How-To Article: Diagnosing Memory Performance in HPC Applications on ARM Architectures

1. Document Scope

This guide is designed for scientists, researchers, and system administrators running High Performance Computing (HPC) applications on ARM-based clusters or supercomputers. It offers a step-by-step approach for identifying, analyzing, and resolving memory performance bottlenecks specific to ARM architectures. We will cover commonly used profiling tools, system commands, and best practices for performance tuning.

Target Audience

• HPC Application Developers needing to optimize for ARM-based CPU clusters.
• System Administrators responsible for monitoring and improving cluster-wide performance.
• Researchers running large simulations or data analytics on ARM-powered HPC systems.

Prerequisites

• Basic understanding of Linux commands and HPC job schedulers (e.g., Slurm, PBS).
• Familiarity with ARM Forge tools (Arm DDT, Arm MAP) or similar profiling/debugging suites.
• Access to an ARM-based HPC environment.

2. Steps to Diagnose Memory Performance

Step 1: Confirm Resource Requests and Usage

1. Check Job Scheduler Stats

   • Slurm Example:

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

     • MaxRSS shows your job’s peak resident set size.
   • PBS Example:

     qstat -fx <job_id> | grep resources_used.mem
     

   • Interpretation: If your actual memory usage often nears or exceeds the requested memory, out-of-memory (OOM) errors can occur, hurting performance or causing job terminations.

2. Real-Time Monitoring

   • Commands:

     free -h      # Displays free/used memory in human-readable format
     top / htop   # Monitors memory usage by active processes
     

   • Interpretation: Identify if a single process or rank is consuming excessive memory.

Step 2: Use ARM-Specific Profiling Tools

1. Arm MAP

   • Command:

     map --profile mpirun -n <num_procs> ./my_arm_app
     

   • Interpretation: Generates a timeline of CPU, memory, and I/O usage. MAP highlights memory spikes and shows which code regions trigger them.

2. Arm Performance Reports

   • Command:

     perf-report mpirun -n <num_procs> ./my_arm_app
     

   • Interpretation: Provides a high-level overview of your application’s performance, including memory bandwidth utilization and potential bottlenecks.

3. Arm DDT

   • Command:

     ddt mpirun -n <num_procs> ./my_arm_app
     

   • Interpretation: A parallel debugger that can detect memory corruption, leaks, or out-of-bounds accesses in MPI applications running on ARM architectures.

Step 3: Profile with General Tools (If ARM Forge Not Available)

1. Valgrind on ARM

   • Command:

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

   • Interpretation: Detects memory leaks, invalid reads/writes, and other issues. Best used on a single node or small-scale runs due to overhead.

2. Perf

   • Command:

     perf stat -e cache-references,cache-misses,cycles,instructions ./my_arm_app
     perf record -g ./my_arm_app
     perf report
     

   • Interpretation: Collects low-level performance counters on ARM CPUs, revealing cache miss rates or cycles spent on memory operations.

3. HPCToolkit

   • Command:

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

   • Interpretation: Shows call-path profiles to help pinpoint which functions or loops are responsible for high memory usage or bandwidth demands.

Step 4: Analyze Memory Layout and NUMA

1. NUMA Layout

   • Command:

     numactl --hardware
     

   • Interpretation: Reveals how many NUMA nodes exist on an ARM system. Local memory access is faster than remote (cross-node) memory access.

2. NUMA Binding

   • Command:

     numactl --cpunodebind=0 --membind=0 ./my_arm_app
     

   • Interpretation: Forces process and memory to the same NUMA node, reducing latency. If your ARM server has multiple NUMA nodes, bind each MPI rank to the nearest memory domain.

Step 5: Address Memory Bottlenecks

1. Data Structure Optimization

   • Why: Contiguous data structures can reduce memory fragmentation and improve cache performance.
   • Action: Restructure large arrays, use tiling/blocking for matrix operations, align data to cache line boundaries (usually 64 bytes on ARM).

2. Enable Large Pages (if supported)

   • Why: Larger pages (e.g., 2MB) can lower TLB miss rates, crucial for memory-intensive HPC.
   • Action: Check system documentation or consult your admin on enabling transparent huge pages or explicit huge pages on ARM-based nodes.

3. Revise Job Memory Requests

   • Slurm:

     #SBATCH --mem=64G
     

   • PBS:

     #PBS -l mem=64gb
     

   • Interpretation: Ensure your memory reservation matches the application’s peak usage uncovered by profiling.

4. MPI Buffers and Environment Variables

   • Why: Overly large MPI buffers can waste memory, while too small buffers might cause performance overhead.
   • Action: Tune Open MPI or MPICH environment variables (btl_vader_single_copy_mechanism, MPICH_GNI_MAX_EAGER_MSG_SIZE, etc.) depending on your system stack.

Step 6: Validate Changes at Scale

1. Small-Scale Testing

   • Apply the identified optimizations on a single node or a few nodes.
   • Re-run profiling tools (Arm MAP, Perf, etc.) to confirm improvements.

2. Gradual Scaling

   • Increase the number of nodes incrementally, monitoring memory usage with scheduler tools:

     sstat -j <job_id> --format=JobID,AveRSS,MaxRSS
     

   • Ensure that any gains persist at higher concurrency levels.

3. Ongoing Monitoring & Regression Testing

   • Integrate memory profiling or checks into your CI pipeline (if applicable).
   • Periodically re-run memory diagnostics on new code versions or data sets to catch regressions early.

3. Conclusion

Diagnosing memory performance on ARM architectures requires a careful blend of system-level inspection, specialized HPC profiling tools (such as Arm MAP or DDT), and strategic optimizations—ranging from data layout improvements to NUMA binding and large pages. By systematically collecting performance data, identifying bottlenecks, and testing your fixes at various scales, you can ensure your ARM-based HPC applications run efficiently and reliably.

Key Takeaways

• Collect Meaningful Data: Use Arm Forge (MAP, DDT) or alternative profilers (Valgrind, Perf) to uncover memory usage patterns.
• Optimize for NUMA: ARM systems with multiple NUMA nodes benefit from careful binding and local memory allocations.
• Data Layout Matters: Contiguity, alignment, and tiling can significantly reduce cache misses and improve memory bandwidth utilization.
• Iterate and Scale: Validate changes on a small scale, then escalate to a full production environment, continuously monitoring for new or recurring performance issues.

By following these steps and employing the right toolset for ARM architectures, HPC practitioners can confidently diagnose memory bottlenecks and achieve optimal application performance on modern ARM-based clusters.