Fixing Libhpc Slurm Sub-task Issues: A HADDOCK3 Guide

Hey there, HADDOCK3 users and HPC enthusiasts! If you've ever found your Slurm jobs mysteriously stalling or your cluster resources getting tangled up when running HADDOCK3, you're definitely not alone. We're diving deep into a super important fix that addresses how libhpc—a critical component of HADDOCK3—handles computing tasks under the Slurm workload manager. This isn't just a minor tweak; it's about ensuring your HADDOCK3 pipeline runs smoothly, efficiently, and without any frustrating resource allocation hiccups or unexpected pipeline freezing. We’re talking about making your computational life a whole lot easier by understanding and resolving issues related to sub-tasks and their respect (or lack thereof) for your carefully crafted SBATCH directives. This guide will walk you through the problem, explain why it happened, and show you the brilliant solution that prevents CPU oversubscription and ensures stable memory management within your HPC environment. Get ready to optimize your HADDOCK3 experience on Slurm and wave goodbye to those perplexing job queues!

This article aims to provide an in-depth, human-friendly explanation of a significant improvement to libhpc's interaction with Slurm, specifically focusing on how HADDOCK3 jobs manage their nested operations. Many of us have experienced the headache of submitting a job to a powerful Slurm cluster, only to see it crawl to a halt or, worse, get stuck in a pending state indefinitely. Often, the culprit lies in how sub-tasks are spawned. When you tell Slurm to allocate, say, 12 CPUs for your main task using --cpus-per-task=12, you expect all subsequent operations within that job to leverage those allocated resources. However, what we've discovered is that libhpc, in its original form, wasn't always playing by these rules for its sub-tasks. Instead of inheriting the parent job's resource allocations, it would often try to launch entirely new sbatch commands for its sub-tasks, each requesting a fresh, single CPU. This effectively meant that a single HADDOCK3 run could rapidly oversubscribe your available CPU cores, especially if you were running multiple instances of the script. Imagine having 48 cores on a node, running four haddock3 scripts, each requesting 12 CPUs. That's a perfect 48 CPU utilization for the main tasks. But then, if each main task tries to launch its own independent sub-tasks via new sbatch calls, each requesting just 1 CPU, those sub-tasks would immediately find no free CPUs. They’d sit there, pending, leading to a complete pipeline freezing and an utterly inefficient use of your valuable HPC resources. This isn't just frustrating; it’s a critical bottleneck for any serious computational work with HADDOCK3, making stable and predictable runs a challenge. We need a way for sub-tasks to respect the resource envelope set by the parent Slurm job, ensuring everything runs within the allocated boundaries and preventing this common pitfall of resource starvation. The goal is seamless, high-performance execution, not a queue of perpetually waiting sub-tasks.

The Root of the Problem: How libhpc Sub-tasks Were Causing Slurm Headaches

Alright, let's get down to the nitty-gritty of why your HADDOCK3 jobs might have been getting stuck. The core issue, folks, was how libhpc—which is essentially the engine driving many of HADDOCK3's operations—was handling its sub-tasks within an already running Slurm job. When you submit a Slurm script with directives like #SBATCH --cpus-per-task=12, you're telling the Slurm workload manager that your main task needs 12 CPU cores. The expectation is that any processes launched within that main task should then leverage those allocated resources and operate within that resource envelope. However, the original implementation in /haddock/libs/libhpc.py was a bit overzealous. Instead of running its sub-tasks as direct subprocesses that inherited the parent job's Slurm environment and resource allocations, it was trying to submit new, independent Slurm jobs for each sub-task by executing another sbatch command.

Think about it: you're already inside a Slurm-managed environment, and then your program tries to call sbatch again for a sub-task. This creates a weird scenario where a new Slurm job is requested from within an existing one. This new sbatch call, by default, would often request its own set of minimal resources, typically 1 CPU, because it wasn't aware of the parent job's existing allocation. So, if your main task was already using 12 of your node's 48 CPUs, and then it tried to spawn a bunch of sub-tasks that each requested another single CPU via a new sbatch call, Slurm would see these as entirely new requests for resources. If all your CPUs were already allocated by your main tasks (e.g., four haddock3 instances each taking 12 CPUs = 48 CPUs total), these new sub-task sbatch requests would just sit there, pending, forever. They'd never get scheduled because there were no unallocated CPUs left on the node or even the cluster, leading to a complete pipeline freezing. This wasn't a problem with Slurm itself, but rather how libhpc was instructing Slurm to handle its children. It was like a child asking for a new car when the family already had one parked in the driveway, ready to go!

Beyond the CPU conundrum, there was another sneaky issue related to memory management. The original setup, if not explicitly told otherwise, could lead to sub-tasks trying to grab the entire available memory for themselves, even if the parent job had already specified a reasonable limit. This, too, could cause pipeline instability and freezing. Imagine your HADDOCK3 run being allocated 5GB of RAM. If a sub-task then, by default, tries to request all memory on the node (or at least a very large chunk that isn't explicitly limited), it could clash with other sub-tasks or even the main task, leading to memory oversubscription and job crashes. This is why explicit memory directives for sub-tasks are crucial, as we’ll see in the solution. These combined issues of CPU oversubscription and uncontrolled memory requests were significant roadblocks for anyone aiming for efficient and stable HADDOCK3 runs on an HPC cluster. It highlighted a clear need for libhpc to behave as a more