Fix high CPU usage from busy-wait in TransferEngineOperationState::wait_for_completion

[Copilot]

TransferEngineOperationState::wait_for_completion() performs tight busy-wait polling without sleep, pegging CPU cores during long transfers under RDMA latency or bandwidth saturation.

Changes

• Replace busy-wait with adaptive exponential backoff
  • Use cv_.wait_for() with exponential backoff: 1ms → 100ms (1.5x multiplier)
  • Leverages existing cv_.notify_all() in set_result_internal() for early wake-up
  • Maintains 60s timeout behavior

// Before: tight loop spinning ~4M times per 100ms
while (true) {
    std::unique_lock<std::mutex> lock(mutex_);
    check_task_status();
    if (result_.has_value()) break;
    // No sleep - continuous polling
}

// After: exponential backoff with condition variable
auto current_backoff = std::chrono::milliseconds(1);
while (true) {
    {
        std::unique_lock<std::mutex> lock(mutex_);
        check_task_status();
        if (result_.has_value()) return;
        
        if (cv_.wait_for(lock, current_backoff, 
                         [this] { return result_.has_value(); })) {
            return;
        }
    }
    current_backoff = std::min(
        std::ceil(current_backoff.count() * 1.5), 100ms);
}

Impact

• 457,416× reduction in polling operations (4.1M → 9 polls per 100ms)
• Eliminates CPU contention for throughput-first workloads
• Fast initial response (1ms) with graceful backoff for long operations

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Original prompt

---

This section details on the original issue you should resolve

<issue_title>[Performance]: High CPU usage due to busy-wait in TransferEngineOperationState::wait_for_completion</issue_title> <issue_description>### Describe your performance question

Describe

TransferEngineOperationState::wait_for_completion() performs busy-waiting: it loops indefinitely and repeatedly calls check_task_status() without any wait/yield/backoff. Under high RDMA latency or bandwidth saturation, this can peg a CPU core and hurt overall throughput when CPU is constrained.

Impact

• Sustained high CPU usage by the waiting thread(s) during large/long transfers.
• Lower system throughput under CPU contention;

Real-world scenario: Offline inference, throughput-first

Network: Large data streaming easily saturates the RDMA NIC; under saturation or transient congestion, transfer completion latency increases, so the current tight polling keeps spinning for long periods.

CPU contention: Each waiting thread can peg a CPU core. This competes with CPU-heavy preprocessing stages (tokenization, chunking/sharding, mmap reads, decoding).

Resource efficiency: Spinning wastes CPU cycles that could be used for data preparation, further affecting subsequent GPU inference and leading to throughput degradation.

Before submitting a new issue...

• [x] Make sure you already searched for relevant issues and read the documentation</issue_description>

Comments on the Issue (you are @copilot in this section)

• Fixes kvcache-ai/Mooncake#1033

---

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[wwq2333]

    constexpr auto kInitialBackoffMs = std::chrono::milliseconds(1);
    constexpr auto kMaxBackoffMs = std::chrono::milliseconds(100);

Would using milliseconds be too high for RDMA transmission?

In my tests, with direct sleep, 1us and 10us, for a transmission with value_size=1MB, the difference in concurrency from 1 to 64 is not very noticeable, but CPU usage drops significantly; however, using 100us, latency increases significantly under low concurrency (such as 1) .

In my test environment, the preferred RDMA network card is 2*400Gbps (MC_MS_AUTO_DISC=1).

Or could initialBackoff and MaxBackoff be made readable through environment variables?

[stmatengss]

/gemini review it

[stmatengss]

    constexpr auto kInitialBackoffMs = std::chrono::milliseconds(1);
    constexpr auto kMaxBackoffMs = std::chrono::milliseconds(100);

Would using milliseconds be too high for RDMA transmission?

In my tests, with direct sleep, 1us and 10us, for a transmission with value_size=1MB, the difference in concurrency from 1 to 64 is not very noticeable, but CPU usage drops significantly; however, using 100us, latency increases significantly under low concurrency (such as 1) .

In my test environment, the preferred RDMA network card is 2*400Gbps (MC_MS_AUTO_DISC=1).

Or could initialBackoff and MaxBackoff be made readable through environment variables?

Sure. microseconds should be better. QQ: for 2*400Gbps environements, there are NV connectX NIC or other types?

[wwq2333]

Sure. microseconds should be better. QQ: for 2*400Gbps environements, there are NV connectX NIC or other types?

four NV connectX NICs, two as preferred and the other two as backups (with MC_MS_AUTO_DISC=1)

---

I simply compared the latency and CPU usage under different sleep intervals (without any backoff mechanism).

