Exploring Reservation and Preemptible Marketplace Strategies in Scheduling for High-Performance Computing

High-Performance Computing (HPC) has firmly established itself as a cornerstone of modern computational endeavors, playing a pivotal role in a diverse range of sectors. From intricate scientific simulations to large-scale data processing tasks, HPC's capabilities are sought after for their unparalleled processing power and efficiency. Yet, within this vast landscape of computational prowess, a challenge persists: the notable underutilization of available resources. Despite the advancements and the increasing reliance on HPC systems, a significant portion of their capacity often remains unused, leading to operational inefficiencies and missed computational opportunities. To address this challenge, the research introduces the concept of a secondary preemptive market. This market, distinct from traditional HPC resource allocation paradigms, seeks to optimize the utilization of computational resources. Users are presented with an opportunity to access these resources under a unique set of conditions. While they can harness the computational power of HPC systems, their tasks are subject to potential interruptions, making way for tasks deemed of higher priority. This dynamic interplay between availability and demand forms the crux of the secondary preemptive market, aiming to strike a balance that maximizes resource utilization. The research employs a rigorous methodology to explore this concept. The initial phase involves an in-depth data analysis, drawing from datasets of renowned HPC systems. This analysis provides a comprehensive overview of current resource utilization patterns, highlighting inefficiencies and offering a baseline against which improvements can be measured. The datasets from HPC systems such as Theta, Cooley, and Mira serve as invaluable resources, offering insights into core hours computation, discrete time values, and idle time behaviors. Through this detailed exploration, the research paints a vivid picture of the current state of HPC resource utilization. Building on the insights from the data analysis, the research then transitions to simulations. These simulations are designed to replicate the operational dynamics of the secondary preemptive market in a controlled environment. By mimicking real-world scenarios, the simulations offer a granular view of task allocation, execution, and potential preemption within the market framework. The design of these experiments is meticulous, ensuring that the simulations accurately reflect the complexities of HPC systems and the proposed market. The results from these simulations are telling. They indicate a potential for improved resource utilization, with fewer dormant resources and more efficient task execution times. In conclusion, the research offers a deep dive into the challenge of HPC resource underutilization and presents the secondary preemptive market as a potential solution. Through detailed data analysis and comprehensive simulations, the viability and benefits of this market are explored. The findings suggest a promising direction for the future of HPC, where resources are not just abundant but are also optimally utilized.