S4 ‐ GPU Rendering Analysis - S3-G31-Kotlin-QueueHub/mobile-app-android GitHub Wiki
GPU Rendering Analysis
In the following video you can see a live telemetry test Live Telemetry. To complement the live telemetry, a system trace profiling test was made. This was the results:
In our comprehensive performance evaluation of the virtual queue application, we conducted both live telemetry testing and system trace profiling to assess the application's runtime characteristics and efficiency.
Initial Performance Observations
During the initial application launch and user authentication, we observed a brief performance spike. This momentary increase in UI latency is characterized by a short "janky" frame, which could indicate a minor processing overhead during the login process. However, the duration of this latency spike is remarkably brief, suggesting efficient resource management.
Resource Consumption Stability
Following the initial login phase, the application demonstrates exceptional performance stability. The system trace reveals consistent memory and CPU consumption, without the characteristic "jigsaw pattern" typically associated with problematic garbage collection cycles. This stability indicates well-optimized memory management and efficient resource allocation.
Key Performance Optimization Strategies
The application's performance can be attributed to the following choices:
Asynchronous Thread Management
By leveraging I/O dispatchers, the application effectively distributes computational tasks across multiple threads. This approach prevents blocking the main thread, thereby mitigating the risk of Application Not Responding (ANR) errors. The strategic use of threading ensures responsive user interactions and smooth background processing.
Intelligent Caching Mechanisms
The integration of Firebase and Glide caching libraries plays a crucial role in optimizing memory consumption. These technologies intelligently cache data and resources, significantly reducing redundant network requests and minimizing unnecessary object creation. This approach not only improves performance but also enhances the application's memory efficiency.
Technical Implications for Virtual Queue Management
In the context of a virtual queue application, these performance optimizations are particularly critical. They ensure that:
User authentication and queue management processes remain swift and responsive Resource-intensive operations like real-time queue updates and user synchronization occur seamlessly The application can handle concurrent user interactions without compromising system stability
Potential Areas for Future Optimization
While the current implementation demonstrates strong performance characteristics, continuous monitoring and incremental improvements can further enhance the application's efficiency.