NUMA工作原理

Contrasting the SMP/UMA and NUMA architectures

The SMP/UMA architecture

The SMP, or UMA architecture, simplified

When the PC world first got multiple processors, they were all arranged with equal access to all of the memory in the system. This is called , or sometimes Uniform Memory Architecture (UMA, especially in contrast to NUMA). In the past few years this architecture has been largely phased out between physical socketed processors, but is still alive and well today within a single processor with multiple cores: all cores have equal access to the memory bank.

The NUMA architecture

The NUMA architecture, simplified

The new architecture for multiple processors, starting with and ²processors (we’ll call these “modern PC CPUs”), is a architecture, or more correctly . In this architecture, each processor has a “local” bank of memory, to which it has much closer (lower latency) access. The whole system may still operate as one unit, and all memory is basically accessible from everywhere, but at a potentially higher latency and lower performance.

Fundamentally, some memory locations (“local” ones) are faster, that is, cost less to access, than other locations (“remote” ones attached to other processors). For a more detailed discussion of NUMA implementation and its support in Linux, see .

How Linux handles a NUMA system

Linux automatically understands when it’s running on a NUMA architecture system and does a few things:

1. Enumerates the hardware to understand the physical layout.
2. Divides the processors (not cores) into “nodes”. With modern PC processors, this means one node per physical processor, regardless of the number of cores present.
3. Attaches each memory module in the system to the node for the processor it is local to.
4. Collects cost information about inter-node communication (“distance” between nodes).

You can see how Linux enumerated your system’s NUMA layout using the numactl --hardwarecommand:

# numactl --hardware
available: 2 nodes (0-1)
node 0 size: 32276 MB
node 0 free: 26856 MB
node 1 size: 32320 MB
node 1 free: 26897 MB
node distances:
node   0   1 
  0:  10  21 
  1:  21  10 

This tells you a few important things:

• The number of nodes, and their node numbers — In this case there are two nodes numbered “0″ and “1″.
• The amount of memory available within each node — This machine has 64GB of memory total, and two physical (quad core) CPUs, so it has 32GB in each node. Note that the sizes aren’t exactly half of 64GB, and aren’t exactly equal, due to some memory being stolen from each node for whatever internal purposes the kernel has in mind.
• The “distance” between nodes — This is a representation of the cost of accessing memory located in (for example) Node 0 from Node 1. In this case, Linux claims a distance of “10″ for local memory and “21″ for non-local memory.

How NUMA changes things for Linux

Technically, as long as everything runs just fine, there’s no reason that being UMA or NUMA should change how things work at the OS level. However, if you’re to get the best possible performance (and indeed in some cases with extreme performance differences for non-local NUMA access, any performance at all) some additional work has to be done, directly dealing with the internals of NUMA. Linux does the following things which might be unexpected if you think of CPUs and memory as black boxes:

• Each process and thread inherits, from its parent, a NUMA policy. The inherited policy can be modified on a per-thread basis, and it defines the CPUs and even individual cores the process is allowed to be scheduled on, where it should be allocated memory from, and how strict to be about those two decisions.
• Each thread is initially allocated a “preferred” node to run on. The thread can be run elsewhere (if policy allows), but the scheduler attempts to ensure that it is always run on the preferred node.
• Memory allocated for the process is allocated on a particular node, by default “current”, which means the same node as the thread is preferred to run on. On UMA/SMP architectures all memory was treated equally, and had the same cost, but now the system has to think a bit about where it comes from, because accessing non-local memory has implications on performance and may cause cache coherency delays.
• Memory allocations made on one node will not be moved to another node, regardless of system needs. Once memory is allocated on a node, it will stay there.

The NUMA policy of any process can be changed, with broad-reaching effects, very simply using as a wrapper for the program. With a bit of additional work, it can be fine-tuned in detail by linking in and writing some code yourself to manage the policy. Some interesting things that can be done simply with the numactl wrapper are:

• Allocate memory with a particular policy:
  • locally on the “current” node — using --localalloc, and also the default mode
  • preferably on a particular node, but elsewhere if necessary — using --preferred=node
  • always on a particular node or set of nodes — using --membind=nodes
  • interleaved, that is, spread evenly round-robin across all or a set of nodes — using --interleaved=all or --interleaved=nodes
• Run the program on a particular node or set of nodes, in this case that means physical CPUs (--cpunodebind=nodes) or on a particular core or set of cores (--physcpubind=cpus).

What NUMA means for MySQL and InnoDB

InnoDB, and really, nearly all database servers (), present an atypical workload (from the point of view of the majority of installations) to Linux: a single large multi-threaded process which consumes nearly all of the system’s memory and should be expected to consume as much of the rest of the system resources as possible.

In a NUMA-based system, where the memory is divided into multiple nodes, how the system should handle this is not necessarily straightforward. The default behavior of the system is to allocate memory in the same node as a thread is scheduled to run on, and this works well for small amounts of memory, but when you want to allocate more than half of the system memory it’s no longer physically possible to even do it in a single NUMA node: In a two-node system, only 50% of the memory is in each node. Additionally, since many different queries will be running at the same time, on both processors, neither individual processor necessarily has preferential access to any particular part of memory needed by a particular query.

