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

["D. Wythe" <alibuda@linux.alibaba.com >]

On Fri, Feb 27, 2026 at 10:11:38AM +0530, Mahanta Jambigi wrote:
> 
> 
> On 24/02/26 7:49 am, D. Wythe wrote:
> > On Fri, Feb 13, 2026 at 04:53:28PM +0530, Mahanta Jambigi wrote:
> >>
> >>
> >> On 09/02/26 1:23 pm, D. Wythe wrote:
> >>> On Fri, Feb 06, 2026 at 04:58:23PM +0530, Mahanta Jambigi wrote:
> >>>>
> >>>>
> >>>> 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.
> >>>>
> >>>
> >>> The only setting I changed was net.smc.smcr_max_conns_per_lgr = 32, all
> >>> other parameters were left at their default values. redis-benchmark is a
> >>> classic Request/Response (RR) workload, which contradicts your test
> >>> results. Since I'm unable to reproduce your results, it would be
> >>> very helpful if you could share the specific test configuration for my
> >>> analysis.
> >>
> >> I used a simple client–server setup connected via 25 Gb/s RoCE_Express2
> >> adapters on the same LAN(connection established via SMC-R v1). After
> >> running the commands shown below, I observed a performance degradation
> >> of up to 59%.
> >>
> >> Server: smc_run uperf -s
> >> Client: smc_run uperf -m rr1c-200x1000-50.xml
> >>
> >> cat rr1c-200x1000-50.xml
> >>
> >> <?xml version="1.0"?>
> >> <profile name="TCP_RR">
> >> 	<group nprocs="50">
> >> 		<transaction iterations="1">
> >> 			<flowop type="connect" options="remotehost=server_ip protocol=tcp
> >> tcp_nodelay" />
> >> 		</transaction>
> >> 		<transaction duration="60">
> >> 			<flowop type="write" options="size=200"/>
> >> 			<flowop type="read" options="size=1000"/>
> >> 		</transaction>
> >> 		<transaction iterations="1">
> >> 			<flowop type="disconnect" />
> >> 		</transaction>
> >> 	</group>
> >> </profile>
> > 
> > Using the exact same XML profile you provided, I tested this on a 25Gb
> > NIC. I observed no degradation. Instead, performance improved
> > significantly:
> > 
> > Original: ~1.08 Gb/s
> > Patched: ~5.1 Gb/s
> > 
> > I suspect the 59% drop might be due to connections falling back to TCP.
> > Could you check smcss -a during your test to see if the traffic is
> > actually running over SMC-R?
> 
> I have checked this. The connection was successful using *SMCR* Mode
> itself. Also I have confirmed this via 'smcr -d stats' command which
> shows 0 count for TCP fallback.
> 

Given that fallback is ruled out, and a 59% drop is quite massive,
especially since I'm seeing a significant improvement on my end.

Since I am unable to reproduce this locally, I would suggest analyzing
the CPU consumption and perf profiles in your environment. With a
regression this severe, the hotspots or differences should be fairly
obvious to identify.

> > 
> >>
> >> I installed redis-server on the server machine & redis-benchmark on the
> >> client machine & I was able to establish the SMC-R using below commands.
> >> If you could help me with the exact commands you used to measure the
> >> redis-benchmark performance, I can try the same on my setup.
> >>
> >> Server: smc_run redis-server --port <port_num> --save "" --appendonly no
> >>   --protected-mode no --bind 0.0.0.0
> >> Client: smc_run redis-benchmark -h <server_ip> -p <port_num> -n 10000 -c
> >> 50 -t ping_inline,ping_bulk -q
> > 
> > Here are the exact commands and scripts I used for the
> > redis-benchmark:
> > 
> > Server: smc_run redis-server --protected-mode no --save
> > 
> > Client: smc_run redis-benchmark -h <server_ip> -n 5000000 -t set --threads 3
> > -c <conn_num>
> > 
> > D. Wythe