ggml-cpu: replace cyclic chunk distribution with atomic work-stealing

[dnislno]

Overview

Replace the per-thread cyclic chunk assignment (current_chunk = ith) with a shared atomic work-stealing counter (atomic_fetch_add) in both ggml_compute_forward_mul_mat and ggml_compute_forward_mul_mat_id in ggml/src/ggml-cpu/ggml-cpu.c.

In the cyclic scheme, thread i starts at chunk i and advances by nth: thread 0 gets chunks 0,8,16...; thread 1 gets 1,9,17... On hybrid CPUs (e.g. Intel Alder Lake P-cores + E-cores), a slow E-core assigned to dense chunks makes other threads wait at ggml_barrier while idle — the fast P-cores have finished their cyclic share but cannot steal remaining work because each chunk is pre-assigned.

With work-stealing, all threads atomically claim the next available chunk from 0. Fast cores automatically take more chunks; slow cores take fewer. The barrier wait is minimized.

The old code also had a redundant if (nth >= nchunks) break guard inside the while body. This was necessary for the cyclic scheme (a thread that started beyond the last chunk had no work), but with work-stealing the while-condition itself handles this correctly — removed.

Related issues/PRs

• Performance e-core bug(?) - only 50% CPU utilization when using all threads - (Win11, Intel 13900k) #842 — original threadpool performance issue
• ggml : spread compute across threads in chunks #1507 — thread synchronization overhead
• ggml : use dynamic thread scheduling for matrix multiplication #6915 — CPU affinity and thread scheduling
• ggml: Implement yield barrier using futex for improved thread scheduling efficiency #13079 — hybrid CPU architecture performance
• Disable NUMA-specific chunking for high-core-count HPC systems #16882 — ggml_barrier spin-wait contention
• --threads -1 double counts via std::thread::hardware_concurrency() due to hyper-threading #19110 — recent threadpool refactoring

No standalone issue was filed for this specific bug.

Source locations

File: ggml/src/ggml-cpu/ggml-cpu.c

Site 1 - ggml_compute_forward_mul_mat reset (line 1351):

Before:

// Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
atomic_store_explicit(&params->threadpool->current_chunk, nth, memory_order_relaxed);

After:

atomic_store_explicit(&params->threadpool->current_chunk, 0, memory_order_relaxed);

Site 2 - ggml_compute_forward_mul_mat loop (line 1410):

Before:

// The first chunk comes from our thread_id, the rest will get auto-assigned.
int current_chunk = ith;

while (current_chunk < nchunk0 * nchunk1) {
    ...
    if (nth >= nchunk0 * nchunk1) {
        break;
    }
    current_chunk = atomic_fetch_add_explicit(&params->threadpool->current_chunk, 1, memory_order_relaxed);
}

After:

int current_chunk;

while ((current_chunk = atomic_fetch_add_explicit(&params->threadpool->current_chunk, 1, memory_order_relaxed)) < nchunk0 * nchunk1) {
    ...
}

Site 3 - ggml_compute_forward_mul_mat_id reset (line 1625):

Before:

*current_chunk_ctr = nth;

After:

*current_chunk_ctr = 0;

Site 4 - ggml_compute_forward_mul_mat_id loop (line 1663):

Before:

int current_chunk = ith;

while (current_chunk < nchunk0 * nchunk1) {
    ...
    if (nth >= nchunk0 * nchunk1) {
        break;
    }
    current_chunk = atomic_fetch_add_explicit(current_chunk_ctr, 1, memory_order_relaxed);
}

After:

int current_chunk;

while ((current_chunk = atomic_fetch_add_explicit(current_chunk_ctr, 1, memory_order_relaxed)) < nchunk0 * nchunk1) {
    ...
}

Local testing

Tested locally on Windows 11 with MSYS2 UCRT64 (GCC 16.1.0, cmake 4.3.4, ninja 1.13.2). CPU: Intel i3-1215U (2P+HT + 4E = 8 logical threads). Model: Qwen3.5-2B-Q4_K_M (1.23 GiB).

Before fix — llama-bench with -p 512 -n 128 -r 3:

| threads | pp512 (t/s) | tg128 (t/s) |
|---------|------------|------------|
| 4       | 68.15      | 13.01      |
| 6       | 70.69      | 11.56      |
| 8       | 69.47      |  9.07      |

Combined throughput at t=8: 29.79 t/s — throughput regresses as threads increase.

After fix — same benchmark:

| threads | pp512 (t/s) | tg128 (t/s) |
|---------|------------|------------|
| 4       | 61.44      | 12.27      |
| 6       | 70.67      | 12.55      |
| 8       | 75.24      | 10.32      |

Combined throughput at t=8: 33.33 t/s (+11.9%).

The fix restores scaling: prompt processing (pp512) now improves with more threads (61 -> 71 -> 75 t/s). Text generation (tg128) remains bandwidth-bound as expected, but the penalty at t=8 is reduced (9.07 -> 10.32 t/s, +13.8%).

Changes summary

ggml/src/ggml-cpu/ggml-cpu.c | 27 ++++++---------------------
 1 file changed, 6 insertions(+), 21 deletions(-)

• Unify both mul_mat and mul_mat_id to use atomic_fetch_add in the while-condition instead of per-thread cyclic start
• Reset chunk counter to 0 (not nth) so all threads compete from the first chunk
• Remove redundant if (nth >= nchunks) break guard
• Remove stale comments about the cyclic scheme

Tests added

No new tests. The existing benchmark suite (llama-bench, llama-perplexity) validates correctness: neural network outputs are deterministic under the same seed regardless of chunk ordering (chunks are independent and accumulated via atomics).

Requirements

• I have read and agree with the contributing guidelines
• AI usage disclosure:
  • Used opencode as assistive tool for codebase navigation (locating the cyclic distribution sites and current_chunk variable), running A/B benchmarks to measure the fix impact, and drafting the PR description. The fix itself is human-authored and understood: replace cyclic indexing with atomic work-stealing, a standard parallel computing pattern.

[ggml-gh-bot]

Hi @dnislno, thanks for your contribution!

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• AI-generated content: This project does not accept PRs, descriptions or commit messages that are fully or predominantly AI-generated. If you have used AI to assist you in writing code, please make sure to disclose that explicitly.

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