How-To Article: Memory Debugging and Optimization for HPC

1. Document Scope

This guide is tailored for users and administrators in High Performance Computing (HPC) environments who aim to diagnose, correct, and optimize memory usage in distributed applications. It covers strategies for pinpointing memory leaks, reducing overhead, and improving overall performance by tuning the HPC software environment and job submission parameters.

Target Audience

• Application developers targeting HPC systems
• HPC administrators responsible for cluster performance and stability
• Researchers and scientists running large-scale simulations or data analyses

Prerequisites

• Familiarity with basic Linux commands (e.g., top, free, ps)
• Understanding of job schedulers (e.g., Slurm, PBS, LSF)
• Basic knowledge of debugging tools (Valgrind, Intel Inspector, etc.)

2. Steps to Debug and Optimize Memory in HPC

Step 1: Confirm Job Resource Requests

1. Check Allocated vs. Consumed Memory

   • Slurm Example:

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

     • MaxRSS reports the maximum resident set size used by your job.
   • PBS Example:

     qstat -fx <job_id> | grep resources_used.mem
     

   • Interpretation: If your peak memory (MaxRSS) is near or exceeds what you requested, you risk out-of-memory errors or job termination. Adjust your --mem (Slurm) or -l mem= (PBS) parameters as needed.

2. Monitor Memory in Real-Time

   • Common Commands:

     free -h       # Shows total, used, and available system memory (human-readable)
     top / htop    # Interactive process monitoring, sorted by memory usage
     

   • Interpretation: These commands can help identify if a single process is consuming excessive memory.

Step 2: Investigate Memory-Related Errors

1. Review HPC Job Logs
   • Slurm: Check slurmd.log on the compute nodes for OOM killer messages.
   • PBS: Look in the MoM (Mother Superior) logs for memory violation errors.
2. Kernel Logs
   • Commands:

     dmesg | grep -i oom
     journalctl -k | grep -i oom
     

   • Interpretation: If the system runs out of memory, the Linux OOM killer may terminate the largest process (often the user’s HPC job).

Step 3: Use Debugging Tools to Identify Memory Leaks or Corruption

1. Valgrind (CPU-Focused)

   • Command:

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

   • Interpretation: Valgrind captures invalid reads/writes and memory leaks. Useful for developing and testing on a single node.

2. Intel Inspector

   • Command:

     inspxe-cl -collect mi1 -- ./my_hpc_app
     

   • Interpretation: Detects memory usage errors, race conditions, and more. Ideal for Intel-compiled applications on HPC systems.

3. CUDA memcheck (GPU-Focused)

   • Command:

     cuda-memcheck ./my_cuda_app
     

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

4. ARM DDT / GDB for MPI

   • Command:

     ddt mpirun -n 4 ./my_mpi_app
     gdb mpirun -n 4 ./my_mpi_app
     

   • Interpretation: Parallel debuggers allow you to step through MPI processes simultaneously, checking for memory anomalies in each rank.

Step 4: Profile Memory Usage for Optimization

1. Profiling Tools

   • HPCToolkit:

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

     • Helps map memory usage to specific call paths.
   • Intel VTune Profiler:

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

     • Analyzes memory bandwidth, cache misses, and NUMA usage.

2. Identify Hotspots

   • Focus on routines or loops using large arrays/buffers.
   • Evaluate data layouts—aligned or contiguous memory structures often yield better performance.

Step 5: Adjust Environment and Application Settings

1. NUMA Awareness

   • Commands:

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

   • Interpretation: Binding both CPU and memory to a local NUMA node can reduce remote memory access latency and improve performance.

2. Huge Pages

   • Large pages (2MB or more) can decrease TLB misses for memory-intensive workloads.
   • Check HPC Docs: Some systems provide modules, e.g., module load craype-hugepages2M or environment variables to enable huge pages.

3. MPI Optimizations

   • Consider using optimized MPI libraries (e.g., Intel MPI, Open MPI with UCX or FCA plugins).
   • Adjust environment variables to manage buffer sizes (e.g., btl_sm_eager_limit in Open MPI) if message buffering consumes excessive memory.

4. Data Structure Alignment & Blocking

   • Ensure large arrays are allocated in ways that favor cache locality (e.g., row-major vs. column-major).
   • Use blocking or tiling techniques to process chunks of data in memory-friendly sizes.

Step 6: Scale Up Testing Gradually

1. Single-Node Debug

   • Start with a single node or small subset of nodes under Valgrind, Intel Inspector, or another debugger.
   • Fix any reported leaks or inefficiencies before large-scale runs.

2. Incremental Scaling

   • Increase node count and observe memory usage growth. Ensure memory usage per node remains stable.
   • Use your scheduler’s monitoring tools (sstat, sacct, qstat, etc.) or external monitoring solutions (e.g., Ganglia, Prometheus).

3. Automated Testing & CI

   • Integrate memory profiling in a continuous integration pipeline where feasible.
   • Early detection of memory regressions can save significant time on large runs.

3. Conclusion

Memory debugging and optimization in HPC is a multi-step process that includes verifying job resource requests, analyzing system logs, employing specialized debuggers, and fine-tuning both the system environment and application code. By systematically applying these strategies—ranging from Valgrind checks on a single node to advanced profiling at scale—you can minimize out-of-memory errors, reduce resource contention, and maximize overall performance.

Key Takeaways

• Know Your Limits: Ensure job memory requests match or exceed peak usage.
• Use the Right Tool for the Job: CPU vs. GPU vs. hybrid HPC environments each benefit from specialized debuggers and profilers.
• Optimize for Data Access: NUMA binding, huge pages, and cache-friendly data structures can significantly reduce memory bottlenecks.
• Iterate, Test, and Scale: Start small with rigorous tests, then scale up while monitoring usage trends and applying continuous improvements.

By following these steps, HPC users and administrators can achieve efficient memory utilization, leading to faster, more reliable scientific computations and data analytics.