diff options
| author | Nhat Pham <[email protected]> | 2023-11-30 11:40:23 -0800 |
|---|---|---|
| committer | Andrew Morton <[email protected]> | 2023-12-12 10:57:02 -0800 |
| commit | b5ba474f3f518701249598b35c581b92a3c95b48 (patch) | |
| tree | 74b6e95f9fdfe7a41cea4692ca8ff1b1948c6b8f /tools/perf/scripts/python/bin/stackcollapse-record | |
| parent | a697dc2be925d4814f26d7588347ccdd2c5525ed (diff) | |
zswap: shrink zswap pool based on memory pressure
Currently, we only shrink the zswap pool when the user-defined limit is
hit. This means that if we set the limit too high, cold data that are
unlikely to be used again will reside in the pool, wasting precious
memory. It is hard to predict how much zswap space will be needed ahead
of time, as this depends on the workload (specifically, on factors such as
memory access patterns and compressibility of the memory pages).
This patch implements a memcg- and NUMA-aware shrinker for zswap, that is
initiated when there is memory pressure. The shrinker does not have any
parameter that must be tuned by the user, and can be opted in or out on a
per-memcg basis.
Furthermore, to make it more robust for many workloads and prevent
overshrinking (i.e evicting warm pages that might be refaulted into
memory), we build in the following heuristics:
* Estimate the number of warm pages residing in zswap, and attempt to
protect this region of the zswap LRU.
* Scale the number of freeable objects by an estimate of the memory
saving factor. The better zswap compresses the data, the fewer pages
we will evict to swap (as we will otherwise incur IO for relatively
small memory saving).
* During reclaim, if the shrinker encounters a page that is also being
brought into memory, the shrinker will cautiously terminate its
shrinking action, as this is a sign that it is touching the warmer
region of the zswap LRU.
As a proof of concept, we ran the following synthetic benchmark: build the
linux kernel in a memory-limited cgroup, and allocate some cold data in
tmpfs to see if the shrinker could write them out and improved the overall
performance. Depending on the amount of cold data generated, we observe
from 14% to 35% reduction in kernel CPU time used in the kernel builds.
[[email protected]: check shrinker enablement early, use less costly stat flushing]
Link: https://lkml.kernel.org/r/[email protected]
Link: https://lkml.kernel.org/r/[email protected]
Signed-off-by: Nhat Pham <[email protected]>
Acked-by: Johannes Weiner <[email protected]>
Tested-by: Bagas Sanjaya <[email protected]>
Cc: Chris Li <[email protected]>
Cc: Dan Streetman <[email protected]>
Cc: Domenico Cerasuolo <[email protected]>
Cc: Michal Hocko <[email protected]>
Cc: Muchun Song <[email protected]>
Cc: Roman Gushchin <[email protected]>
Cc: Seth Jennings <[email protected]>
Cc: Shakeel Butt <[email protected]>
Cc: Shuah Khan <[email protected]>
Cc: Vitaly Wool <[email protected]>
Cc: Yosry Ahmed <[email protected]>
Cc: Chengming Zhou <[email protected]>
Signed-off-by: Andrew Morton <[email protected]>
Diffstat (limited to 'tools/perf/scripts/python/bin/stackcollapse-record')
0 files changed, 0 insertions, 0 deletions