How-To Article: Optimizing and Troubleshooting Memory on HPC Clusters

1. Document Scope

This guide is tailored for HPC (High Performance Computing) users, developers, and administrators who want to diagnose and enhance memory usage within cluster environments. It details a step-by-step approach—from identifying memory bottlenecks and common errors to applying advanced optimizations that improve application performance and prevent out-of-memory (OOM) issues.

Target Audience

• Researchers/Scientists running large-scale simulations or data processing
• HPC Application Developers needing to debug and tune parallel applications
• Systems Administrators managing cluster resources and seeking to optimize utilization

Prerequisites

• Basic familiarity with Linux/Unix commands
• Understanding of job schedulers (e.g., Slurm, PBS)
• Access to memory debugging tools (Valgrind, Intel Inspector, etc.)
• Some knowledge of parallel programming (MPI, OpenMP)

2. Steps to Optimize and Troubleshoot Memory

Step 1: Assess Current Memory Usage

1. Check Allocated vs. Actual Memory

   • Slurm Example:

     sacct -j <job_id> --format=JobID,MaxRSS,AveRSS,Elapsed,State
     

     • MaxRSS indicates your peak memory usage per job step.
   • PBS Example:

     qstat -fx <job_id> | grep resources_used.mem
     

   • Interpretation: If MaxRSS (or resources_used.mem) is near your requested memory, you risk hitting OOM or memory-related slowdowns.

2. Live Monitoring

   • Commands:

     free -h       # Human-readable memory overview
     top / htop    # Real-time process listing, sorted by memory use
     

   • Interpretation: Quickly reveals which processes are potential memory hogs.

3. Review Scheduler Logs for Errors

   • Slurm: Check slurmd.log on compute nodes for OOM messages.
   • PBS: Review mom_logs on the node where the job ran.

Step 2: Identify Memory Errors and Leaks

1. Valgrind (Single-Node Debugging)

   • Command:

     valgrind --leak-check=full --track-origins=yes ./my_hpc_app
     

   • Interpretation: Flags memory leaks, invalid writes/reads, and uninitialized variables. Especially useful before scaling out.

2. Intel Inspector

   • Command:

     inspxe-cl -collect mi1 -- ./my_hpc_app
     

   • Interpretation: Detects memory and threading issues in HPC applications compiled with Intel compilers.

3. ARM DDT

   • Command:

     ddt mpirun -n <n_procs> ./my_hpc_app
     

   • Interpretation: Parallel debugger to locate memory corruption or leaks across MPI ranks.

4. CUDA memcheck (GPU Focus)

   • Command:

     cuda-memcheck ./my_cuda_app
     

   • Interpretation: Identifies out-of-bounds or misaligned accesses in GPU kernels.

Step 3: Profile Memory for Bottlenecks

1. HPCToolkit

   • Command:

     hpcrun -e MEM_UOPS_RETIRED -o hpctoolkit-out ./my_hpc_app
     hpcviewer hpctoolkit-out
     

   • Interpretation: Displays call-path profiles linking heavy memory usage to specific functions.

2. Intel VTune Profiler

   • Command:

     amplxe-cl -collect memory-access -- ./my_hpc_app
     

   • Interpretation: Identifies cache misses, bandwidth bottlenecks, and NUMA imbalances.

3. ARM MAP

   • Command:

     map --profile ./my_hpc_app
     

   • Interpretation: Time-based memory consumption view, showing when and where usage spikes.

Step 4: Optimize Memory Usage

1. Refine Data Structures

   • Why: Large arrays, unaligned data, or frequent temporary allocations can increase cache misses and fragmentation.
   • Action:
     • Use blocking or tiling for matrix operations.
     • Align arrays to cache boundaries (often 64-byte alignment).
     • Reuse buffers instead of allocating/deallocating repeatedly.

2. Leverage NUMA Binding

   • Commands:

     numactl --hardware
     numactl --cpunodebind=0 --membind=0 ./my_hpc_app
     

   • Interpretation: Binding an application to a local NUMA domain can lower memory access latency and improve performance.

3. Enable Huge Pages

   • Why: Large pages reduce TLB misses and overhead for memory-intensive codes.
   • Action: Some clusters offer modules like module load craype-hugepages2M; consult documentation for activation methods.

4. Tune MPI Buffer Settings

   • Why: Overly large or small MPI buffers can degrade performance or use excessive memory.
   • Action: Check your MPI library documentation for environment variables (e.g., OMPI_MCA_btl_vader_single_copy_mechanism) that influence shared memory usage.

5. Schedule Memory Appropriately

   • Slurm:

     #SBATCH --mem=64G
     

   • PBS:

     #PBS -l mem=64gb
     

   • Interpretation: Update requests to closely match observed usage (with some buffer) to prevent OOM and conserve resources.

Step 5: Validate Changes and Scale

1. Re-Test on a Single Node

   • Verify that memory leaks and errors identified in Step 2 are resolved.
   • Use profilers or debuggers again to confirm improved performance.

2. Incremental Scaling

   • Increase node count and compare memory usage trends with commands such as sacct (Slurm) or qstat (PBS).
   • Check if performance gains persist or if new bottlenecks emerge at larger scales.

3. Continuous Monitoring

   • Integrate memory checks into a CI pipeline if available.
   • Regularly profile workloads as problem sizes and codebases evolve.

3. Conclusion

Optimizing and troubleshooting memory on HPC clusters involves a multi-faceted approach—analyzing usage patterns, detecting leaks or corruption, and applying targeted optimizations like NUMA binding and huge pages. By systematically profiling and tuning your application, you can dramatically reduce the likelihood of OOM errors, improve node utilization, and accelerate time to solution.

Key Takeaways

• Collect Data First: Use job scheduler commands and debug logs to pinpoint memory trouble spots before making adjustments.
• Employ Specialized Tools: Valgrind, Intel Inspector, ARM DDT, and GPU-specific tools (like CUDA memcheck) each address distinct segments of the memory debugging puzzle.
• Optimize in Layers: Start with data layout and algorithmic improvements, then consider system-level features like NUMA binding and huge pages.
• Iterate and Scale: Confirm each optimization on a small set of nodes before moving to large production runs, keeping an eye on new or recurring memory issues.

By following these steps and best practices, HPC practitioners can maintain stable, high-performing applications that make optimal use of cluster memory resources.