Performance Problems

Your job is running slower than expected? Here’s how to diagnose and improve performance.

Common Performance Issues

1. I/O Bottleneck

Symptoms: High wait time, low CPU utilization

Solutions:

  • Read/write data in larger chunks

  • Use parallel I/O (MPI-IO, HDF5)

  • Avoid writing to $HOME (use $WORK or local disk)

  • Cache frequently accessed data

2. Memory Issues

Symptoms: Swapping, slow performance

Solutions:

  • Request more memory

  • Optimize memory usage in code

  • Use memory-mapped files for large datasets

3. Network Latency

Symptoms: Slow MPI communication

Solutions:

  • Use InfiniBand (not Ethernet) for MPI

  • Optimize communication patterns

  • Use collective operations instead of point-to-point

4. Not Using Enough Resources

Symptoms: Job completes quickly but results are slow

Solutions:

  • Use more cores if algorithm is parallelizable

  • Try MPI instead of OpenMP (or vice versa)

  • Consider GPU acceleration for suitable workloads

Profiling Tools

CPU Profiling

# Load profiling module
module load gprof/perf

# Profile your code
perf record ./your_program
perf report

Memory Profiling

# Check memory usage
module load valgrind
valgrind --tool=massif ./your_program

Getting Help

If performance issues persist:

  1. Profile your application

  2. Check if it’s I/O or CPU bound

  3. Contact help@hpc.msstate.edu with profiling results