8 Ways to Optimise Your HPC Workloads for Maximum Efficiency

HPC computing

The growing need for high-performance computing (HPC) resources calls for leveraging all ways to optimise those workloads. Numerous factors affect overall efficiency, starting from workload characteristics and jobs submitted to scheduling algorithms and I/O performance.

Such bottlenecks and inefficiencies, while relating to hardware, software, policies and user behaviours, need a holistic approach to be addressed. Through effective utilisation and job scheduling, HPC computing administrators can achieve lower costs, a shorter turnaround and higher throughput.

Let’s tackle 8 essential steps in designing the HPC workload for maximum efficiency.

1. Consolidate Workloads

An effective way to increase the efficiency of HPC computing is to, when possible, combine multiple small jobs into more substantial tasks. Dividing the big task among many small jobs and running them one after another instead of all together takes considerably more time because of the overhead of the startup for each job. This overhead is greatly eliminated through this consolidation, and therefore system utilisation improves.

Administrators have to figure out the workload involved, and it is necessary to join jobs as long as this will not cause a problem for others. For instance, the aggregation of several short serial jobs from the same customer or an agency would become possible. This dependency among jobs must also be taken into account so as to achieve on-time delivery.

2. Right-Size Job Resource Requests

It is common for users to over-allocate resources in their job submissions out of uncertainty or a desire to complete jobs quickly. However, over-requesting nodes, cores, memory, or other resources leads to resource fragmentation and reduced system utilisation. Administrators should work with users to right-size job requests based on detailed profiling of past runs.

For example, if a job typically uses 16 cores and 4 GB of memory but is submitted requesting 32 cores and 8 GB, its resource allocation could be reduced. This “right-sizing” opens up resources for other jobs without negatively impacting performance. In some cases, minor adjustments like reducing the number of extra cores from 32 to 24 can have a big impact on overall throughput.

3. Employ Backfill Scheduling Strategies

Backfill scheduling allows lower-priority HPC computing jobs to use resources that would otherwise be idle, improving overall system utilisation. There are different backfill strategies an administrator can implement:

  • Conservative backfill only allows a job to backfill if it does not delay any higher-priority jobs. This ensures strict priority order but may leave resources idle.
  • Aggressive backfill relaxes constraints and allows more jobs to backfill, increasing system utilisation but possibly delaying some higher-priority jobs.
  • Easy backfill prioritises easy-to-preempt jobs for backfill to balance utilisation and turnaround time impact.

Testing different strategies using workload traces can help determine the optimal approach. In general, more aggressive backfill leads to better utilisation at the cost of increased average wait times for lower-priority jobs.

4. Apply Node/Core Binding

Normally, a job scheduler tends to allocate tasks to any spare nodes or cores for execution without any restriction. However, this can cause greater inefficiency as the work is balanced across physical nodes. Applying node binding or the core binding method allows for related processes in multiple jobs to be combined in a single application, which improves data locality and minimises communication overhead.

MPI jobs are one of those that, when bound to specific cores, minimise latency and congestion. “Pinning” operations in this procedure increase performance, particularly in cases of communication-intensive workloads.

5. Implement Burst Buffer Integration

Burst buffers are fast parallel storage systems that sit between compute nodes and parallel file systems in the storage hierarchy. They provide a large pool of temporary scratch storage for HPC computing workloads during runtime. Integrating burst buffers into the I/O stack optimises storage performance in several ways:

  • Jobs read input data from and write output to the high-speed burst buffer before transferring to slower parallel storage. This “stages” I/O and reduces congestion.
  • The burst buffer caches frequently accessed data, avoiding repeated reads from parallel storage and speeding up re-runs or ensemble jobs with common input.
  • Parallel I/O libraries like MPI-IO can leverage the burst buffer to enhance collective I/O performance through data aggregation and striping.

Burst buffers significantly improve I/O performance for storage-bound HPC computing workloads and enable faster job turnaround when configured properly.

6. Apply Multi-Level Scheduling

Multi-level scheduling groups and schedule jobs together at different levels of grouping. For example, there may be a definition of job classes based on runtime, resources, etc., into which individual jobs fit into and schedule from.

This mechanism would, therefore, allow the job use of all the available parallelism dimensions: sockets, cores, and vectors of only one node. Short jobs in class backfill the resources used by long-runners, hence improving the class’s level of node utilisation without stalling it. Similarly, classes with common attributes, such as memory usage, are grouped together.

Multi-level scheduling optimises parallelism at the job, node, and system level, thereby enhancing overall cluster efficiency compared to single-level scheduling of individual jobs. It maintains SLA provision through strict performance isolation between workloads with different SLAs.

7. Tune Job Launch Overheads

The startup time for an HPC computing job includes allocation, initialization, and launch overheads. While these may seem negligible for large jobs, they can significantly impact small jobs and the overall system throughput. Administrators should profile and optimise these overheads.

Some techniques include:

  • Caching job environment modules and preloading them at the scheduler level.
  • Implementing job pre-staging to pre-fetch input data.
  • Enabling lazy allocation means starting jobs before full resources are available.
  • Increasing job batching will amortise scheduling costs over more jobs.

Even minor reductions from seconds to milliseconds in these overheads, when compounded over thousands of jobs, can yield big efficiency gains. Proper tuning eliminates unnecessary delays between each job stage.

8. Apply Adaptive Job Controls

By utilising adaptive HPC computing job controls, schedulers can dynamically modify job priorities and adjust resource usage during changing system conditions, not only through static settings. Hence, it is used to increase cluster usage in those cases when workloads change randomly and unpredictably.

To illustrate, when the capacity reaches a high level, the maximum allocation for less urgent tasks can be reduced or suspended in order to dedicate resources to more urgent needs. In contrast, when machines are working, idle limits are loosened to let more tasks run during the latent times.

Final Words

By applying these optimisation techniques, HPC administrators can significantly improve workload efficiency, drive down costs, and increase job throughput and turnaround times. Regular review and tuning are required as workloads evolve. Automating optimisations using tools and machine learning also helps maximise efficiency at scale. With the continued growth of HPC, finding new ways to optimise cluster utilisation will remain a top priority.

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