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cuml.accel: serve managed memory from an RMM pool - #8810
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install() configured PrefetchResourceAdaptor(ManagedMemoryResource()) with no pool, so every allocation was a cudaMallocManaged plus a prefetch and every free a cudaFree, each costing milliseconds; a KMeans.predict issued 12 cudaFrees. Wrap the managed resource in a PoolMemoryResource the way cudf.pandas's managed_pool mode does, but starting from a small block that grows on demand, since cuml.accel is also installed in every worker process a user spawns. LinearRegression.predict on 1,000 rows: 1.35 ms -> 0.50 ms per call. Part of NVIDIA#8807. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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| Layer / File(s) | Summary |
|---|---|
Configure and verify managed-memory pooling python/cuml/cuml/accel/core.py, python/cuml/cuml_accel_tests/test_core.py, docs/source/cuml-accel/usage.rst |
install wraps managed memory in an RMM pool before applying the prefetch adaptor when the existing resource and hardware conditions are met. If pool construction raises MemoryError, it uses unpooled managed memory. Subprocess tests check both outcomes, and the documentation describes on-demand pool growth. |
Priority:
Estimated code review effort: 2 (Simple) | ~10 minutes
Change: Refactor
Possibly related issues
- Reduce data-transfer and allocation overhead in cuml.accel benchmark workloads #8807 reports allocation overhead from
cuml.accel.install()using unpooled managed memory. This change configures an RMM pool for those allocations.
Suggested labels: improvement, non-breaking
Merge Risk: 
55753
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Managed-memory installation retains an unpooled fallback, and no merge-blocking issue was identified.
Pre-merge checks: 
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Check name Status Explanation Resolution Docstring Coverage Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 4 functions across 2 files. Write docstrings for the functions missing them to satisfy the coverage threshold. Description check The description follows the template. It has a Summary section that explains the problem (per-allocation cudaMallocManaged, cudaMemPrefetchAsync, and cudaFree costs) and the key implementation choices… Add a GitHub closing keyword that links this PR to its issue, or confirm that no issue should be closed (for example, "Closes #1234" for the issue this PR resolves). If#8807is a tracking issue, state that explicitly. Also confirm the temp…-
Check name Status Explanation Title check The title "cuml.accel: serve managed memory from an RMM pool" matches the main change. The PR makes cuml.accel.install() serve managed allocations from an RMM pool, with the prefetch adaptor outermost… Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Full details: Title check
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Explanation
The title "cuml.accel: serve managed memory from an RMM pool" matches the main change. The PR makes cuml.accel.install() serve managed allocations from an RMM pool, with the prefetch adaptor outermost. The title is specific and concise, and it avoids vague terms.
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Full details: Description check
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Explanation
The description follows the template. It has a Summary section that explains the problem (per-allocation cudaMallocManaged, cudaMemPrefetchAsync, and cudaFree costs) and the key implementation choices (256 MiB initial pool, growth on demand, prefetch adaptor outermost, and a comparison with cudf.pandas managed_pool). It links the parent issue
#8807as "Part of" rather than with a GitHub closing keyword. The template asks for a closing keyword such as "Closes#1234", and the description does not include one. The description also omits the template's confirmation checkbox section about reviewing changes and disclosing unreviewed parts. The benchmark and test-result detail is thorough, but it does not replace those missing template items.Resolution
Add a GitHub closing keyword that links this PR to its issue, or confirm that no issue should be closed (for example, "Closes
#1234" for the issue this PR resolves). If#8807is a tracking issue, state that explicitly. Also confirm the template's responsibility checklist: state which parts were reviewed line by line and how the change was validated. The Summary already explains the problem and the implementation choices.
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Review comments at @python/cuml/cuml/accel/core.py:
- Line 272: Reduce the mandatory initial allocation configured by
initial_pool_size when constructing PoolMemoryResource so install() can start on
constrained workers; alternatively, retry pool construction with a smaller size
if the initial allocation fails.
