Real-time Compilation and Performance Monitoring for High-Performance Systems

High Performance Computing applications are diverse and they operate in dynamic environments. This requires a shift in compilation techniques from static, hardcoded algorithm-driven approaches to dynamic, real-time optimizing strategies. Current compiler methodologies primarily rely on static code analysis to apply optimizations during the compilation phase. Optimization techniques such as loop unrolling, vectorization, and function inlining are only effective for predictable workloads. However, they lack the adaptive ability in changing runtime conditions and hardware variability. System specific optimizations can be manually applied by the programmer, but this makes the code less portable and requires rewrite of entire programs, thus increasing the cost of maintenance. Our approach includes a real-time performance monitoring system that can trigger a recompilation dynamically to change code execution patterns. Runtime feedback is used to identify bottlenecks such as core overutilization, cache inefficiencies, and memory bottlenecks. A distinctive feature of the system is feedback-driven real-time compiler optimization. Whereas the performance benefits of dynamically compiled code is offset by the overhead incurred from the monitoring and recompilation system, the overall efficiency of the program throughout its runtime improves incrementally over each iteration of dynamically recompiled code. This efficiency improvement can also lead to energy savings in terms of reduction in wasted computational resources. The work presented here lays the foundations for adaptable and feedback-driven compiler optimization strategies.