How-To Article: Memory Management and Debugging

1. Document Scope

This guide is intended for developers, researchers, and system administrators seeking to manage and debug memory usage in HPC (High Performance Computing) environments or other large-scale computing systems. It outlines a systematic approach to identifying, analyzing, and optimizing memory consumption, with a focus on tools and techniques commonly used in multi-node clusters.

Target Audience

• HPC Application Developers working with parallel programming (MPI, OpenMP, CUDA, etc.)
• System Administrators needing to ensure cluster stability and efficiency
• Researchers running large-scale data analytics or scientific simulations

Prerequisites

• Familiarity with basic Linux commands and job schedulers (e.g., Slurm, PBS)
• Some experience with profiling and debugging tools (Valgrind, Intel Inspector, etc.)
• Access to an HPC cluster or multi-node system, if performing distributed debugging

2. Steps to Manage and Debug Memory

Step 1: Assess Memory Requirements and Resource Requests

1. Identify Application Needs

   • Estimate how much memory your job will require (based on data size, number of processes, etc.).
   • Look at similar applications or reference benchmarks to gauge typical usage.

2. Set Appropriate Memory Limits

   • Slurm:

     #SBATCH --mem=32G
     

   • PBS:

     #PBS -l mem=32gb
     

   • Interpretation: Under-requesting memory can cause Out-of-Memory (OOM) kills, whereas over-requesting can waste resources and reduce cluster throughput.

3. Monitor Real-time Usage

   • Commands:

     free -h
     top / htop
     

   • Interpretation: Spot-check whether a single process or rank is consuming more memory than expected.

Step 2: Investigate Logs for Memory-Related Errors

1. Scheduler Logs

   • Slurm: Check slurmd.log on compute nodes for memory violations or OOM events.
   • PBS: Review mom_logs to see if the job exceeded its memory allocation.

2. System Logs

   • Commands:

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

   • Interpretation: The OOM killer may terminate the largest process if physical memory is insufficient.

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

1. Valgrind

   • Command:

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

   • Interpretation: Identifies memory leaks, invalid reads/writes, and uninitialized memory usage. Typically used on a single node for detailed debugging.

2. Intel Inspector

   • Command:

     inspxe-cl -collect mi1 -- ./my_app
     

   • Interpretation: Ideal for code compiled with Intel compilers; detects memory and threading errors.

3. ARM DDT

   • Command:

     ddt mpirun -n <num_procs> ./my_app
     

   • Interpretation: A parallel debugger that helps locate memory and threading bugs across multiple MPI ranks.

4. CUDA memcheck (For GPU Applications)

   • Command:

     cuda-memcheck ./my_cuda_app
     

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

Step 4: Profile Memory Usage and Performance

1. HPCToolkit

   • Command:

     hpcrun -e MEM_UOPS_RETIRED -o hpctoolkit-data ./my_app
     hpcviewer hpctoolkit-data
     

   • Interpretation: Generates call-path profiles, pinpointing functions responsible for excessive memory usage.

2. Intel VTune Profiler

   • Command:

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

   • Interpretation: Analyzes cache misses, bandwidth, and NUMA usage, helping detect memory bottlenecks.

3. ARM MAP

   • Command:

     map --profile ./my_app
     

   • Interpretation: Provides a time-based view of memory consumption, correlating spikes with specific code regions.

Step 5: Optimize and Tune Memory Usage

1. Revise Data Structures

   • Why: Large, contiguous arrays or aligned data structures can reduce fragmentation and cache misses.
   • Action: Apply loop blocking/tiling, minimize temporary buffers, and ensure alignment to CPU cache boundaries (often 64 bytes).

2. Leverage NUMA Awareness

   • Commands:

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

   • Interpretation: Keeping memory close to the CPU cores that use it reduces remote access latencies on multi-socket nodes.

3. Enable Huge Pages

   • Why: Larger page sizes (e.g., 2MB) reduce TLB misses for memory-intensive workloads.
   • Action: Some systems require loading a module like craype-hugepages2M; consult your HPC documentation to enable and configure.

4. MPI Buffer Tuning

   • Why: MPI library defaults might be too large or too small for your workload’s messaging patterns.
   • Action: Adjust environment variables (e.g., OMPI_MCA_btl_vader_single_copy_mechanism or eager/rendezvous settings) to optimize memory usage.

5. Adjust Scheduler Requests

   • Action: Increase or decrease requested memory based on the profiling data.
   • Goal: Ensure you have enough headroom to avoid OOM while not over-allocating resources.

Step 6: Validate Changes and Scale Up

1. Small-Scale Tests

   • Confirm that leaks and other issues flagged by Valgrind or profilers are resolved on a single node.
   • Check that any code modifications do not introduce new bugs or regressions.

2. Incremental Scaling

   • Gradually move from a few nodes to full production-scale runs, monitoring memory usage closely with sacct, qstat, or cluster monitoring tools (e.g., Prometheus, Grafana).

3. Continuous Integration (CI) & Regression Testing

   • Integrate memory checks into automated tests if possible, catching new leaks or performance regressions early in the development cycle.

3. Conclusion

Effective memory management and debugging are vital for achieving high performance, minimizing job failures, and maximizing resource efficiency in HPC environments. By thoroughly examining resource usage, leveraging specialized debugging and profiling tools, and tuning both the application code and system parameters, you can optimize your memory footprint and avoid out-of-memory errors.

Key Takeaways

• Match Requests to Actual Usage: Use scheduler logs and profiling tools to accurately gauge how much memory your application truly needs.
• Deploy the Right Tools: Valgrind, Intel Inspector, ARM DDT, and CUDA memcheck each cater to specific architectures and parallel models.
• Optimize Data Layout: Align data structures and reduce fragmentation for better cache utilization.
• Iterate and Validate: Always re-test after applying fixes or optimizations, starting small before scaling up to full production runs.

By following these best practices, HPC practitioners and developers can maintain efficient, robust applications that make the most of available compute resources.