***********
Performance
***********

This guide covers techniques for optimizing Jobmon workflow performance.

.. note::
   This page is a placeholder. Content will be expanded based on common 
   performance questions and best practices.

Overview
========

Workflow performance depends on several factors:

- **Task granularity**: How much work each task does
- **Parallelization**: How many tasks can run concurrently
- **Resource allocation**: Memory, cores, and runtime settings
- **Dependencies**: How tasks are connected

Key Principles
==============

Right-Size Your Tasks
---------------------

Tasks that are too small create overhead. Tasks that are too large limit parallelism.

**Guidelines**:

- Target 5-30 minutes runtime per task
- Avoid tasks shorter than 1 minute
- Break up tasks longer than several hours

Minimize Dependencies
---------------------

Each dependency adds coordination overhead. Only add dependencies that are 
truly necessary.

Use Arrays Efficiently
----------------------

Jobmon groups tasks into Slurm job arrays automatically. Tasks with the same 
TaskTemplate and compute resources are batched together.

**Tip**: Keep compute resources consistent within a TaskTemplate when possible.

Resource Optimization
=====================

Start Conservative
------------------

Request less resources initially:

.. code-block:: python

   compute_resources={
       "memory": "2G",
       "runtime": "30m",
       "cores": 1,
   }

Let Jobmon's resource retry handle cases that need more.

Use Resource Usage Data
-----------------------

After a workflow completes, check actual usage:

.. code-block:: python

   # Get resource usage for a task template
   usage = task_template.resource_usage(workflows=[workflow_id])
   print(usage)

Concurrency Limits
==================

Control maximum concurrent tasks:

.. code-block:: python

   workflow = tool.create_workflow(
       name="my_workflow",
       max_concurrently_running=500
   )

Or per TaskTemplate:

.. code-block:: python

   workflow.set_task_template_max_concurrency_limit(
       task_template_name="heavy_compute",
       limit=100
   )

Monitoring Performance
======================

Use the GUI to monitor:

- Tasks completed over time
- Resource utilization
- Bottlenecks (tasks waiting on dependencies)

Future Content
==============

This section will be expanded with:

- Detailed benchmarking guidance
- Cluster-specific optimization
- Memory profiling techniques
- I/O optimization strategies