# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 3 Results and Discussion
### Parallel Pipeline Performance and Scalability
#### 3.5.3 Memory Utilization and Shuffle I/O  
Memory management is a crucial factor in large-scale HPC applications. The Data Preprocessing and Outlier Detection steps consume the highest memory, with on-heap memory usage reaching 1024 MiB, while other tasks, such as Bi-LSTM Optimization and Forecasting LSTM, require significantly lower memory allocations. This suggests that data-intensive processes may require additional memory optimizations to enhance scalability further.  
Figure 8: Efficiency trends across different processes.  
Shuffle I/O, representing the amount of data exchanged between distributed processes, varies significantly across computational stages. Random Forest Forecasting exhibits the highest shuffle I/O at 300 MiB, likely due to the need for extensive data exchange among Spark executors when constructing multiple decision trees. In contrast, LSTM-based processes show minimal shuffle activity (9.1 MiB), as neural network training is inherently less reliant on inter-process communication.  
Figure 9(a),b provide a comparative visualization of memory consumption and shuffle I/O across different processes.  
The strong scaling performance of the computational tasks demonstrates near-linear speedup with parallel execution. Notably, Bi-LSTM optimization and Random Forest forecasting achieve speedups exceeding 5\(\times\) on an 8-core setup. This indicates that parallel processing effectively distributes computational loads, allowing tasks that would traditionally take hours on a single-core machine to be completed in significantly shorter timeframes. The ability to scale across multiple cores ensures that high-complexity computations can be executed efficiently, particularly in scenarios where large-scale data processing is required.  
Deep learning workloads exhibit high efficiency in parallel execution, with the Bi-LSTM optimization stage achieving 71.12% efficiency. Similarly, LSTM-based forecasting tasks maintain efficiencies of approximately 40%, confirming that neural network training and inference scale effectively when distributed across multiple processing units. The ability to distribute neural network computations ensures that training deep learning models for real-time forecasting remains computationally feasible without excessive delays.  
A key insight from the throughput and latency analysis is that throughput gains are achieved without significantly reducing task latency. The outlier detection stage demonstrates the highest throughput at 40.8 tasks per second, benefiting from parallel execution and the ability to process multiple data points simultaneously. However, deep learning  
Figure 10: (a) Memory Utilization Distribution. (b) Shuffle I/O Distribution.  
Figure 9: Throughput and Latency Across Processes.  
based tasks maintain lower throughput due to the higher computational demands of model training and inference. This suggests that while parallel execution accelerates processing speed, individual task completion times remain relatively stable.  
Memory and shuffle I/O overheads vary across different processes, with Random Forest training requiring significantly higher memory (512 MiB) and shuffle I/O (300 MiB) compared to LSTM-based tasks. The higher memory consumption and disk I/O indicate that tree-based models impose a larger computational footprint due to their reliance on extensive data movement and decision tree construction. In contrast, deep learning-based tasks exhibit lower memory and I/O requirements, benefiting from optimized GPU acceleration.  
The combination of parallel CPU execution, MPI task distribution, and GPU acceleration results in substantial computational gains. These improvements make real-time forecasting feasible while ensuring robust resource utilization across hardware components. By leveraging a hybrid HPC approach that incorporates multi-core processing and GPU acceleration, the computational efficiency of the forecasting pipeline is maximized, enabling faster execution times and more scalable forecasting capabilities.