Understanding NUMA: From JVM Settings To Linux Optimization
I often hear people talking about NUMA (Non-Uniform Memory Access) and its implications for system performance, but there's still a lot of confusion about what it actually is and how it affects our systems. Is it just an abbreviation, or is there more to it? This idea might have originated from misunderstandings about how modern processors and memory architectures work together.
What is NUMA and Why Does It Matter?
NUMA is a computer memory design used in multiprocessing, where the memory access time depends on the memory location relative to the processor. Under NUMA, a processor can access its own local memory faster than non-local memory (memory local to another processor or memory shared between processors). This architecture has become increasingly common in modern servers and high-performance computing environments.
The combinations that result in 'num' and 'numa' and all other combinations between prepositions (a, de, em, por) and indefinite articles (um, uns, uma, umas) are correct in Portuguese grammar, but when we talk about computing, NUMA specifically refers to this memory architecture that can significantly impact application performance.
Checking NUMA Capabilities on Your System
When hopping from Java garbage collection topics, I came across JVM settings for NUMA optimization. This made me curious about whether my CentOS server has NUMA capabilities or not. Is there a *nix command or utility that can help me determine this?
To check if your system supports NUMA, you can use several commands. The most straightforward is:
numactl --hardware This command will show you the NUMA topology of your system, including the number of nodes, their CPU and memory distribution, and the distance between nodes. Another useful command is:
lscpu | grep -i numa This will tell you if NUMA is enabled and how many nodes your system has. You can also check the /proc filesystem:
cat /proc/cmdline Look for numa= parameters in the boot options to see if NUMA is enabled at the kernel level.
NUMA Balancer Configuration in Linux
A common question that arises is: Is NUMA balancer enabled by default in recent Linux versions? If so, how can I disable the NUMA balancer? Please let me know.
The NUMA balancer is indeed enabled by default in most modern Linux distributions. It's designed to automatically migrate tasks to improve memory locality and overall system performance. However, in some cases, you might want to disable it for specific workloads or testing purposes.
To check the current status of the NUMA balancer, you can use:
cat /proc/sys/kernel/numa_balancing A value of 1 means it's enabled, while 0 means it's disabled. To disable it temporarily:
echo 0 | sudo tee /proc/sys/kernel/numa_balancing For a permanent change, add this line to /etc/sysctl.conf:
kernel.numa_balancing = 0 Then apply the changes with:
sysctl -p NUMA Sensitivity and Application Optimization
My question is whether NUMA is enabled with those default options or is there something further I need to do to enable NUMA? Both are production-sized VMs, but the one on Azure I'm working with is a standard D-series instance.
NUMA is typically enabled by default if your hardware supports it, but the kernel needs to be configured to take advantage of it. In most cases, modern Linux distributions automatically detect and enable NUMA support during boot. However, you might need to adjust specific settings depending on your workload.
NUMA sensitivity first - I would question if you are really sure that your process is NUMA sensitive. In the vast majority of cases, processes are not NUMA sensitive, so then any optimization is unnecessary and might even hurt performance due to the overhead of managing NUMA-specific policies.
To determine if your application is NUMA-sensitive, you can use monitoring tools like numastat to observe memory allocation patterns:
numastat -c your_application If you see significant memory allocation across multiple NUMA nodes, your application might benefit from NUMA optimization. Otherwise, the default scheduling should be sufficient.
Addressing NUMA Node Memory Issues
The issue here is that some of your NUMA nodes aren't populated with any memory. Both are production-sized VMs, but the one on Azure I'm working with is a standard D-series instance with specific memory allocation patterns.
When NUMA nodes lack memory, it can lead to performance degradation as the system has to fetch data from distant memory nodes. This is particularly problematic in virtualized environments where the hypervisor's NUMA topology might not match the guest operating system's expectations.
To diagnose memory distribution across NUMA nodes:
numactl --hardware free -m Compare the total memory reported by free with the memory distribution shown by numactl. If there's a mismatch, you might need to adjust your VM's NUMA configuration or contact your cloud provider for assistance.
Best Practices for NUMA Optimization
For applications that are confirmed to be NUMA-sensitive, consider these optimization strategies:
Process binding: Use
numactlto bind processes to specific NUMA nodes:numactl --cpunodebind=0 --membind=0 your_applicationJVM NUMA optimization: For Java applications, enable NUMA-aware memory allocation:
java -XX:+UseNUMA your_applicationMemory interleaving: If your workload benefits from it, enable memory interleaving across NUMA nodes:
numactl --interleave=all your_applicationMonitor and tune: Regularly monitor your system's NUMA performance using tools like
numastat,numad, andperf.
Conclusion
Understanding and properly configuring NUMA can significantly impact your system's performance, especially for memory-intensive workloads. While NUMA is enabled by default in most modern systems, the key is determining whether your specific applications benefit from NUMA optimization.
Remember that NUMA optimization is not a one-size-fits-all solution. Many applications perform perfectly fine without any NUMA-specific tuning, and in some cases, trying to optimize for NUMA can actually introduce unnecessary complexity and overhead. Always start by monitoring your application's memory access patterns and only implement NUMA optimizations when you have clear evidence that they will provide measurable benefits.
By understanding your system's NUMA topology, using the right tools to monitor performance, and applying targeted optimizations only when necessary, you can ensure that your applications run at peak efficiency while avoiding the pitfalls of over-optimization.
