add GGML_USE_NUMA_MIGRATE feature to optimize cross NUMA op computation #13649

wenlujon · 2025-05-20T07:22:09Z

This PR adds GGML_USE_NUMA_MIGRATE feature to optimize cross NUMA op computation as the cross-NUMA memory access would be the bottleneck if spawning threads across NUMA nodes:

optimize the ggml_barrier() for cross NUMA case by adding ggml_barrier_numa_aware(), currently enabled for aarch64 forward_mul_mat()
add build option GGML_USE_NUMA_MIGRATE to enable the feature
add option --numa migrate if GGML_USE_NUMA_MIGRATE is enabled, which would migrate pages across NUMA nodes so that mul_mat op would only do the computation in local numa part of tensor data src0 and dst according to the ith, cores would be set affinity with the option.

Test Results

With the feature, tested on NeoVerse N2 with multiple NUMA nodes (with 64 cores per NUMA node), see performance improvements with running llama3 model.

Base data

running command:

$ numactl -m 0 -N 0 $LLAMA/build/bin/llama-batched-bench -m ./models/Meta-Llama-3-8B-Instruct.Q4_0.gguf -c 4096 -b 2048 -ub 512 -npp 128 -ntg 128 -npl 1,4,8,16 -t 64

results:

|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|   128 |    128 |    1 |    256 |    0.286 |   447.56 |    5.119 |    25.01 |    5.405 |    47.37 |
|   128 |    128 |    4 |   1024 |    1.391 |   368.03 |    6.025 |    84.98 |    7.416 |   138.08 |
|   128 |    128 |    8 |   2048 |    2.925 |   350.03 |    8.641 |   118.51 |   11.566 |   177.07 |
|   128 |    128 |   16 |   4096 |    6.853 |   298.83 |   11.240 |   182.21 |   18.093 |   226.38 |

Result with the feature

running command:

$ ./llama-batched-bench -m $MODEL_DIR/$MODEL -c 4096 -b 2048 -ub 512 -npp 128 -ntg 128 -npl 1,2,4,8,16 -t 128 --cache-type-k q8_0 --cache-type-v q8_0 -fa --numa migrate

results:

|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|   128 |    128 |    1 |    256 |    0.291 |   439.45 |    4.186 |    30.58 |    4.477 |    57.18 |
|   128 |    128 |    4 |   1024 |    1.458 |   351.08 |    5.909 |    86.65 |    7.367 |   139.00 |
|   128 |    128 |    8 |   2048 |    2.943 |   347.95 |    7.499 |   136.55 |   10.442 |   196.13 |
|   128 |    128 |   16 |   4096 |    7.738 |   264.68 |   10.933 |   187.33 |   18.670 |   219.39 |

For example, there's S_TG t/s 22% performance improvement with B=1, slightly S_PP t/s drop (1.8%), overall 20.7% S T/s improvement.

Tested perplexity, there's no regression with the feature:

$ numactl -m 0,1 -N 0,1 $LLAMA/build/bin/llama-perplexity -m ./models/Meta-Llama-3-8B-Instruct.Q4_0.gguf -f $LLAMA/wikitext-2-raw/wiki.test.raw -t 128 --numa migrate
Final estimate: PPL = 8.7538 +/- 0.06481

Enablement

The feature is disabled by default, this is the guidances to enable the feature:

enable DGGML_NUMA_MIGRATE during cmake

$ cmake -B build -DGGML_NUMA_MIGRATE=ON

add --numa migrate option when running, and set thread numbers according to the number of numa nodes (which is 2 nodes by default which is the best trade-off number during the NeoVerse N2 platform in test, could be changed by setting GGML_NUMA_MIGRATE_NODES build option, it's also better to bind llama to numa node 0 and 1 through numactl -m 0,1 -N 0,1).

Warshmao · 2025-05-20T07:58:45Z

enum ggml_status ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
    size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
    size = GGML_PAD(size, talloc->alignment);

    if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
        GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
                __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
        GGML_ABORT("not enough space in the buffer");
    }

    void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset;
    talloc->offset += size;

    assert(((uintptr_t)addr % talloc->alignment) == 0);

    return ggml_backend_tensor_alloc(talloc->buffer, tensor, addr);
}

load model error: "not enough space in the buffer"

wenlujon · 2025-05-21T02:29:58Z

enum ggml_status ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
    size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
    size = GGML_PAD(size, talloc->alignment);

    if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
        GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
                __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
        GGML_ABORT("not enough space in the buffer");
    }

    void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset;
    talloc->offset += size;

    assert(((uintptr_t)addr % talloc->alignment) == 0);

    return ggml_backend_tensor_alloc(talloc->buffer, tensor, addr);
}

load model error: "not enough space in the buffer"

The issue should be caused by the GGML_PAD with page size alignment hence the overall size exceeds the original allocated size in ggml_backend_cpu_buffer_type_alloc_buffer().
The issue relates with test environment as I don't see the issue with the models (llama3, DeepSeek, and Qwen3) and platforms I tested (NeoVerse and Rome), so could you please share the environment you're using: the model, test platform information? So that I could have a fix and then verify it.

