linux-kernel - Re: [PATCH RFC net-next] net/smc: transition to RDMA core CQ pooling

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Message-ID: <daefb72f-398e-489f-bdbc-db997ef9c5ae@linux.ibm.com>
Date: Fri, 6 Feb 2026 16:58:23 +0530
From: Mahanta Jambigi <mjambigi@...ux.ibm.com>
To: "D. Wythe" <alibuda@...ux.alibaba.com>,
        "David S. Miller" <davem@...emloft.net>,
        Dust Li
 <dust.li@...ux.alibaba.com>,
        Eric Dumazet <edumazet@...gle.com>, Jakub Kicinski <kuba@...nel.org>,
        Paolo Abeni <pabeni@...hat.com>,
        Sidraya Jayagond <sidraya@...ux.ibm.com>,
        Wenjia Zhang <wenjia@...ux.ibm.com>
Cc: Simon Horman <horms@...nel.org>, Tony Lu <tonylu@...ux.alibaba.com>,
        Wen Gu <guwen@...ux.alibaba.com>, linux-kernel@...r.kernel.org,
        linux-rdma@...r.kernel.org, linux-s390@...r.kernel.org,
        netdev@...r.kernel.org, oliver.yang@...ux.alibaba.com,
        pasic@...ux.ibm.com
Subject: Re: [PATCH RFC net-next] net/smc: transition to RDMA core CQ pooling



On 02/02/26 3:18 pm, D. Wythe wrote:
> The current SMC-R implementation relies on global per-device CQs
> and manual polling within tasklets, which introduces severe
> scalability bottlenecks due to global lock contention and tasklet
> scheduling overhead, resulting in poor performance as concurrency
> increases.
> 
> Refactor the completion handling to utilize the ib_cqe API and
> standard RDMA core CQ pooling. This transition provides several key
> advantages:
> 
> 1. Multi-CQ: Shift from a single shared per-device CQ to multiple
> link-specific CQs via the CQ pool. This allows completion processing
> to be parallelized across multiple CPU cores, effectively eliminating
> the global CQ bottleneck.
> 
> 2. Leverage DIM: Utilizing the standard CQ pool with IB_POLL_SOFTIRQ
> enables Dynamic Interrupt Moderation from the RDMA core, optimizing
> interrupt frequency and reducing CPU load under high pressure.
> 
> 3. O(1) Context Retrieval: Replaces the expensive wr_id based lookup
> logic (e.g., smc_wr_tx_find_pending_index) with direct context retrieval
> using container_of() on the embedded ib_cqe.
> 
> 4. Code Simplification: This refactoring results in a reduction of
> ~150 lines of code. It removes redundant sequence tracking, complex lookup
> helpers, and manual CQ management, significantly improving maintainability.
> 
> Performance Test: redis-benchmark with max 32 connections per QP
> Data format: Requests Per Second (RPS), Percentage in brackets
> represents the gain/loss compared to TCP.
> 
> | Clients | TCP      | SMC (original)      | SMC (cq_pool)       |
> |---------|----------|---------------------|---------------------|
> | c = 1   | 24449    | 31172  (+27%)       | 34039  (+39%)       |
> | c = 2   | 46420    | 53216  (+14%)       | 64391  (+38%)       |
> | c = 16  | 159673   | 83668  (-48%)  <--  | 216947 (+36%)       |
> | c = 32  | 164956   | 97631  (-41%)  <--  | 249376 (+51%)       |
> | c = 64  | 166322   | 118192 (-29%)  <--  | 249488 (+50%)       |
> | c = 128 | 167700   | 121497 (-27%)  <--  | 249480 (+48%)       |
> | c = 256 | 175021   | 146109 (-16%)  <--  | 240384 (+37%)       |
> | c = 512 | 168987   | 101479 (-40%)  <--  | 226634 (+34%)       |
> 
> The results demonstrate that this optimization effectively resolves the
> scalability bottleneck, with RPS increasing by over 110% at c=64
> compared to the original implementation.

I applied your patch to the latest kernel(6.19-rc8) & saw below
Performance results:

1) In my evaluation, I ran several *uperf* based workloads using a
request/response (RR) pattern, and I observed performance *degradation*
ranging from *4%* to *59%*, depending on the specific read/write sizes
used. For example, with a TCP RR workload using 50 parallel clients
(nprocs=50) sending a 200‑byte request and reading a 1000‑byte response
over a 60‑second run, I measured approximately 59% degradation compared
to SMC‑R original performance.

2) In contrast, with uperf *streaming‑type* workloads, your patch shows
clear gains. I observed performance *improvement* ranging from *11%* to
*75%*, again depending on the specific streaming parameters. One
representative case is a TCP streaming/bulk‑receive workload with 250
parallel clients (nprocs=250) performing 640 reads per burst with 30 KB
per read, running continuously for 60 seconds, where I measured
approximately *75%* *improvement* over the SMC‑R original performance.

Note: I ran above tests with default WR(work request buffers), default
receive & transmit buffer size with smc_run.

I am looking for additional details regarding the redis-benchmark
performance results you previously shared. I would like to understand
whether the workload behaved more like a traditional request/response
(RR) pattern or a streaming-type workload, and what SMC‑R configuration
was used during the tests?

1) SMC Work Request (WR) Settings - Did your test environment use the
default SMC‑R work request buffers?
  net.smc.smcr_max_recv_wr = 48
  net.smc.smcr_max_send_wr = 16
2) SMC-R Buffer sizes used via smc_run - Did you use default transmit &
receive buffer sizes(smc_run -r <recv_size> -t <send_size>)?
3) Additional system or network tuning e.g CPU affinity, NIC offload
settings etc?