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Monitoring and Performance Management in HPC

Malgukke HPC

Key Areas of Monitoring and Performance in HPC

Explore the critical areas for effective monitoring and performance management in High-Performance Computing, ensuring system efficiency and reliability.

System Monitoring

Monitoring physical components like CPU, GPU, memory, and storage. Ensures hardware availability and detects potential failures or bottlenecks.

Application Monitoring

Tracks application performance to identify inefficiencies, focusing on metrics like execution time, I/O operations, and latency.

Performance Profiling

Detailed code-level analysis to optimize the efficiency of software modules, identifying bottlenecks due to poor scaling or I/O issues.

Error Monitoring

Monitors for hardware or software errors such as memory faults and network issues, helping prevent system failures or performance drops.

Job Scheduling and Queuing

Monitors job queues and scheduling, ensuring effective resource allocation, job prioritization, and analyzing wait times for optimization.

Network Monitoring

Tracks network traffic and latency, crucial for communication between compute nodes in the HPC cluster.

Storage Monitoring

Monitors data storage usage and I/O performance, ensuring efficient filesystem operations and sufficient storage availability.

Power and Energy Monitoring

Monitors energy consumption of the entire system, optimizing energy usage to reduce operating costs and improve sustainability.

HPC Performance Monitoring Scenarios

Explore real-world scenarios that showcase effective monitoring and performance management in High-Performance Computing (HPC) systems.

System Monitoring


Continuous monitoring of the 158,976 A64FX CPUs' temperature to prevent overheating and hardware failures.

Application Monitoring


Monitors GPU utilization and I/O operations during climate model simulations to adjust resource allocation and avoid bottlenecks.

Performance Profiling


Identifies inefficient memory management in weather forecasting applications, optimizing scaling and boosting performance by 30%.

Error Monitoring


Detects repeated memory errors during large-scale genomic analysis, reallocating jobs to prevent system failure and diagnose hardware issues.

Job Scheduling and Queuing


SLURM system dynamically reallocates resources to prioritize critical COVID-19 research jobs without impacting overall system performance.

Network Monitoring


Identifies increased latency between compute nodes during a large scientific simulation, bypassing a faulty switch to maintain uninterrupted operations.

Storage Monitoring


Detects I/O bandwidth drop during astrophysical simulations and automatically redirects operations to less congested storage pools, stabilizing performance.

Power and Energy Monitoring


Optimizes energy usage during biological simulations by dynamically reducing power to underutilized compute nodes, achieving 15% energy savings.

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