Warshmao · 2025-05-21T02:48:49Z

enum ggml_status ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
    size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
    size = GGML_PAD(size, talloc->alignment);

    if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
        GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
                __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
        GGML_ABORT("not enough space in the buffer");
    }

    void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset;
    talloc->offset += size;

    assert(((uintptr_t)addr % talloc->alignment) == 0);

    return ggml_backend_tensor_alloc(talloc->buffer, tensor, addr);
}
load model error: "not enough space in the buffer"
The issue should be caused by the GGML_PAD with page size alignment hence the overall size exceeds the original allocated size in ggml_backend_cpu_buffer_type_alloc_buffer(). The issue relates with test environment as I don't see the issue with the models (llama3, DeepSeek, and Qwen3) and platforms I tested (NeoVerse and Rome), so could you please share the environment you're using: the model, test platform information? So that I could have a fix and then verify it.

model: Qwen3-30B-A3B-Q4_K_M
system: ubuntu 22.04
cpu: intel Xeon 8480C * 2
mem: 1T

wenlujon · 2025-05-21T03:02:36Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

Warshmao · 2025-05-21T03:19:00Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

not work.
Segmentation fault.

wenlujon · 2025-05-21T03:22:27Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

not work. Segmentation fault.

Good thing is it seems it passed the buffer allocation. what's the backtrace for the seg fault?

Warshmao · 2025-05-21T03:26:04Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

not work. Segmentation fault.

Good thing is it seems it passed the buffer allocation. what's the backtrace for the seg fault?

debug log info

wenlujon · 2025-05-21T03:28:53Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

not work. Segmentation fault.

Good thing is it seems it passed the buffer allocation. what's the backtrace for the seg fault?

debug log info

Do you have a core dump generated? if so, you might use gdb to open it to see the backtrace.

$ gdb [your program] [core file]
$ bt

it would be better if you could build a debug build, and then use gdb attach to it.

gdb [your program]
(gdb) run [with your arguments]

BTW, what's the running command of your test?

Warshmao · 2025-05-21T03:36:25Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

not work. Segmentation fault.

Good thing is it seems it passed the buffer allocation. what's the backtrace for the seg fault?

debug log info

Do you have a core dump generated? if so, you might use gdb to open it to see the backtrace.
$ gdb [your program] [core file]
$ bt
it would be better if you could build a debug build, and then use gdb attach to it.
gdb [your program]
(gdb) run [with your arguments]
BTW, what's the running command of your test?

llama-bench -m Qwen3-30B-A3B-Q4_K_M.gguf -t 50 -p 512 -n 128 --numa migrate --verbnose

wenlujon · 2025-05-21T05:41:31Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

not work. Segmentation fault.

Good thing is it seems it passed the buffer allocation. what's the backtrace for the seg fault?

debug log info

Do you have a core dump generated? if so, you might use gdb to open it to see the backtrace.
$ gdb [your program] [core file]
$ bt
it would be better if you could build a debug build, and then use gdb attach to it.
gdb [your program]
(gdb) run [with your arguments]
BTW, what's the running command of your test?
llama-bench -m Qwen3-30B-A3B-Q4_K_M.gguf -t 50 -p 512 -n 128 --numa migrate --verbnose

so the cgraph of running llama-bench is null, I've fixed it in the latest commit, could you please give it a try?

Warshmao · 2025-05-21T06:40:00Z

Qwen3-30B-A3B-Q4_K_M

OK, Seems an issue also related with platform, could you please retry the latest commit? I added the size with num of tensors * page_size, which should address the issue here. it should not have big impact on memory consumption, for example, for Qwen3-235B-A22B Q8_0 which has 1131 tensors, the increased memory would be 1131*4k=4.4M.

not work. Segmentation fault.

Good thing is it seems it passed the buffer allocation. what's the backtrace for the seg fault?

debug log info

Do you have a core dump generated? if so, you might use gdb to open it to see the backtrace.
$ gdb [your program] [core file]
$ bt
it would be better if you could build a debug build, and then use gdb attach to it.
gdb [your program]
(gdb) run [with your arguments]
BTW, what's the running command of your test?
llama-bench -m Qwen3-30B-A3B-Q4_K_M.gguf -t 50 -p 512 -n 128 --numa migrate --verbnose
so the cgraph of running llama-bench is null, I've fixed it in the latest commit, could you please give it a try?

It worked.

Warshmao · 2025-05-21T07:05:31Z

but the performance is not up to ~20%

numa_migrate

b5439

wenlujon · 2025-05-21T07:25:56Z

but the performance is not up to ~20%

numa_migrate

b5439

I notice you have 112 cores, could you run with -t 112, only in this case, the feature would leverage more cores to do OP computation.

