Fixing High CPU Usage In Launchpad_submitter.py

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Fixing High CPU Usage in launchpad_submitter.py

Hey guys! Have you ever noticed your CPU going crazy when running launchpad_submitter.py? It turns out there's a common problem: a busy-wait loop that causes unnecessary high CPU usage. Let's dive into what's happening and how we can fix it!

The Problem: Busy-Wait Loops

The launchpad_submitter.py module, responsible for handling submissions, uses a polling mechanism to check the status of jobs and submissions. The issue arises because this polling is implemented as a busy-wait loop. Basically, the script repeatedly checks for updates in a tight loop without pausing or yielding processing time to the system. This continuous checking consumes a significant amount of CPU, even when there are no actual status changes.

Why is this a problem? Well, high CPU usage can lead to several negative consequences:

  • Performance Degradation: On CI runners and developer machines, this unnecessary CPU load can slow down other processes, impacting overall system performance.
  • Increased CI Costs: In CI environments, CPU time often translates directly into cost. High CPU usage means you're paying more for the same amount of work.
  • Reduced Responsiveness: On lower-powered systems, the constant CPU drain can make the system feel sluggish and unresponsive.
  • Wasted Energy: Unnecessary CPU usage translates to wasted energy, which is not ideal from an environmental perspective.

Understanding Busy-Waiting

To truly grasp the issue, let's break down what a busy-wait loop actually is.

A busy-wait loop is a coding construct where a program repeatedly checks a condition until it becomes true. The crucial element here is that the program does not relinquish control of the CPU while waiting. It remains in a constant state of checking, consuming CPU cycles in the process. In the context of launchpad_submitter.py, the script continuously queries the status of a submission, even if the status remains unchanged. This constant querying is the root cause of the high CPU usage.

Now, let's look at the specifics within launchpad_submitter.py.

Imagine a scenario where you submit a job. The script then enters a loop to monitor its progress. Inside this loop, it sends requests to Launchpad, asking for the current status of the job. The problem? It does this rapidly, without any significant delay between requests. This rapid-fire querying keeps the CPU working hard, even when the job is still processing on the server and there is nothing new to report.

In essence, the script is asking, "Is it done yet? Is it done yet? Is it done yet?" repeatedly, without taking a break. This relentless questioning is what we need to address.

Diagnosing the Issue

So, how can you tell if you're experiencing this busy-wait problem? Here are a few steps to help you diagnose it:

  1. Run the Submission Flow: Execute the submission process that involves launchpad_submitter.py. This will trigger the polling mechanism we're investigating.
  2. Observe CPU Usage: While the submission is pending (i.e., still being processed), use system monitoring tools like top or htop to observe CPU usage.
  3. Identify High CPU Usage: Look for sustained high CPU usage associated with the launchpad_submitter.py process. You might see it consuming close to 100% of a CPU core.

If you observe this pattern, it's a strong indication that the busy-wait loop is the culprit. The process is constantly active, consuming CPU resources even when it's essentially waiting for an external event (the job status change).

Observed Behavior

The key symptom is sustained high CPU usage, even when the process is primarily waiting. This contrasts with a more efficient approach where the process would sleep or yield CPU time while waiting for updates.

Suggested Fixes

Alright, let's get to the good stuff – how to fix this! Here are some suggested solutions to mitigate the high CPU usage caused by the busy-wait loop:

  1. Implement time.sleep():

    • Replace the tight polling loops with time.sleep(...). This introduces a delay between each poll, allowing the CPU to rest. A reasonable default interval would be between 1–5 seconds, but this should be configurable.

    Implementation: Insert a time.sleep(interval) call within the loop, where interval is the sleep duration in seconds. This tells the script to pause execution for the specified time before checking the status again. For example:

import time

while not job_done:
    status = check_job_status()
    if status == "completed":
        job_done = True
    else:
        time.sleep(2)  # Sleep for 2 seconds

  1. Implement Exponential Backoff:

    • Use exponential backoff for repeated polls. This means starting with a short sleep interval and gradually increasing it if the status remains unchanged. This is useful because the longer the process has been running, the less frequently it's necessary to check.

    Implementation: Start with a small sleep interval and increase it by a factor (e.g., 1.5 or 2) after each unsuccessful poll, up to a maximum value. This approach reduces CPU usage over time and is particularly beneficial when submissions take a while to complete. For example:

import time

wait_time = 1
max_wait = 30
while not job_done:
    status = check_job_status()
    if status == "completed":
        job_done = True
    else:
        time.sleep(wait_time)
        wait_time = min(wait_time * 1.5, max_wait)  # Increase wait time, up to a limit

  1. Use Event-Driven APIs:

    • If Launchpad or the HTTP client supports long-polling or webhooks, use these event-driven APIs. Instead of constantly polling, the server will notify the client when there's a status change. This is the most efficient approach, as it eliminates the need for polling altogether.

    Implementation: Investigate whether Launchpad provides a mechanism for receiving push notifications when a job's status changes. If so, re-architect the script to subscribe to these notifications instead of actively polling. The specific implementation will depend on the Launchpad API.

  2. Add Instrumentation and Configuration:

    • Add instrumentation to measure poll frequency and provide a configuration option to tweak it. This allows users to adjust the polling behavior based on their specific needs and system resources.

    Implementation: Introduce a configuration parameter (e.g., in a configuration file or command-line argument) that controls the polling interval. Also, add logging statements to track how frequently the script is polling, allowing for performance analysis and tuning. For example:

import time
import logging

# Read poll interval from config (default to 5 seconds)
poll_interval = config.get("poll_interval", 5)

while not job_done:
    status = check_job_status()
    if status == "completed":
        job_done = True
    else:
        logging.debug(f"Polling, sleeping for {poll_interval} seconds")
        time.sleep(poll_interval)

Location in Code

You can find the problematic polling loop in launchpad_submitter.py. Look for the section of code that checks the submission or job status. This is where you'll need to apply the suggested fixes.

Impact and Priority

The impact of this issue is significant. It affects CI costs, reduces responsiveness on low-powered systems, and wastes CPU cycles. While it doesn't directly cause incorrect results (correctness), it does negatively impact performance. Therefore, the priority is medium – addressing it will improve the overall efficiency and resource utilization of the system.

By implementing these fixes, we can significantly reduce CPU usage and improve the performance of launchpad_submitter.py. Let's make our systems more efficient and reduce unnecessary energy consumption! Happy coding, everyone!