# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 2 Background
### Characteristics of Jobs in HPC Systems  
HPC systems are designed to handle large-scale computation and data processing tasks that require substantial computing power and high IOPS [13]. Typical jobs performed on HPC systems include complex simulations, big data analytics, and AI model training and serving [14; 15; 16]. These workloads often include intensive I/O operations requiring quick data access, loading, and manipulation. In recent years there has been a significant increase in the demand for AI training and serving workloads on HPC systems. While AI models require compute-intensive workloads, efficient computation also requires careful consideration of I/O performance [17]. The machine learning and deep learning tasks central to AI models often involve I/O-intensive operations, making it essential to manage I/O effectively throughout the entire workflow. A notable example is the training of the GPT series models, such as GPT-3 and GPT-4, which requires extensive computational resources and high IOPS to handle the vast amounts of data involved [18; 19]. These models  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Target and Result** & **Focus** \\ \hline [PERSON] et al. [7] & Combines round-robin with a genetic algorithm for CPU scheduling, resulting in lower average waiting times than traditional methods. & CPU \\ \hline [PERSON] et al. [8] & Applies fuzzy logic in the design and implementation of a rule-based scheduling algorithm to solve the shortcoming of well-known scheduling algorithms (FCFS and SJF), achieving better waiting and turnaround times than priority scheduling with efficiency close to SJF while handling task priority. & CPU \\ \hline [PERSON] et al. [9] & Analyzes a proposed algorithm with existing round-robin scheduling algorithm on the basis of varying time quantum, average waiting time, average turnaround time, and number of context switches, achieving reduced OS overhead and dispatch latency. & \\ \hline [PERSON] et al. [10] & Proposes a novel bandwidth-aware mapping algorithm for I/O list-scheduling policies, achieving improved system stretch and resource utilization while maintaining throughput. Evaluated strategies show better control of resource waste, especially with increased I/O nodes, optimizing performance for both throughput and latency. & I/O performance \\ \hline [PERSON] et al. [11] & Analyzes the effects of interference on application I/O bandwidth and proposes a scheduling technique to mitigate congestion, achieving reduced performance variability and improved system efficiency. & I/O performance \\ \hline [PERSON] et al. [12] & Proposes an adaptively periodic I/O scheduling (APIO) method by taking the stochastically iterative I/O periodicity of the applications into account, achieving improved system efficiency and reduced application dilation compared to existing methods. & I/O performance \\ \hline \hline \end{tabular}
\end{table}
Table 1: Previous scheduling studies.  
have billions of parameters, and need to rapidly access and update massive datasets during training. For instance, GPT-3, with 175 billion parameters, processes terabytes of text data, requiring the system to read and write data at high speeds to meet the computational demands of GPUs or TPUs [20]. Similarly, other deep learning tasks demand high IOPS for efficient data access and processing, especially during the training phase when large datasets are continuously processed. Ensuring high IOPS is crucial to prevent bottlenecks and optimize the utilization of computational resources [21].