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* [PATCH v5 0/7] hugetlb: parallelize hugetlb page init on boot
@ 2024-01-26 15:24 Gang Li
  2024-01-26 15:24 ` [PATCH v5 1/7] hugetlb: code clean for hugetlb_hstate_alloc_pages Gang Li
                   ` (6 more replies)
  0 siblings, 7 replies; 16+ messages in thread
From: Gang Li @ 2024-01-26 15:24 UTC (permalink / raw)
  To: David Hildenbrand, David Rientjes, Mike Kravetz, Muchun Song,
	Andrew Morton, Tim Chen
  Cc: linux-mm, linux-kernel, ligang.bdlg, Gang Li

Hi all, hugetlb init parallelization has now been updated to v5.

This version is tested on next-20240125.

Update Summary:
- Use prep_and_add_allocated_folios in 2M hugetlb parallelization
- Update huge_boot_pages in arch/powerpc/mm/hugetlbpage.c
- Revise struct padata_mt_job comment
- Add 'max_threads' section in cover letter
- Collect more Reviewed-by

# Introduction
Hugetlb initialization during boot takes up a considerable amount of time.
For instance, on a 2TB system, initializing 1,800 1GB huge pages takes 1-2
seconds out of 10 seconds. Initializing 11,776 1GB pages on a 12TB Intel
host takes more than 1 minute[1]. This is a noteworthy figure.

Inspired by [2] and [3], hugetlb initialization can also be accelerated
through parallelization. Kernel already has infrastructure like
padata_do_multithreaded, this patch uses it to achieve effective results
by minimal modifications.

[1] https://lore.kernel.org/all/783f8bac-55b8-5b95-eb6a-11a583675000@google.com/
[2] https://lore.kernel.org/all/20200527173608.2885243-1-daniel.m.jordan@oracle.com/
[3] https://lore.kernel.org/all/20230906112605.2286994-1-usama.arif@bytedance.com/
[4] https://lore.kernel.org/all/76becfc1-e609-e3e8-2966-4053143170b6@google.com/

# max_threads
This patch use `padata_do_multithreaded` like this:

```
job.max_threads	= num_node_state(N_MEMORY) * multiplier;
padata_do_multithreaded(&job);
```

To fully utilize the CPU, the number of parallel threads needs to be
carefully considered. `max_threads = num_node_state(N_MEMORY)` does
not fully utilize the CPU, so we need to multiply it by a multiplier.

Tests below indicate that a multiplier of 2 significantly improves
performance, and although larger values also provide improvements,
the gains are marginal.

  multiplier     1       2       3       4       5
 ------------ ------- ------- ------- ------- -------
  256G 2node   358ms   215ms   157ms   134ms   126ms
  2T   4node   979ms   679ms   543ms   489ms   481ms
  50G  2node   71ms    44ms    37ms    30ms    31ms

Therefore, choosing 2 as the multiplier strikes a good balance between
enhancing parallel processing capabilities and maintaining efficient
resource management.

# Test result
      test case       no patch(ms)   patched(ms)   saved
 ------------------- -------------- ------------- --------
  256c2T(4 node) 1G           4745          2024   57.34%
  128c1T(2 node) 1G           3358          1712   49.02%
     12T         1G          77000         18300   76.23%

  256c2T(4 node) 2M           3336          1051   68.52%
  128c1T(2 node) 2M           1943           716   63.15%

# Change log
Changes in v5:
- Use prep_and_add_allocated_folios in 2M hugetlb parallelization
- Update huge_boot_pages in arch/powerpc/mm/hugetlbpage.c
- Revise struct padata_mt_job comment
- Add 'max_threads' section in cover letter
- Collect more Reviewed-by

Changes in v4:
- https://lore.kernel.org/r/20240118123911.88833-1-gang.li@linux.dev
- Make padata_do_multithreaded dispatch all jobs with a global iterator
- Revise commit message
- Rename some functions
- Collect Tested-by and Reviewed-by

Changes in v3:
- https://lore.kernel.org/all/20240102131249.76622-1-gang.li@linux.dev/
- Select CONFIG_PADATA as we use padata_do_multithreaded
- Fix a race condition in h->next_nid_to_alloc
- Fix local variable initialization issues
- Remove RFC tag

Changes in v2:
- https://lore.kernel.org/all/20231208025240.4744-1-gang.li@linux.dev/
- Reduce complexity with `padata_do_multithreaded`
- Support 1G hugetlb

v1:
- https://lore.kernel.org/all/20231123133036.68540-1-gang.li@linux.dev/
- parallelize 2M hugetlb initialization with workqueue

Gang Li (7):
  hugetlb: code clean for hugetlb_hstate_alloc_pages
  hugetlb: split hugetlb_hstate_alloc_pages
  padata: dispatch works on different nodes
  hugetlb: pass *next_nid_to_alloc directly to
    for_each_node_mask_to_alloc
  hugetlb: have CONFIG_HUGETLBFS select CONFIG_PADATA
  hugetlb: parallelize 2M hugetlb allocation and initialization
  hugetlb: parallelize 1G hugetlb initialization

 arch/powerpc/mm/hugetlbpage.c |   2 +-
 fs/Kconfig                    |   1 +
 include/linux/hugetlb.h       |   2 +-
 include/linux/padata.h        |   2 +
 kernel/padata.c               |  14 +-
 mm/hugetlb.c                  | 234 +++++++++++++++++++++++-----------
 mm/mm_init.c                  |   1 +
 7 files changed, 175 insertions(+), 81 deletions(-)

-- 
2.20.1


^ permalink raw reply	[flat|nested] 16+ messages in thread

end of thread, other threads:[~2024-02-09 17:17 UTC | newest]

Thread overview: 16+ messages (download: mbox.gz / follow: Atom feed)
-- links below jump to the message on this page --
2024-01-26 15:24 [PATCH v5 0/7] hugetlb: parallelize hugetlb page init on boot Gang Li
2024-01-26 15:24 ` [PATCH v5 1/7] hugetlb: code clean for hugetlb_hstate_alloc_pages Gang Li
2024-01-26 15:24 ` [PATCH v5 2/7] hugetlb: split hugetlb_hstate_alloc_pages Gang Li
2024-01-26 15:24 ` [PATCH v5 3/7] padata: dispatch works on different nodes Gang Li
2024-01-26 22:23   ` Tim Chen
2024-01-26 15:24 ` [PATCH v5 4/7] hugetlb: pass *next_nid_to_alloc directly to for_each_node_mask_to_alloc Gang Li
2024-01-26 15:24 ` [PATCH v5 5/7] hugetlb: have CONFIG_HUGETLBFS select CONFIG_PADATA Gang Li
2024-01-26 15:24 ` [PATCH v5 6/7] hugetlb: parallelize 2M hugetlb allocation and initialization Gang Li
2024-01-29  3:44   ` Muchun Song
2024-01-26 15:24 ` [PATCH v5 7/7] hugetlb: parallelize 1G hugetlb initialization Gang Li
2024-01-29  3:56   ` Muchun Song
2024-02-05  7:28   ` Muchun Song
2024-02-05  8:26     ` Gang Li
2024-02-05  9:09       ` Muchun Song
2024-02-07  1:53         ` Jane Chu
2024-02-09 17:17           ` Daniel Jordan

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