TITAN-ROCm

TITAN-ROCm is an autonomous AI-powered GPU optimization platform designed to improve the performance of PyTorch CUDA workloads through intelligent code analysis, iterative optimization, and real-time benchmarking. The system combines large language models, GPU profiling, and automated evaluation to simulate an intelligent performance engineering workflow. Users can input PyTorch CUDA code directly into the platform, where TITAN-ROCm analyzes the workload, identifies optimization opportunities, and generates multiple optimized code variants using an LLM-based optimization agent. Each generated variant is automatically benchmarked and evaluated based on key GPU performance metrics including latency, throughput, memory efficiency, and execution stability. The platform performs closed-loop optimization by continuously comparing generated variants and selecting the best-performing implementation through benchmark-guided evolution. Functional validation ensures that optimized code remains executable and preserves the original behavior while improving performance. TITAN-ROCm includes a professional Streamlit dashboard with: Real-time GPU benchmarking Optimization history tracking Throughput and latency visualization Side-by-side code comparison GPU information panel Downloadable optimized code LLM token and inference tracking Autonomous optimization reasoning The system is designed to demonstrate autonomous AI-driven performance engineering and intelligent CUDA optimization workflows for deep learning and GPU computing applications.