My test environment:

• Client pod: 32 cores, 128 GB RAM, 4 RDMA NICs; running only the mooncake-store client with multiple threads performing get operations on 1 MB values.
• Worker pod: 32 cores, 128 GB RAM, 4 RDMA NICs; deployed with both master and worker components (a client that only performing sleep operations).

concurrency:1

Sleep Interval	Average Used Cores	Throughput (GB/s)	Average Latency (µs)
0 ns	4.69	8.55	113.65
100 ns	4.32	7.08	137.49
1,000 ns	4.29	6.54	148.87
10,000 ns	4.27	6.48	150.21

concurrency:16

Sleep Interval	Average Used Cores	Throughput (GB/s)	Average Latency (µs)
0 ns	10.23	70.08	222.10
100 ns	6.35	61.23	253.34
1,000 ns	6.13	60.49	256.74
10,000 ns	6.05	58.00	268.00

concurrency:64

Sleep Interval	Average Used Cores	Throughput (GB/s)	Average Latency (µs)
0 ns	25.02	45.43	1374.30
100 ns	8.04	84.62	737.03
1,000 ns	7.97	86.54	720.73
10,000 ns	7.90	86.66	719.69

Note: Under 64‑concurrency testing, the results for 1,000 ns and 10,000 ns sleep intervals are practically the same.
Each run shows slight variance, with either one occasionally performing slightly better.

'Average Used Cores' is calculated from utime and stime in /proc/self/stat, (user + system CPU time difference) / wall clock time.
Peak CPU usage observed in top is slightly higher than these averages; for the 64‑concurrency case without sleep, the peak usage reached over 30 cores.

Overall, when the CPU and RDMA network bandwidth are not heavily loaded, busy‑polling indeed provides lower latency. So it seems better to keep busy‑polling as the default behavior, which keeps the user experience the same as before. Meanwhile, the configuration could be made adjustable through environment variables, allowing users to choose a short sleep approach in certain scenarios.

Of course, a more elegant solution would be event‑driven completion: when a batch or task finishes, the waiter is notified via a condition variable. That said, having the transfer engine call into the store directly feels a bit awkward and introduces unnecessary coupling between layers.

I noticed the V3 roadmap ([Draft] Mooncake Store V3 Roadmap). Perhaps this topic could also be considered as part of that plan?

[stmatengss]

Sure. microseconds should be better. QQ: for 2*400Gbps environements, there are NV connectX NIC or other types?

four NV connectX NICs, two as preferred and the other two as backups (with MC_MS_AUTO_DISC=1)

I simply compared the latency and CPU usage under different sleep intervals (without any backoff mechanism).

My test environment:

• Client pod: 32 cores, 128 GB RAM, 4 RDMA NICs; running only the mooncake-store client with multiple threads performing get operations on 1 MB values.
• Worker pod: 32 cores, 128 GB RAM, 4 RDMA NICs; deployed with both master and worker components (a client that only performing sleep operations).

concurrency:1

Sleep Interval Average Used Cores Throughput (GB/s) Average Latency (µs) 0 ns 4.69 8.55 113.65 100 ns 4.32 7.08 137.49 1,000 ns 4.29 6.54 148.87 10,000 ns 4.27 6.48 150.21 concurrency:16

Sleep Interval Average Used Cores Throughput (GB/s) Average Latency (µs) 0 ns 10.23 70.08 222.10 100 ns 6.35 61.23 253.34 1,000 ns 6.13 60.49 256.74 10,000 ns 6.05 58.00 268.00 concurrency:64

Sleep Interval Average Used Cores Throughput (GB/s) Average Latency (µs) 0 ns 25.02 45.43 1374.30 100 ns 8.04 84.62 737.03 1,000 ns 7.97 86.54 720.73 10,000 ns 7.90 86.66 719.69

Note: Under 64‑concurrency testing, the results for 1,000 ns and 10,000 ns sleep intervals are practically the same. Each run shows slight variance, with either one occasionally performing slightly better.

'Average Used Cores' is calculated from utime and stime in /proc/self/stat, (user + system CPU time difference) / wall clock time. Peak CPU usage observed in top is slightly higher than these averages; for the 64‑concurrency case without sleep, the peak usage reached over 30 cores.

Overall, when the CPU and RDMA network bandwidth are not heavily loaded, busy‑polling indeed provides lower latency. So it seems better to keep busy‑polling as the default behavior, which keeps the user experience the same as before. Meanwhile, the configuration could be made adjustable through environment variables, allowing users to choose a short sleep approach in certain scenarios.

Of course, a more elegant solution would be event‑driven completion: when a batch or task finishes, the waiter is notified via a condition variable. That said, having the transfer engine call into the store directly feels a bit awkward and introduces unnecessary coupling between layers.

I noticed the V3 roadmap ([Draft] Mooncake Store V3 Roadmap). Perhaps this topic could also be considered as part of that plan?

That's a good idea, I will add it to the Roadmap. If you have interest, how about taking this task?

[wwq2333]

That's a good idea, I will add it to the Roadmap. If you have interest, how about taking this task?

Sure, I’d be happy to give it a try.