From patchwork Fri Feb 3 03:01:34 2023 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Jingbo Xu X-Patchwork-Id: 13126934 Return-Path: X-Spam-Checker-Version: SpamAssassin 3.4.0 (2014-02-07) on aws-us-west-2-korg-lkml-1.web.codeaurora.org Received: from vger.kernel.org (vger.kernel.org [23.128.96.18]) by smtp.lore.kernel.org (Postfix) with ESMTP id 784E5C636D3 for ; Fri, 3 Feb 2023 03:01:54 +0000 (UTC) Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S232256AbjBCDBw (ORCPT ); Thu, 2 Feb 2023 22:01:52 -0500 Received: from lindbergh.monkeyblade.net ([23.128.96.19]:38220 "EHLO lindbergh.monkeyblade.net" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S232165AbjBCDBt (ORCPT ); Thu, 2 Feb 2023 22:01:49 -0500 Received: from out30-101.freemail.mail.aliyun.com (out30-101.freemail.mail.aliyun.com [115.124.30.101]) by lindbergh.monkeyblade.net (Postfix) with ESMTPS id 82A3D8694; Thu, 2 Feb 2023 19:01:46 -0800 (PST) X-Alimail-AntiSpam: AC=PASS;BC=-1|-1;BR=01201311R541e4;CH=green;DM=||false|;DS=||;FP=0|-1|-1|-1|0|-1|-1|-1;HT=ay29a033018045170;MF=jefflexu@linux.alibaba.com;NM=1;PH=DS;RN=6;SR=0;TI=SMTPD_---0VampZ0J_1675393303; Received: from localhost(mailfrom:jefflexu@linux.alibaba.com fp:SMTPD_---0VampZ0J_1675393303) by smtp.aliyun-inc.com; Fri, 03 Feb 2023 11:01:44 +0800 From: Jingbo Xu To: xiang@kernel.org, chao@kernel.org, linux-erofs@lists.ozlabs.org Cc: huyue2@coolpad.com, linux-kernel@vger.kernel.org, linux-fsdevel@vger.kernel.org Subject: [PATCH v3 0/9] erofs: support page cache sharing between EROFS images in fscache mode Date: Fri, 3 Feb 2023 11:01:34 +0800 Message-Id: <20230203030143.73105-1-jefflexu@linux.alibaba.com> X-Mailer: git-send-email 2.19.1.6.gb485710b MIME-Version: 1.0 Precedence: bulk List-ID: X-Mailing-List: linux-fsdevel@vger.kernel.org v3: - the page cache sharing now supports readahead: - add patch 1: support readahead in meta routine - patch 4: initialize f_ra of anonymous file in prep for readahead - patch 5: since now readahead is performed upon the data blob, the accuate inode size of data blob needs to be derived - patch 6: filemap_read() is reused when implementing .read_iter() - cover letter: ~20% memory usage reduction can be seen when distributing tensorflow:2.10.0 and tensorflow:2.10.1 at the same node (see "Effect" section of the cover letter) RFC: https://lore.kernel.org/all/20230106125330.55529-1-jefflexu@linux.alibaba.com/ v2: https://lore.kernel.org/all/20230111083158.23462-1-jefflexu@linux.alibaba.com/ changes since RFC: - patch 2: allocate an anonymous file (realfile) when file is opened, rather than allocate a single anonymous file for each blob at mount time - patch 7: add 'sharecache' mount option to control if page cache sharing shall be enabled [Background] ============= Erofs already supports chunk deduplication across different images to minimize disk usage since v6.1. Furthermore, we can make inodes among different images share page cache for these deduplicated chunks to reduce the memory usage. This shall be much usable in container scenarios as deduplication is requisite for container images. [Implementation] ================ This is achieved by managing page cache of deduplicated chunks in blob's address space. In this way, all inodes sharing the deduplicated chunk will refer to and share the page cache in the blob's address space. [Restriction] ============== The page cache sharing feature also supports .mmap(). The reverse mapping requires that one vma can not be shared among inodes and can be linked to only one inode. As the vma will be finally linked to the blob's address space when page cache sharing enabled, the restriction of the reverse mapping actually requires that the mapped file area can not be mapped to multiple blobs. Thus page cache sharing can only be enabled for those files mapped to one blob. The chunk based data layout guarantees that a chunk will not cross the device (blob) boundary. Thus in chunk based data layout, those files smaller than the chunk size shall be guaranteed to be mapped to one blob. As chunk size is tunable at a per-file basis, this restriction can be relaxed at image building phase. As long as we ensure that the file can not be deduplicated, the file's chunk size can be set to a reasonable value larger than the file size, so that the file contains only one chunk, in which case page cache sharing feature can be enabled on this file later. [Effect] ======== The final optimization result of this feature depends on the following factors: 1. The number of deduplicated (shared) chunks. Images sharing most of the layers (e.g. a base image and v1 image based on the base image) will achieve better optimization. 2. As the restriction mentioned above, the number of files for which page cache sharing can ben enabled among the files accessed. I test the workload of starting up Tensorflow, which will access quite many (~5K) files among the startup phase. I get the base image of Tensorflow from [1] and build a new image (e.g. v1 image) on top of this base image. Since the image got from [1] is in OCI format, I have to convert it to erofs format with buildkit[2], with default chunk size of 1MB. I run containers from these two images with containerd (base image first, v2 image secondly). The (page cache) memory usage of the rootfs (container image) is shown as below: | page cache sharing | page cache sharing | disabled | enabled ------------------------|-----------------------|------------------- First container image | | page cache usage (MB) | 150 | 150 ------------------------+-----------------------|------------------- Second container image | | page cache usage (MB) | 150 | 7 It can be seen that most (~95%, 143MB/150MB) memory usage reduced under this workload (when starting following containers sharing container image layers). The remained 7MB memory usage is consumed by directories, since page cache sharing is enabled only for regular files in this RFC implementation. I also tested the memory usage reduction among minor versions of the same container image. I tested with v2.10.0 and v2.10.1 of tensorflow. It shows ~20% memory usage reduction, as most files accessed in the workload is .pyc files which are updated during the version bump. | page cache sharing | enabled ------------------------|------------------- First container image | (tensorflow:2.10.0) | page cache usage (MB) | 150 ------------------------+------------------- Second container image | (tensorflow:2.10.1) | page cache usage (MB) | 122 [1] docker.io/tensorflow/tensorflow:2.10.0 [2] https://github.com/moby/buildkit Jingbo Xu (9): erofs: support readahead in meta routine erofs: remove unused device mapping in the meta routine erofs: unify anonymous inodes for blob erofs: allocate anonymous file of blob for page cache sharing erofs: set accurate anony inode size for page cache sharing erofs: implement .read_iter for page cache sharing erofs: implement .mmap for page cache sharing erofs: add helper checking if page cache sharing shall be enabled erofs: introduce 'sharecache' mount option Documentation/filesystems/erofs.rst | 2 + fs/erofs/fscache.c | 271 +++++++++++++++++++++------- fs/erofs/inode.c | 4 + fs/erofs/internal.h | 34 +++- fs/erofs/super.c | 15 ++ 5 files changed, 256 insertions(+), 70 deletions(-)