Batch & Parallel Processing Integration is the bridge between "running one script" and "running science at scale."

In a standard IT environment, a server runs one web server or database indefinitely.

In HPC, we have thousands of disjointed tasks (Batch Jobs) and massive simulations spanning thousands of cores (Parallel Jobs).

• Batch Processing: "I have 10,000 photos to resize." (High Throughput, embarrassingly parallel).
• Parallel Computing: "I have one 3D model of a car crash that needs 1,000 CPUs to solve." (Tightly coupled, MPI).

Integration focuses on configuring the Scheduler (Slurm) and the Workflow Managers to handle both types efficiently on the same hardware without them fighting for resources.

Here is the detailed breakdown of the integration strategies, the workflow frameworks, and the configuration tuning, followed by the downloadable Word file.

1. The Challenge: Tetris with Different Shapes

The scheduler (Slurm) has to fit two very different types of blocks into the cluster:

1. The "Rocks" (Parallel MPI Jobs): Massive, rigid blocks. They need 500 cores all at once. If 499 are available, the job waits.
2. The "Sand" (Serial Batch Jobs): Tiny, flexible grains. They need 1 core for 10 minutes.

Integration Strategy: Use the "Sand" to fill the gaps around the "Rocks" (Backfilling). This raises cluster utilization from 60% to 95%.

2. Integration Frameworks

A. The Low-Level: Slurm Job Arrays

• Use Case: You need to run the same simulation on 1,000 different input files.
• Bad Way: Submit 1,000 separate jobs (sbatch run1.sh, sbatch run2.sh...). This floods the scheduler database and slows down the whole system.
• Integrated Way: Submit One Job Array (sbatch --array=1-1000 run.sh). Slurm treats it as a single object but spawns 1,000 tasks. It’s cleaner, faster, and easier to manage.

B. The High-Level: Scientific Workflow Managers (Nextflow / Snakemake)

• Problem: Real science is a pipeline. "Step 1: Download Data (Batch). Step 2: Clean Data (Batch). Step 3: Simulate (Parallel MPI). Step 4: Graph Results (Batch)."
• Integration: Tools like Nextflow integrate directly with Slurm.
• You write the pipeline in a simple script.
• Nextflow automatically submits Step 1 to the cluster.
• It waits for it to finish.
• It submits Step 3 only when Step 2 is done.
• If Step 3 crashes, it retries it automatically.

3. Configuration for Hybrid Performance

A. Partitioning Strategy

Don't mix Rocks and Sand in the same queue blindly.

• Partition batch: Configured with "Oversubscription" (Allowing 2 jobs to share 1 core) for light I/O tasks.
• Partition mpi: Configured with "Exclusive Mode" (One job owns the entire node) to ensure the CPU cache isn't polluted by other tasks.

B. Task Packing (The "Knapsack" Problem)

• If you have 100 tasks that each use 1 core, don't spread them across 100 nodes (1 task per node).
• Integration: Force the scheduler to Pack them. Put all 100 tasks onto just 2 nodes (50 tasks per node). This allows the other 98 nodes to remain empty for a big MPI job.

4. Key Applications & Tools

Category	Tool	Usage
Scheduler	Slurm	The engine. Handles Job Arrays and Dependencies (--dependency=afterok:1234).
Workflow Manager	Nextflow	The gold standard for Bioinfo and Data Science pipelines. Portable (runs on laptop or cluster).
	Snakemake	Python-based workflow manager. Very popular because of its readability.
Meta-Scheduling	Pegasus / HTCondor	For "High Throughput Computing" (HTC). Manages millions of tiny jobs across grid resources.
Parallelism	GNU Parallel	A simple command-line tool to turn a serial loop into a parallel job on a single node.