The aim of this project is to develop an application profiler tool that runs specified applications for a designated duration and provides visual analytics, primarily focusing on parallelization features such as cache memory hit/miss, storage access times, running/waiting times, active thread counts, and more. The tool will be developed using Python and will incorporate visualization techniques to present the analyzed data in an easily understandable format.

1. Application Profiling: The tool will be capable of running designated applications for a specified duration, collecting relevant data during runtime.

2. Parallelization Analysis: It will analyze various aspects related to parallelization, including cache memory utilization (hit/miss), storage access times, CPU core utilization, thread activity, etc.

3. Visual Analytics: The collected data will be processed and presented through visualizations such as graphs, charts, and heatmaps to aid in easy interpretation and understanding of performance metrics.

4. Exporting Reports: The tool will allow users to export analysis reports in standard formats like PDF or CSV for further examination or sharing.