Why job schedulers are needed

The Role of Job Schedulers in HPC

High‑performance computing systems are shared, expensive resources. A job scheduler (also called a batch system or resource manager) is the software that decides who can use which resources, when, and for how long. Without it, large clusters would quickly become unusable.

This chapter explains why schedulers are essential in HPC, without going into the details of any particular scheduler (that belongs to later chapters).

Why “just logging in and running” doesn’t work

On a laptop or desktop, you start a program and it runs immediately because:

• You are typically the only user.
• There are few cores and a single memory space.
• The OS can manage everything interactively.

In an HPC cluster:

• There can be hundreds or thousands of users.
• Nodes might have dozens of cores and large memory.
• There may be hundreds or thousands of nodes.
• Jobs can run from minutes to weeks.

If everyone just logged into compute nodes and started programs directly:

• Some nodes would be overloaded while others idle.
• Multiple large jobs could compete for memory and cores, crashing or slowing each other.
• Users could accidentally (or intentionally) take over many nodes for a long time.
• The system would become unstable and unpredictable.

A scheduler enforces a controlled, orderly way to start and manage jobs across many users and nodes.

Core goals of a job scheduler

Job schedulers in HPC aim to:

• Protect the cluster
  Prevent overloading nodes, crashing the OS, or corrupting shared filesystems.
• Protect users from each other
  Ensure one user’s job cannot starve others of CPU, memory, or licenses.
• Increase overall throughput
  Arrange jobs to get as many useful computations done as possible per day.
• Provide fairness and policy enforcement
  Encode site rules: who can use what, when, and how much.
• Enable non‑interactive, long‑running work
  Let users run large or long jobs reliably, even when they are not logged in.

Resource sharing and contention

HPC clusters are multi‑tenant systems: many independent users share the same physical hardware.

Resources that need to be shared include:

• CPU cores and hardware threads
• RAM on each node
• GPU devices or other accelerators
• Interconnect bandwidth
• Parallel filesystem bandwidth and metadata servers
• Licensed software seats or tokens
• Special hardware (high‑memory nodes, large‑GPU nodes, etc.)

Without a scheduler, contention happens:

• Two large jobs may oversubscribe CPUs on the same node, each expecting exclusive use.
• Memory‑heavy jobs might exceed available RAM, causing swapping or crashes.
• Multiple I/O‑heavy jobs can saturate the filesystem, slowing everything.

A scheduler addresses this by:

• Tracking available resources across the cluster.
• Accepting jobs with declared resource requests (e.g. cores, nodes, time).
• Deciding when a job can run so that requested resources are reserved.

Fairness and policy implementation

Schedulers also implement institutional policies. Examples:

• Fair‑share usage
  If one user or project has used a lot of resources recently, the scheduler may temporarily lower their priority to let others catch up.
• Priority groups
  Some users (e.g. production climate runs, urgent industry partners) may have higher priority queues or partitions.
• Account and project limits
  Limit total cores, nodes, or GPU hours a project can consume concurrently.
• Wall‑time limits
  Restrict max runtime to prevent “runaway” jobs from blocking resources indefinitely.
• Access control
  Restrict some nodes/partitions (e.g. GPU nodes, high‑mem nodes) to specific groups.

These policies would be nearly impossible to enforce manually on a large, busy system.

Increasing utilization and throughput

From a facility perspective, an HPC cluster is a capital investment that should be used as fully as possible.

Schedulers increase utilization and throughput by:

• Packing jobs efficiently
  Filling nodes with combinations of jobs that fit available cores and memory.
• Backfilling
  While waiting for a large job to start (that needs many nodes), the scheduler can insert smaller, shorter jobs into gaps, as long as they finish before the large job is scheduled to start. This raises utilization without delaying high‑priority work.
• Supporting job arrays
  Users can submit many similar small jobs (e.g. parameter sweeps) efficiently; the scheduler manages them as a batch, reducing overhead and improving packing.

Without such mechanisms, large parts of a cluster would sit idle waiting for “just the right time” to start big jobs.

Reliability for long‑running jobs

Many HPC jobs:

• Run for days or weeks.
• Use large numbers of nodes.
• Cannot be restarted easily without careful planning.

Schedulers improve reliability by:

• Ensuring resources are reserved exclusively for a job once started.
• Avoiding oversubscription that leads to system instability.
• Tracking job state so that:
• Job logs and exit codes are preserved.
• Jobs that fail immediately (e.g. due to configuration errors) are clearly labeled.
• Integrating with mechanisms such as:
• Checkpoint/restart (via scripts and tools, not implemented by the scheduler itself).
• Automatic requeueing in some failure situations (e.g. node failure).

Running such jobs manually on shared nodes would be too fragile and error‑prone.

Decoupling interactive work from batch work

HPC workflows typically have:

• An interactive phase: editing code, compiling, light testing.
• A batch phase: submitting heavy computations that may run for a long time.

Schedulers enforce the separation:

• Login nodes are reserved for light, interactive work.
• Compute nodes are accessed only through jobs managed by the scheduler.

This separation:

• Keeps login nodes responsive.
• Prevents accidental heavy jobs from being run interactively on shared login resources.
• Provides a clear interface: “if it’s big or long, submit it as a job.”

Some schedulers also offer interactive jobs, which give you a shell on compute nodes but still under scheduler control (time‑limited, resource‑limited).

Enabling complex workflows and dependencies

Real HPC tasks are often not single standalone runs; they are workflows:

• Pre‑processing → main simulation → post‑processing → analysis
• Multi‑stage pipelines
• Parameter sweeps with aggregation of results

Schedulers can manage these via:

• Job dependencies: “Start job B only after job A finishes successfully.”
• Job arrays: “Run this job for 1000 different parameter combinations.”
• Reservations and advance bookings (e.g. for tutorials or maintenance).

Without scheduler support, users would have to continuously monitor and manually start the next job in the sequence, which does not scale and is error‑prone.

Accounting and reporting

HPC centers must track how resources are used:

• How many core‑hours each user or project has consumed.
• How GPUs and specialized nodes are utilized.
• Which jobs failed and why.

Schedulers provide:

• Accounting data for billing or allocation reporting.
• Usage statistics for capacity planning and purchasing decisions.
• Audit trails to diagnose problematic jobs (e.g. those consistently crashing nodes).

Manual tracking on a large cluster would be impractical.

Summary: Why schedulers are indispensable in HPC

In a large, shared HPC environment, job schedulers are needed to:

• Manage resource sharing safely and efficiently.
• Enforce fairness and site policies.
• Maximize overall cluster utilization and scientific throughput.
• Provide a robust environment for long‑running and large‑scale jobs.
• Separate interactive usage from heavy computation.
• Support complex workflows and provide accounting.

All later topics on specific batch systems and tools build on this fundamental need: without a scheduler, a modern HPC cluster cannot operate effectively as a shared scientific instrument.