Warshmao · 2025-05-21T07:50:17Z

but the performance is not up to ~20%
numa_migrate
b5439

I notice you have 112 cores, could you run with -t 112, only in this case, the feature would leverage more cores to do OP computation.

numa_migrate -t 112

wenlujon · 2025-05-21T07:55:49Z

but the performance is not up to ~20%
numa_migrate
b5439

I notice you have 112 cores, could you run with -t 112, only in this case, the feature would leverage more cores to do OP computation.

numa_migrate -t 112

OK, so we could see there's obvious performance uplift for pp512 test, though there's no improvement for tg128 (guess the cross numa traffic isn't the bottleneck in the case).
BTW, could you run base data with also 112 threads? so we could have a better comparison.

…rier as the barrier only happens in few cores

Warshmao · 2025-05-21T08:23:37Z

but the performance is not up to ~20%
numa_migrate
b5439

I notice you have 112 cores, could you run with -t 112, only in this case, the feature would leverage more cores to do OP computation.

numa_migrate -t 112

OK, so we could see there's obvious performance uplift for pp512 test, though there's no improvement for tg128 (guess the cross numa traffic isn't the bottleneck in the case). BTW, could you run base data with also 112 threads? so we could have a better comparison.

b5439 -t 112

wenlujon · 2025-05-21T08:31:49Z

but the performance is not up to ~20%
numa_migrate
b5439

I notice you have 112 cores, could you run with -t 112, only in this case, the feature would leverage more cores to do OP computation.

numa_migrate -t 112

OK, so we could see there's obvious performance uplift for pp512 test, though there's no improvement for tg128 (guess the cross numa traffic isn't the bottleneck in the case). BTW, could you run base data with also 112 threads? so we could have a better comparison.

b5439 -t 112

I saw there're 2 runs with the feature, the first is at 29.51 t/s and second run is 34.63 t/s, the base data is stable at 29.72 t/s, could you rerun the pp512 with the feature to see if it's stable at 34 t/s? if that's the case, then we could observe 17% uplift. anyway, x86 has better cross NUMA traffic latency than aarch64.

wenlujon · 2025-05-22T05:59:01Z

Hi @Warshmao , will you do more testing with pp512? Or do you have an aarch64 system to verify it?
BTW, I tested the llama3 with IceLake systems, I do see improvements with pp512 tests (8% ~ 16%) and slight drop with tg128 (5%):

$ lscpu
On-line CPU(s) list:                0-93
Off-line CPU(s) list:               94,95
Thread(s) per core:                 1
Core(s) per socket:                 24
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              106
Model name:                         Intel(R) Xeon(R) Gold 6336Y CPU @ 2.40GHz


Base:
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           pp512 |       225.78 ± 17.27 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           tg128 |         33.76 ± 0.19 |
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           pp512 |       241.63 ± 33.17 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           tg128 |         34.70 ± 0.66 |

| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           pp512 |        237.33 ± 9.02 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           tg128 |         33.01 ± 1.42 |

NUMA opt:

| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           pp512 |        262.09 ± 3.29 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           tg128 |         31.90 ± 0.10 |
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           pp512 |        262.37 ± 2.27 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           tg128 |         31.90 ± 0.12 |
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           pp512 |        260.17 ± 4.15 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      94 |           tg128 |         32.18 ± 0.26 |

And for aarch64, there's 16.8% uplift for pp512 and 25.9% uplift for tg128 with 64 threads as base data (64 cores per numa node in the platform, will get best performance with 64 threads in aarch64 as base data), if comparing with 128 threads as base data, that's 27.9% and 140% uplift for pp512 and tg128 respectively:

Base:
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      64 |           pp512 |        498.54 ± 1.69 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      64 |           tg128 |         25.06 ± 0.01 |
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      64 |           pp512 |        499.53 ± 0.45 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |      64 |           tg128 |         25.17 ± 0.13 |

Base with 128 threads (see performance drop compared to 64 threads):
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           pp512 |        451.64 ± 9.04 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           tg128 |         11.06 ± 0.92 |
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           pp512 |        471.37 ± 5.17 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           tg128 |         13.03 ± 0.48 |


NUMA OPT:
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           pp512 |        582.72 ± 0.44 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           tg128 |         31.30 ± 0.07 |
| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           pp512 |        577.65 ± 0.46 |
| llama 8B Q4_0                  |   4.33 GiB |     8.03 B | CPU        |     128 |           tg128 |         31.71 ± 0.06 |

So the feature would improve performance significantly for aarch64 platform.

add GGML_USE_NUMA_MIGRATE feature to optimize cross NUMA op computation

a7a6ce9

github-actions bot added examples ggml changes relating to the ggml tensor library for machine learning labels May 20, 2025

fix the threads number larger than numa node numbers

23fe7d3

fix the buffer allocate size for NUMA page migration

e5cb47d

fix the cgraph null issue when running with llama-bench

ab80f55

use ggml_barrier instead of ggml_barrier_numa_aware for src1 data bar…

3e3a878

…rier as the barrier only happens in few cores

remove debug code for ggml barrier

ed4d987

add GGML_USE_NUMA_MIGRATE feature to optimize cross NUMA op computation #13649

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add GGML_USE_NUMA_MIGRATE feature to optimize cross NUMA op computation #13649

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Result with the feature

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