Troubleshooting Poor RCCL Performance On AMD Instinct MI300X With Mpirun

by James Vasile 73 views

Hey guys! Let's dive into a tricky situation where someone is facing poor performance while running RCCL tests on two AMD Instinct MI300X nodes. This can be a real head-scratcher, so let's break it down and see what might be going on.

Problem Overview

The user is running RCCL tests on a setup with two nodes, each equipped with AMD Instinct MI300X GPUs. They've compiled the rccl-tests following the steps outlined in the ROCm documentation. However, when running the tests using mpirun, the performance is significantly lower than expected.

Key Components and Configuration

To get a clearer picture, here's a rundown of the system and configuration:

  • Operating System: Ubuntu 22.04.5 LTS (Jammy Jellyfish)
  • CPU: AMD EPYC 9534 64-Core Processor
  • GPUs: AMD Instinct MI300X
  • ROCm Version: 6.2.0
  • MPI Configuration: The user has configured MPI with a hostfile containing the private IP addresses of the nodes, along with slots=8. This indicates that each node is expected to handle 8 MPI processes.
  • Command: The mpirun command used is:
    HSA_NO_SCRATCH_RECLAIM=1 mpirun -np 16 --hostfile hostfile.txt --bind-to numa ./all_reduce_perf -b 8 -e 128M -f 2 -g 8
    Let's break this down:
    • HSA_NO_SCRATCH_RECLAIM=1: This environment variable can sometimes help with memory management.
    • mpirun -np 16: This tells MPI to run 16 processes in total.
    • --hostfile hostfile.txt: Specifies the file containing the list of hosts and slots.
    • --bind-to numa: This option binds MPI processes to NUMA nodes, which can improve performance by keeping memory access local.
    • ./all_reduce_perf: This is the RCCL test being run.
    • -b 8 -e 128M: These parameters likely define the range of message sizes for the test (from 8 bytes to 128MB).
    • -f 2: This could relate to the number of iterations or a similar factor.
    • -g 8: This might specify the number of GPUs to use per process, in this case 8.

The Performance Issue

The main problem is that the performance results, which are attached in the all_reduce_test.txt file, indicate suboptimal behavior. We don't have the exact numbers here, but the user's concern suggests that the achieved bandwidth and latency are not meeting expectations for this hardware.

Potential Bottlenecks and Solutions

Okay, so where do we start troubleshooting? There are several factors that could contribute to poor performance in a multi-GPU, multi-node setup. Let's explore some of the most common culprits:

1. Network Interconnect

  • The Importance of Network: In a multi-node setup, the network interconnect is crucial. Data needs to be transferred between nodes, and the speed and latency of this communication directly impact overall performance. If the network is slow or congested, it becomes a major bottleneck.
  • Ethernet vs. InfiniBand: Typically, high-performance computing (HPC) systems rely on low-latency, high-bandwidth interconnects like InfiniBand. Standard Ethernet, while ubiquitous, might not be sufficient for demanding workloads that require intense inter-node communication.
  • Checking the Interconnect: We need to verify the type of network interconnect being used. Is it Ethernet? If so, what speed (e.g., 10GbE, 25GbE, 100GbE)? Is it InfiniBand? If so, what generation (e.g., EDR, HDR)? The lower the bandwidth and higher the latency of the interconnect, the more it will limit performance.
  • Troubleshooting Steps:
    • Identify the Interconnect: Use tools like lspci or network monitoring utilities to determine the network hardware and its capabilities.
    • Check Network Configuration: Ensure that the network interfaces are properly configured and that there are no obvious issues like incorrect IP addresses or subnet masks.
    • Run Network Benchmarks: Use tools like iperf3 or netperf to measure the actual bandwidth and latency between the nodes. This will help you identify if the network is performing as expected.
    • Key Takeaway: A slow network interconnect is a prime suspect in multi-node performance issues.**

