Re: [PATCH v3 3/7] padata: dispatch works on different nodes

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On 2024/1/12 01:50, Tim Chen wrote:
On Tue, 2024-01-02 at 21:12 +0800, Gang Li wrote:
When a group of tasks that access different nodes are scheduled on the
same node, they may encounter bandwidth bottlenecks and access latency.

Thus, numa_aware flag is introduced here, allowing tasks to be
distributed across different nodes to fully utilize the advantage of
multi-node systems.

Signed-off-by: Gang Li <gang.li@xxxxxxxxx>
---
  include/linux/padata.h | 3 +++
  kernel/padata.c        | 8 ++++++--
  mm/mm_init.c           | 1 +
  3 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/include/linux/padata.h b/include/linux/padata.h
index 495b16b6b4d72..f79ccd50e7f40 100644
--- a/include/linux/padata.h
+++ b/include/linux/padata.h
@@ -137,6 +137,8 @@ struct padata_shell {
   *             appropriate for one worker thread to do at once.
   * @max_threads: Max threads to use for the job, actual number may be less
   *               depending on task size and minimum chunk size.
+ * @numa_aware: Dispatch jobs to different nodes. If a node only has memory but
+ *              no CPU, dispatch its jobs to a random CPU.
   */
  struct padata_mt_job {
  	void (*thread_fn)(unsigned long start, unsigned long end, void *arg);
@@ -146,6 +148,7 @@ struct padata_mt_job {
  	unsigned long		align;
  	unsigned long		min_chunk;
  	int			max_threads;
+	bool			numa_aware;
  };
/**
diff --git a/kernel/padata.c b/kernel/padata.c
index 179fb1518070c..1c2b3a337479e 100644
--- a/kernel/padata.c
+++ b/kernel/padata.c
@@ -485,7 +485,7 @@ void __init padata_do_multithreaded(struct padata_mt_job *job)
  	struct padata_work my_work, *pw;
  	struct padata_mt_job_state ps;
  	LIST_HEAD(works);
-	int nworks;
+	int nworks, nid = 0;

If we always start from 0, we may be biased towards the low numbered node,
and not use high numbered nodes at all.  Suggest you do
static nid = 0;


When we use `static`, if there are multiple parallel calls to
`padata_do_multithreaded`, it may result in an uneven distribution of
tasks for each padata_do_multithreaded.

We can make the following modifications to address this issue.

```
diff --git a/kernel/padata.c b/kernel/padata.c
index 1c2b3a337479e..925e48df6dd8d 100644
--- a/kernel/padata.c
+++ b/kernel/padata.c
@@ -485,7 +485,8 @@ void __init padata_do_multithreaded(struct padata_mt_job *job)
        struct padata_work my_work, *pw;
        struct padata_mt_job_state ps;
        LIST_HEAD(works);
-       int nworks, nid = 0;
+       int nworks, nid;
+       static volatile int global_nid = 0;

        if (job->size == 0)
                return;
@@ -516,12 +517,15 @@ void __init padata_do_multithreaded(struct padata_mt_job *job)
        ps.chunk_size = max(ps.chunk_size, job->min_chunk);
        ps.chunk_size = roundup(ps.chunk_size, job->align);

+       nid = global_nid;
        list_for_each_entry(pw, &works, pw_list)
-               if (job->numa_aware)
-                       queue_work_node((++nid % num_node_state(N_MEMORY)),
-                                       system_unbound_wq, &pw->pw_work);
-               else
+               if (job->numa_aware) {
+ queue_work_node(nid, system_unbound_wq, &pw->pw_work);
+                       nid = next_node(nid, node_states[N_CPU]);
+               } else
                        queue_work(system_unbound_wq, &pw->pw_work);
+       if (job->numa_aware)
+               global_nid = nid;

/* Use the current thread, which saves starting a workqueue worker. */ padata_work_init(&my_work, padata_mt_helper, &ps, PADATA_WORK_ONSTACK);
```


if (job->size == 0)
  		return;
@@ -517,7 +517,11 @@ void __init padata_do_multithreaded(struct padata_mt_job *job)
  	ps.chunk_size = roundup(ps.chunk_size, job->align);
list_for_each_entry(pw, &works, pw_list)
-		queue_work(system_unbound_wq, &pw->pw_work);
+		if (job->numa_aware)
+			queue_work_node((++nid % num_node_state(N_MEMORY)),
+					system_unbound_wq, &pw->pw_work);

I think we should use nid = next_node(nid, node_states[N_CPU]) instead of
++nid % num_node_state(N_MEMORY).  You are picking the next node with CPU
to handle the job.

Tim


I agree.




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