It turns out that this seems to matter in one very important way. Using we can see all of the allocations made by mysqld, and some interesting information about them. If you look for a really big number in the anon=size, you can pretty easily find the buffer pool (which will consume more than 51GB of memory for the 48GB that it has been configured to use) [line-wrapped for clarity]:

2aaaaad3e000 default anon=13240527 dirty=13223315 
  swapcache=3440324 active=13202235 N0=7865429 N1=5375098

The fields being shown here are:

• 2aaaaad3e000 — The virtual address of the memory region. Ignore this other than the fact that it’s a unique ID for this piece of memory.
• default — The NUMA policy in use for this region.
• anon=number — The number of anonymous pages mapped.
• dirty=number — The number of pages that are dirty because they have been modified. Generally memory allocated only within a single process is always going to be used, and thus dirty, but if a process forks it may have many copy-on-write pages mapped that are not dirty.
• swapcache=number — The number of pages swapped out but unmodified since they were swapped out, and thus they are ready to be freed if needed, but are still in memory at the moment.
• active=number — The number of pages on the “active list”; if this field is shown, some memory is inactive (anon minus active) which means it may be paged out by the swapper soon.
• N0=number and N1=number — The number of pages allocated on Node 0 and Node 1, respectively.

The entire numa_maps can be quickly summarized by the a simple script numa-maps-summary.pl, which I’ve written while analyzing this problem:

N0        :      7983584 ( 30.45 GB)
N1        :      5440464 ( 20.75 GB)
active    :     13406601 ( 51.14 GB)
anon      :     13422697 ( 51.20 GB)
dirty     :     13407242 ( 51.14 GB)
mapmax    :          977 (  0.00 GB)
mapped    :         1377 (  0.01 GB)
swapcache :      3619780 ( 13.81 GB)

An couple of interesting and somewhat unexpected things pop out to me:

1. The sheer imbalance in how much memory is allocated in Node 0 versus Node 1. This is actually absolutely normal per the default policy. Using the default NUMA policy, memory was preferentially allocated in Node 0, but Node 1 was used as a last resort.
2. The sheer amount of memory allocated in Node 0. This is absolutely critical — Node 0 is out of free memory! It only contains about 32GB of memory in total, and it has allocated a single large chunk of more than 30GB to InnoDB’s buffer pool. A few other smaller allocations to other processes finish it off, and suddenly it has no memory free, and isn’t even caching anything.

The memory allocated by MySQL looks something like this:

Allocating memory severely imbalanced, preferring Node 0

Due to Node 0 being completely exhausted of free memory, even though the system has plenty of free memory overall (over 10GB has been used for caches) it is entirely on Node 1. If any process scheduled on Node 0 needs local memory for anything, it will cause some of the already-allocated memory to be swapped out in order to free up some Node 0 pages. Even though there is free memory on Node 1, the Linux kernel in many circumstances (which admittedly I don’t totally understand³) prefers to page out Node 0 memory rather than free some of the cache on Node 1 and use that memory. Of course the paging is far more expensive than non-local memory access ever would be.

A small change, to big effect

An easy solution to this is to interleave the allocated memory. It is possible to do this using numactlas described above:

# numactl --interleave all command

We can use this with MySQL by making a , adding the following line (after cmd="$NOHUP_NICENESS"), which prefixes the command to start mysqld with a call tonumactl:

cmd="/usr/bin/numactl --interleave all $cmd"

Now, when MySQL needs memory it will allocate it interleaved across all nodes, effectively balancing the amount of memory allocated in each node. This will leave some free memory in each node, allowing the Linux kernel to cache data on both nodes, thus allowing memory to be easily freed on either node just by freeing caches (as it’s supposed to work) rather than paging.

Performance regression testing has been done comparing the two scenarios (default local plus spillover allocation versus interleaved allocation) using the DBT2 benchmark, and found that performance in the nominal case is identical. This is expected. The breakthrough comes in that: In all cases where swap use could be triggered in a repeatable fashion, the system no longer swaps!

You can now see from the numa_maps that all allocated memory has been spread evenly across Node 0 and Node 1:

2aaaaad3e000 interleave=0-1 anon=13359067 dirty=13359067 
  N0=6679535 N1=6679532

And the summary looks like this:

N0        :      6814756 ( 26.00 GB)
N1        :      6816444 ( 26.00 GB)
anon      :     13629853 ( 51.99 GB)
dirty     :     13629853 ( 51.99 GB)
mapmax    :          296 (  0.00 GB)
mapped    :         1384 (  0.01 GB)

In graphical terms, the allocation of all memory within mysqld has been made in a balanced way:

Allocating memory balanced (interleaved) across nodes

An aside on zone_reclaim_mode

The zone_reclaim_mode tunable in /proc/sys/vm can be used to fine-tune memory reclamation policies in a NUMA system. Subject to from the linux-mm mailing list, it doesn’t seem to help in this case.