2. NUMA Configuration

  • Understanding NUMA: Non-Uniform Memory Access (NUMA) is a memory architecture used in multi-processor systems. Each processor (or socket) has its own local memory, and accessing memory that is local to a processor is much faster than accessing memory on a different processor. Think of it like having your desk right next to a filing cabinet versus having to walk across the office.
  • The --bind-to numa Option: The user is using the --bind-to numa option in mpirun, which is a good practice. This tells MPI to try to run processes on the same NUMA node as the GPUs they are using. However, it's crucial to ensure that this binding is actually happening correctly and that the processes are not being spread across NUMA nodes unnecessarily.
  • Incorrect Binding: If processes are not correctly bound to NUMA nodes, they might be accessing memory on a different node, leading to significant performance degradation.
  • Troubleshooting Steps:
    • Verify NUMA Configuration: Use tools like numactl --hardware to check the NUMA configuration of the system. This will show you how many NUMA nodes there are and how the CPUs and memory are distributed.
    • Check Process Placement: After running the mpirun command, use tools like htop or ps to see where the MPI processes are actually running. Are they all on the expected NUMA nodes?
    • Experiment with Binding Strategies: Sometimes, different NUMA binding strategies can yield better results. You can try using options like --cpus-per-proc or --map-by ppr:X:node to fine-tune process placement.
    • Key Takeaway: Proper NUMA binding is essential for performance. Verify that processes are running on the correct NUMA nodes.**

3. ROCm and Driver Issues

  • ROCm Version Compatibility: ROCm is constantly evolving, and sometimes there can be compatibility issues between different versions of ROCm, the drivers, and the RCCL library. Using the correct versions is critical.
  • Driver Bugs: Like any software, drivers can have bugs that affect performance. If you're seeing unexpected behavior, it's worth checking if there are known issues with the ROCm drivers for your GPUs.
  • Troubleshooting Steps:
    • Verify ROCm Installation: Double-check that ROCm is installed correctly and that all the necessary components are present. The ROCm documentation provides detailed instructions for installation and verification.
    • Check Driver Versions: Ensure that you are using the recommended driver version for your ROCm version and GPUs. You might want to try upgrading or downgrading drivers to see if it resolves the issue.
    • Look for Known Issues: Search the ROCm issue tracker and forums for any reports of similar performance problems with your hardware and software configuration. There might be known workarounds or fixes available.
    • Key Takeaway: ROCm version mismatches or driver bugs can severely impact performance. Ensure compatibility and look for known issues.**

4. RCCL Configuration and Version

  • RCCL's Role: RCCL (ROCm Collective Communication Library) is the library that handles communication between GPUs. It's a critical component for multi-GPU performance.
  • RCCL Version Compatibility: Similar to ROCm, using a compatible version of RCCL is crucial. RCCL needs to be aligned with the ROCm version.
  • RCCL Configuration Options: RCCL has various configuration options that can affect performance. These options control things like the communication algorithms used and the amount of memory allocated for buffers.
  • Troubleshooting Steps:
    • Verify RCCL Installation: Ensure that RCCL is installed correctly and that the necessary libraries are in the system's library path.
    • Check RCCL Version: Determine the version of RCCL being used and ensure it is compatible with your ROCm version.
    • Experiment with RCCL Environment Variables: RCCL uses environment variables to control its behavior. You can try setting variables like RCCL_IB_HCA, RCCL_IB_PORT, and RCCL_DEBUG to tune performance or get more information about RCCL's operation.
    • Key Takeaway: RCCL is at the heart of multi-GPU communication. Verify its installation, version, and configuration.**

5. MPI Implementation and Configuration

  • MPI's Role: MPI (Message Passing Interface) is the standard for inter-process communication in HPC. It's used to coordinate the work between different processes running on different nodes.
  • MPI Implementation: There are several MPI implementations available (e.g., OpenMPI, MPICH). The choice of MPI implementation and its configuration can affect performance.
  • MPI Binding: We've already discussed NUMA binding, but MPI also has its own mechanisms for binding processes to cores and nodes. Incorrect binding can lead to performance problems.
  • Troubleshooting Steps:
    • Identify MPI Implementation: Determine which MPI implementation you are using.
    • Check MPI Configuration: Ensure that MPI is configured correctly for your network and hardware. This might involve setting environment variables or modifying configuration files.
    • Experiment with MPI Binding Options: MPI provides various options for controlling process placement. Try different options to see if they improve performance.
    • Key Takeaway: MPI is the foundation for multi-node communication. Choose a suitable implementation and configure it correctly.**

