44.1 Best Practices for HPC in Data Analysis

  1. Test First on a Small Scale

Before scaling your analysis to full production, begin with small-scale tests. This ensures your code runs as expected, while also providing crucial information about resource usage.

  • Start small: Run the analysis on a subset of your data (e.g., 1% or 10% of the total).
  • Measure resource usage:
    • Execution time (wall clock time vs. CPU time).
    • Memory footprint.
    • CPU and disk I/O utilization.
  • Record the metrics:
    • Create logs or structured reports for future reference.

Example:
If you’re modeling customer churn on a dataset of 10 million records, start with 100,000 and profile its behavior.

  1. Estimate Resource Requirements

Use insights from small-scale tests to extrapolate the resources needed for the full analysis.

  • Estimate CPU cores, memory, and execution time requirements.
  • Add an overhead buffer:
    • Parallel tasks introduce communication overhead and synchronization delays.
    • Real-world data can have higher complexity than test data.

Guideline: If a small test consumes 1 GB of RAM and runs in 10 minutes on a single core, a 100x dataset may not scale linearly. Communication costs, disk I/O, and parallel inefficiencies could require a different scaling factor.

  1. Parallelize Strategically

Not all parts of your analysis benefit equally from parallelization. Identify and prioritize computational bottlenecks.

  • Analyze your workflow:
    • Data ingestion: Can you parallelize reading large files or querying databases?
    • Transformations: Are data wrangling tasks parallelizable?
    • Modeling/Training: Can independent model fits or simulations be distributed?
  • Balance granularity:
    • Overly fine-grained parallelization leads to high communication overhead.
    • Coarser tasks are generally more efficient in parallel environments.

Tip: Use embarrassingly parallel strategies where possible—these tasks require minimal communication between workers.

  1. Use Adequate Scheduling and Queue Systems

In HPC clusters, job scheduling systems manage resource allocation and prioritize workloads.

Common systems include:

  • Slurm (Simple Linux Utility for Resource Management)

  • PBS (Portable Batch System)

  • LSF (Load Sharing Facility)

  • SGE (Sun Grid Engine)

Best practices:

  • Write job submission scripts specifying:

    • Wall time limits.

    • Memory and CPU requests.

    • Node allocation (if required).

  • Monitor jobs:

    • Examine logs for resource utilization (memory, time, CPU load).

    • Use scheduler tools (e.g., sacct in Slurm) to assess historical performance.

Example Slurm job script:

#!/bin/bash
#SBATCH --job-name=churn_model
#SBATCH --output=churn_model_%j.out
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G
#SBATCH --time=04:00:00

module load R/4.2.0
Rscript run_churn_model.R
  1. Incremental Scaling

Resist the temptation to scale from small tests directly to massive production jobs.

  • Iterate gradually:

    • Start with small jobs, progress to medium-sized runs, and only then scale to full production.

  • Monitor I/O overhead:

    • Parallel jobs often stress shared storage systems.

    • Optimize data locality and prefetching if possible.

Tip: Use asynchronous job submission (batchtools::submitJobs() or future::future()) to manage job batches efficiently.

  1. Documentation and Reporting

Good documentation facilitates reproducibility and future optimization.

Maintain a structured report of each run, including:

  • Input parameters: Dataset size, preprocessing steps, model parameters.

  • Cluster specification: Number of nodes, CPUs per node, memory allocations.

  • Execution logs: Total run time, CPU utilization, memory usage.

  • Software environment: R version, package versions, job scheduler version.

Template for documentation:

Parameter Value
Dataset size 10 million records
Model type Random Forest (500 trees)
Nodes used 4
CPUs per node 8
Memory per node 32 GB
Wall time 2 hours
Software environment R 4.2.0, ranger 0.14.1
Scheduler Slurm 20.11.8