AI-powered image correction. Supports real-time video feed analysis.
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Thread Management & Process Synchronization Implements ThreadMapper to track active CPU & GPU threads. Dynamically registers & unregisters active threads for performance tuning. Core Components & Functionality
🔹 ArmageddonAlgorithm.cpp/h Main AI-driven computational logic for performance optimization. Implements Load Balancing AI, Real-time CPU & GPU adjustments. Uses machine learning predictions to optimize execution time. 🔹 CUDA_SPY.cpp/h Monitors CUDA activity to optimize GPU workload. Detects bottlenecks and improves GPU utilization. Replicates operations using CUDA to balance CPU-GPU loads.
🔹 SupervisorData.cpp/h Implements Supervisor Kernel to analyze CUDA and CPU execution times. Compares execution times between different threads and predicts faster computation paths.
🔹 CodeInjector.cpp/h Injects AI-enhanced CPU instructions into target processes. Supports AVX-optimized execution for fast data processing.
🔹 GPUVerifier.cpp/h Logs GPU hardware details and validates GPU performance. Checks CUDA core count, VRAM, multiprocessor performance.
🔹 PixelDecompressor.cpp/h Real-time image processing module. Enhances sharpness, corrects pixel artifacts. Used for image-based AI decision-making.
🔹 ThreadMapper.cpp/h Maps and tracks CPU & GPU threads. Dynamically adjusts workload allocation.
🔹 VRAMCacheManager.cpp/h Manages GPU cache for faster memory access. Optimizes VRAM allocation for high-performance execution. Technical Highlights CUDA Kernels for High-Speed Computation
Matrix multiplication, thread-based AI computations, and GPU parallelism. Implements linear regression prediction models within CUDA. Multi-Level AI Computation Tuning
Uses machine learning predictions to optimize real-time execution. Analyzes CPU & GPU load, shifting computation dynamically. DirectX & System Integration
Hooks into DirectX pipeline to monitor GPU workload. Injects AI-enhanced CUDA execution paths into running processes. Advanced Memory Management
Pinned memory allocation for fast CPU-GPU memory access. Implements VRAM thread pooling for large-scale parallel execution. Ideal Use Cases High-performance AI & ML-based computing frameworks Real-time gaming optimizations Machine learning inference acceleration Video processing with CUDA & AI-based image enhancements System-level process monitoring & optimizations Suggested GitHub Description 🚀 Armageddon2: AI-Powered CUDA-Accelerated Performance Optimizer 🚀
Armageddon2 is an AI-accelerated computational framework that optimizes CPU & GPU workloads in real-time. Using CUDA, cuDNN, AVX instructions, and AI-powered predictions, it dynamically balances CPU & GPU tasks, enhances image processing, and improves system performance.
✨ Key Features AI-Driven CPU/GPU Load Balancing CUDA-Accelerated Parallel Processing Real-Time Memory Optimization & Defragmentation Dynamic Code Injection for System Optimizations AI-Powered Thread Management & Computation Predictions DirectX GPU Monitoring & Game Performance Tuning Real-Time Image Processing with AI-Enhanced Pixel Correction 💡 Technologies Used CUDA & cuDNN AVX-Optimized CPU Computation DLL Injection & Code Hooking DirectX Integration Machine Learning-Based AI Predictions Pinned Memory & VRAM Thread Pooling 🎯 Ideal Use Cases High-Performance AI/ML Computation Game Performance Optimization GPU-Accelerated Parallel Processing Real-Time Video Processing 🔗 Open-source & free for all developers under the MIT License! 🚀✨
📌 Finalized Breakdown of Armageddon2 With all components analyzed, this is the most complete breakdown of Armageddon2, an AI-powered performance optimizer, memory manager, system automation tool, and GPU/CPU load balancer.
This final version provides a detailed technical summary, project goals, and usage cases.
🚀 Armageddon2 - AI-Powered System & GPU Performance Optimizer Armageddon2 is an AI-enhanced high-performance computing framework that monitors, optimizes, and automates workload distribution between CPU and GPU.
It integrates CUDA, cuDNN, DirectX 12, deep-learning inference, AVX CPU optimizations, and real-time system telemetry to enhance computing speed, reduce memory fragmentation, optimize GPU utilization, and improve AI-based processing tasks.
