Practical guide to optimizing HPC grids

Strategic and technical framework based on real projects.

• Bottleneck identification
• Scheduling best practices
• Queue optimization
• Advanced monitoring

An HPC grid that works is not the same as an HPC grid that is efficient. In banking and insurance, compute clusters grow for years by adding nodes and queues, until the cost per task rises, close windows tighten and nobody knows for certain where the time goes. This guide summarises the framework we apply on real grid projects on IBM Spectrum Symphony, TIBCO DataSynapse and hybrid clusters extended to the cloud.

The order matters: measure first, then plan, then tune the queues and, only at the end, add capacity. In most of the environments we have audited, capacity was not the problem; the distribution was.

1. Identifying bottlenecks

Before touching any configuration you need to know what is holding the grid back: CPU, memory, I/O, network or the logic of the tasks themselves. The symptoms are usually the same (windows that stretch, idle nodes next to saturated ones, tasks that retry), but the cause changes from one environment to another.

• Profile the longest tasks and the most frequent ones separately: they rarely coincide and are rarely optimised the same way.
• Measure real utilisation per node and per queue during the critical window, not the daily average.
• Locate the data dependencies (databases, shared files, external services) that serialise the work.
• Separate compute time from queue waiting time and from data transfer time.

2. Scheduling good practices

The scheduler decides which task runs on which node and when. A poorly tuned scheduling policy wastes capacity even when hardware is plentiful: priorities that override each other, reservations nobody uses and short tasks stuck behind long ones.

• Define priorities from the business calendar (closes, regulatory reporting), not from who asks first.
• Group tasks by resource profile so the scheduler can pack them without fragmenting nodes.
• Limit automatic retries and record their cause: a silent retry is a hidden problem.
• Review the pre-emption policy: useful for regulatory peaks, expensive if it fires daily.

3. Queue optimisation

Queues are where operational debt accumulates: created for a project and never retired, they overlap and end up competing for the same nodes. Simplifying is almost always the first win.

• Consolidate queues with the same profile and retire those that have received no work in weeks.
• Size per-queue limits from measured demand, with headroom for known peaks.
• Separate interactive from batch workloads so the former never wait behind the latter.
• Extend to the cloud only the queue that suffers the peak, with nodes that shut down when done (cloud bursting).

4. Advanced monitoring

Without continuous metrics, every optimisation is an opinion. Monitoring must cover the whole grid (queues, nodes, tasks and data) and be tied to alerts that anticipate the problem rather than confirm it.

• Time series of utilisation, queue time and failure rate per queue and per application.
• Trend-based alerts (a window that stretches three days in a row), not just thresholds.
• Dashboards shared by operations, business and audit: the same figure for everyone.
• Traceability of every run (version, parameters, node, duration) as evidence for the supervisor.