6. Application Code and Workload

  • Communication Patterns: The communication patterns of your application can significantly impact performance. Applications that involve frequent small messages might be more sensitive to network latency than applications that send large messages infrequently.
  • Workload Balance: If the workload is not evenly distributed across the GPUs, some GPUs might be idle while others are overloaded, leading to poor overall performance.
  • Troubleshooting Steps:
    • Profile the Application: Use profiling tools to identify communication bottlenecks and workload imbalances.
    • Optimize Communication: Try to minimize the amount of communication between GPUs. Use collective communication operations (like Allreduce) efficiently.
    • Balance the Workload: Ensure that the work is evenly distributed across the GPUs.
    • Key Takeaway: The application's communication patterns and workload balance are critical factors. Profile your application to identify bottlenecks.**

7. Hardware Issues

  • Hardware Failures: Although less common, hardware failures (e.g., a faulty network card, a failing GPU) can cause performance problems.
  • Overheating: If the GPUs are overheating, they might be throttling their performance to prevent damage.
  • Troubleshooting Steps:
    • Monitor Hardware Health: Use tools to monitor the health of your hardware (e.g., GPU temperature, network interface status).
    • Run Hardware Diagnostics: Run diagnostic tests to check for hardware failures.
    • Key Takeaway: Don't rule out hardware issues. Monitor hardware health and run diagnostics if necessary.**

Analyzing the Provided Information

Now, let's circle back to the information provided by the user. We know the following:

  • Ubuntu 22.04.5 LTS
  • AMD EPYC 9534 64-Core Processors
  • AMD Instinct MI300X GPUs
  • ROCm 6.2.0
  • mpirun command with HSA_NO_SCRATCH_RECLAIM=1, -np 16, --hostfile, --bind-to numa, and specific parameters for all_reduce_perf

Based on this, here are some initial thoughts and steps:

  1. Network: We need to determine the network interconnect being used. Is it Ethernet or InfiniBand? If Ethernet, what speed? If InfiniBand, what generation?
  2. NUMA Binding: The --bind-to numa option is good, but we need to verify that it's working correctly. Use numactl --hardware and check process placement with htop or ps.
  3. RCCL Tests: The user is running all_reduce_perf. This test is a good starting point, but it might be helpful to run other RCCL tests to get a more complete picture of performance.
  4. Environment Variables: The HSA_NO_SCRATCH_RECLAIM=1 variable is used. It might be worth experimenting with other ROCm and RCCL environment variables.
  5. Output File: The all_reduce_test.txt file contains the performance results. Analyzing this file will be crucial to understanding the specific bottlenecks.

Next Steps for the User

To help the user move forward, here are some specific actions they can take:

  1. Provide Network Details: Share information about the network interconnect being used (Ethernet or InfiniBand, speed, generation).
  2. Run numactl --hardware: Share the output of this command to show the NUMA configuration.
  3. Check Process Placement: After running mpirun, use htop or ps to verify that the processes are bound to the correct NUMA nodes.
  4. Share all_reduce_test.txt Contents: Ideally, share the actual numbers from the output file so we can see the bandwidth and latency results.
  5. Experiment with Environment Variables: Try setting different ROCm and RCCL environment variables (e.g., RCCL_DEBUG=INFO) to see if they provide any insights.

Conclusion

Troubleshooting performance issues in multi-GPU, multi-node systems can be complex, but by systematically investigating potential bottlenecks, we can usually pinpoint the root cause. In this case, the network interconnect, NUMA configuration, ROCm and driver compatibility, RCCL setup, MPI configuration, application code, and even hardware issues could be playing a role. By gathering more information and experimenting with different settings, we can help the user achieve the performance they expect from their AMD Instinct MI300X GPUs.

Let's get those GPUs humming!