44.3 Recommendations

  1. Incremental Testing
  • Always begin with a small fraction of the data to validate your HPC pipeline and catch issues early.
  • Collect critical metrics:
    • Run time (both wall clock time and CPU time).
    • CPU utilization (are all requested cores working efficiently?).
    • Memory usage (peak RAM consumption).
  • Use tools such as system.time(), profvis::profvis(), or Rprof() in R to profile your code locally before scaling.
  1. Resource Allocation
  • Use test metrics to guide your resource requests (memory, cores, and wall time).
  • Avoid:
    • Under-requesting: jobs may fail due to out-of-memory (OOM) errors or timeouts.
    • Over-requesting: results in longer queue times, resource underutilization, and potential policy violations on shared systems.
  • Rule of thumb:
    • Request slightly more memory than observed usage (e.g., 10-20% buffer).
    • Request wall time based on observed run time, plus an additional safety margin (e.g., 15-30%).

  1. Parallelization Strategy

    • Identify tasks that can be embarrassingly parallel:
      • Monte Carlo simulations.
      • Bootstrap resampling.
      • Model fitting on independent data partitions.
    • Focus on minimizing communication overhead:
      • Send only essential data between processes.
      • Use shared-memory parallelism when possible to avoid data duplication.
      • For distributed nodes, serialize and compress data before transmission when feasible.

  2. Monitoring and Logging

    • Use HPC job scheduler logs to track job performance:
      • Slurm: sacct, scontrol show job, or seff.
      • PBS/SGE: check standard output/error logs and resource summaries.
    • Capture R logs and console output:
      • Direct output to log files using sink() or by redirecting standard output in your job script.
    • Record:
      • Start and end times.
      • Memory and CPU metrics.
      • Warnings and error messages.

  3. Scaling

    • For large-scale runs (e.g., 100x your initial test), do not jump from small-scale to full-scale directly.
      • Run intermediate-scale tests (e.g., 10x, 50x).
      • Confirm resource usage scales as expected.
    • Watch for non-linear effects:
      • Increased I/O overhead from parallel file reads/writes.
      • Communication overhead in distributed tasks.
      • Load balancing inefficiencies (stragglers can delay job completion).

  4. Future-Proofing

    • HPC hardware and software evolve rapidly:
      • Faster networks (InfiniBand, RDMA).
      • Improved schedulers and resource managers.
      • Containerization (Docker, Singularity) for reproducible environments.
    • Regularly test and validate reproducibility after:
      • Software/package updates.
      • Hardware upgrades.
      • HPC policy changes (e.g., new quotas or job priorities).