🛠 Key Features 1️⃣ AI-Driven CPU/GPU Load Balancing Real-Time AI Predictions: Adjusts CPU-GPU workload allocation dynamically using machine learning algorithms (AI_Integration.cpp). AVX-Optimized Execution: Uses vectorized computations for fast CPU processing (HostVerifier.cpp). Automatic Workload Distribution: Shifts AI-based calculations to the fastest available processor (GPU or CPU) (SupervisorData.cpp). 2️⃣ CUDA & GPU-Accelerated Parallel Processing Optimized CUDA Kernels: Includes high-speed matrix operations, AI inference execution, and parallel thread management (kernel.cu.txt). GPU Spy Monitoring: Logs GPU activity, CUDA execution time, and processing efficiency (CUDA_SPY.cpp). Pinned Memory for Fast CPU-GPU Transfers: Enables low-latency memory access (PinnedMemory.h). 3️⃣ Memory Management & VRAM Optimization AI-Powered VRAM Cache Manager: Dynamically allocates GPU memory based on active tasks (VRAMCacheManager.cpp). CUDA Memory Bridge: Ensures fast & efficient interprocess communication between CPU & GPU (MemoryBridge.cpp). HyperCache AI: Uses precomputed execution paths to predict workload bottlenecks (HyperCache.cpp). 4️⃣ DirectX 12 Integration & GPU Pipeline Hooking Hooks into DirectX 12 Pipeline: Monitors GPU workloads & enhances rendering performance (DirectXLinker.cpp). AI-Based Motion Estimation: Uses deep-learning models for motion prediction & frame interpolation (motion_estimation_kernel.cpp). Dual GPU Rendering Support: Supports multi-GPU setups for large-scale computations (Dual Gpu Rtv Helper.h). 5️⃣ AI-Assisted Game Optimization Controller Input Prediction: Uses AI to predict joystick & button inputs in FPS games (GameInputListener.cpp). Memory Injection & Real-Time Hooking: Modifies game behavior dynamically to enhance system efficiency (ProcessUtils.h). Performance Analytics & AI-Based Adjustments: Analyzes game activity and dynamically boosts rendering pipeline efficiency. 6️⃣ Advanced System Automation & Execution Monitoring Supervisor Kernel for CPU/GPU Execution Tracking: Logs real-time system performance and adjusts execution threads dynamically (SupervisorData.cpp). ThreadMapper for Dynamic Execution Adjustments: Reassigns CPU-GPU task execution in real-time (ThreadMapper.cpp). Node Executor for AI Execution Scheduling: Runs predefined AI processing commands in multi-threaded pipelines (NodeExecutor.cpp). 7️⃣ Deep Learning & AI Model Integration Voice Model AI for System Commands: Allows voice-based system automation & task execution (VoiceModel.cpp). Real-Time OCR Processing & AI-Based Text Recognition: Uses Tesseract & OpenCV for AI-enhanced text processing (TextRecognition.cpp). Matrix Operations for AI-Based Data Processing: Includes CUDA-powered AI tensor operations (MatrixOps.cpp). 8️⃣ AI-Powered Object Detection & Scene Understanding Real-Time Object Recognition: Uses AI-driven motion tracking & image processing (ObjectDetector.cpp). Pixel Decompression & Image Correction: Enhances video clarity using CUDA-powered pixel transformations (PixelDecompressor.cpp). DirectX 12-Based AI-Enhanced Screen Capture: Captures real-time screen frames for AI-assisted analysis (ScreenCapture.cpp). 🎯 Use Cases ✅ High-Performance AI Computing – Uses CUDA & AI models for advanced real-time processing. ✅ Game Optimization – Enhances rendering, physics, & AI-based game decision-making. ✅ Real-Time Performance Tuning – Prevents CPU & GPU bottlenecks dynamically. ✅ System Automation & AI-Based Execution Management – Automates AI commands, thread execution, and process scheduling. ✅ GPU-Based AI Inference Acceleration – Offloads machine learning computations to CUDA for faster inference.
💡 Technologies Used CUDA & cuDNN – GPU-based parallel execution. AVX-Optimized CPU Execution – Faster vectorized computations. Machine Learning-Based AI Predictions – Smart workload allocation. DirectX 12 Pipeline Hooking – Enhances real-time GPU rendering performance. Pinned Memory & VRAM Cache Optimization – Low-latency memory allocation. 📜 License MIT License – Open-source & free for all developers! Modify, distribute, and contribute freely! 🚀