Health checks for HPC virtual machines
Azure provides several automated tests to help identify unhealthy virtual machines. By using these automated tests, you can remove an unhealthy virtual machine (VM) from the fleet when they're discovered, and before customers have a chance to access them. However, sometimes these automated tests don't catch every unhealthy VM.
Note
The automated health checks in Azure are continually updated and improved.
Some high-performance computing (HPC) applications, especially HPC applications that are tightly coupled, are sensitive to unhealthy VMs. This sensitivity can cause an HPC application to have a runtime failure or run more slowly than expected. The chances that you might encounter an unhealthy VM increase as the scale of your tightly coupled HPC application increases.
As a precaution, you should run your own health checks, especially when you're running tightly coupled HPC applications at scale. Running your own health checks increases the probability of running your HPC applications optimally at scale.
We recommend that your VM health checks verify that the following components are working and performing as expected: InfiniBand network, memory, storage, and CPU cores.
InfiniBand health checks
In most scenarios, measuring point-to-point InfiniBand latency and bandwidth via a ping-pong test (sending and receiving a message) is enough to verify the health of the InfiniBand network.
The Intel IMP-MPI1 PingPong test is a useful option because it's prebuilt and available on the CentOS HPC 7.7+ image in Azure Marketplace. There's a version included in the Intel MPI (message-passing interface) environment and a version in the HPC-X MPI environment, though you typically get the best latency and bandwidth performance by using the HPC-X version. Scripts to run Intel IMP-MPI1 PingPong tests are available in the IMB-MPI benchmarks project.
The following table illustrates the expected InfiniBand network latency and bandwidth measured through an HPC-X Intel IMB-IMP1 PingPong benchmark on HPC VMs. Values that are outside the following ranges are considered unhealthy.
| VM SKU | Expected network latency | Expected network bandwidth |
|---|---|---|
| HB120_v2 | < 2 μs | > 23.0 GB/s |
| HC44 | < 2 μs | > 11.5 GB/s |
| HB60 | < 2 μs | > 11.5 GB/s |
Other tools and benchmarks to consider are osu-micro-benchmarks and Mellanox ClusterKit. The latter is also preinstalled in the CentOS HPC 7.7+ Azure Marketplace image in the HPC-X environment. Example ClusterKit scripts are available in the Azure health-check scripts project.
Memory health checks
Measuring the memory bandwidth is a good way to verify that a VM's memory is healthy. Intel Memory Latency Checker (MLC) is a useful tool for benchmarking the memory bandwidth. Other options are to build and run the stream benchmark or use ClusterKit, which can also measure memory bandwidth. You can find example scripts in the Azure health-check scripts project.
The following table shows the expected measured memory bandwidth on each of the HPC VMs running Intel MLC. Memory bandwidths that are outside these ranges are considered unhealthy.
| VM SKU | Expected memory bandwidth |
|---|---|
| HB120_v2 | > 300 GB/s |
| HC44 | > 200 GB/s |
| HB60 | > 250 GB/s |
Storage health checks
It's important to understand what storage solution you're using and its expected I/O performance, and then verify the expected I/O performance by running an I/O benchmark. You can choose from several I/O benchmark tools:
To measure your storage health, you create the maximum I/O throughput to generate an IOPS benchmark. Then you compare your measured I/O benchmark results with the expected I/O performance for your storage solution.
You can find example IOR scripts in the IOR and mdtest project. You can find example FIO scripts in the FIO project.
CPU core health
A great way to verify that your CPU cores are healthy and performing as expected is to measure the maximum floating-point operations per second (FLOPS). Then, compare your measured results with the theoretical expected FLOPS.
You can use High Performance Linpack (HPL) to measure maximum FLOPS. A side benefit of running HPL is that it can help to identify some bad memory DIMMs (dual in-line memory modules). Also, it can help assess the overall health of the HPC cluster, because it performs some limited testing for tightly coupled communication.
HPC datacenters often use HPL to burn in a new HPC system. You can find example HPL scripts in the linpack project.