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…e created PoolMemoryResource allocates its initial block on construction; on a constrained device that must not stop acceleration, so catch the MemoryError and use the unpooled managed resource that ran before. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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Review comments at @python/cuml/cuml/accel/core.py:
- Line 281: Update the MemoryError fallback branch that assigns `mr = managed`
so its debug message identifies unpooled managed memory; keep the pooled managed
memory message only in the branch that successfully selects
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When the initial pool block cannot be allocated, install() falls back to unpooled managed memory but still logged "Enabled pooled managed memory." Log main's "Enabled managed memory." in that case, and add a test of the selected resource and message for both paths. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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/ok to test 5575397 |
| mr = rmm.mr.PoolMemoryResource( | ||
| managed, initial_pool_size=256 << 20 | ||
| ) | ||
| except MemoryError as exc: |
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When will this exception be raised? I think you can allocate a pool bigger than the memory of the GPU without raising an exception:
import cupy as cp
import rmm
from rmm.allocators.cupy import rmm_cupy_allocator
# 10 TiB initial pool of managed memory: far more than GPU + host RAM
pool = rmm.mr.PoolMemoryResource(
rmm.mr.ManagedMemoryResource(), initial_pool_size=10 << 40
)
rmm.mr.set_current_device_resource(rmm.mr.PrefetchResourceAdaptor(pool))
cp.cuda.set_allocator(rmm_cupy_allocator)
print("free device memory:", cp.cuda.runtime.memGetInfo()[0] >> 20, "MiB")
x = cp.ones(64 << 20, dtype=cp.float32) # 256 MiB from the pool, written by a kernel
print("sum:", float(x.sum()))So I think this is an unnecessary try/except.
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I think we discussed doing this a few months ago, so I will try and find out what the conclusion was. Otherwise this looks like a free improvement, which is nice. However, nothing is ever free :D How much thinking and probing have you as a human done about this wrt the trade-offs, potential traps we are setting for users, etc? |
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Thanks for pushing on this. You were right that it isn't free. Since your question I've had Claude Code run a much broader set of experiments at my direction (scripts and logs available on request). The findings, the earlier history, and a mitigation I'd like your opinion on: This was tried before. #7834 added What goes wrong. Measured on a bare-metal RTX 5090 (32 GiB) with 62 GiB of host RAM, swapping only
The Speed depends on the per-allocation driver cost. One
Nothing between a117 and a161, in cuML or in RMM's managed, prefetch or pool resources, changes the allocation path. The difference is the system. On a cloud VM like the first row, the gain is large. Proposed mitigation: a bounded cache. Only allocations up to 64 MiB come from a pool, the pool is capped at 1 GiB per process, and everything else goes straight to managed memory as on main today. The per-call allocations are small, so they keep being reused, and retention is bounded at 1 GiB per process by construction. A Python prototype (dispatching via
At the smallest size the prototype gives back part of the gain to its per-allocation Python call. A real version removes that: a small RMM adaptor, or a capped Would you be open to reworking this PR into the capped version, perhaps behind an option at first? Or would you rather not have any caching in 🤖 Investigation run with Claude Code |
Summary
Part of #8807 (cuml.accel slower than scikit-learn on nine published benchmark workloads). Independent of the other PRs linked there.
cuml.accel.install()sets RMM's device resource toPrefetchResourceAdaptor(ManagedMemoryResource())with no pool, so every device allocation is acudaMallocManagedplus acudaMemPrefetchAsync, and every free is acudaFree. In an nsys trace of one 500 MBLinearRegression.predictthrough the proxy those were 1.5 ms of prefetch, a 11.8 ms and a 3.5 mscudaFree, for a call whose only kernel is 0.35 ms; aKMeans.predictcall issued 12cudaFrees. At 1,000 rows the whole call is dominated by them.This PR serves the managed allocations from an RMM
PoolMemoryResource, with the prefetch adaptor outermost so each allocation handed out is still prefetched, the same construction ascudf.pandas'smanaged_poolmode. Unlikecudf.pandasit starts from a 256 MiB block and grows on demand rather than reserving 80% of free device memory up front:cuml.accelis also installed in every worker process a user spawns (joblib, multiprocessing), and a large reservation in each of them is not acceptable. The measured gains are identical for the two sizes.--disable-uvmbehaves as before; the "RMM already configured" and "already managed" branches are unchanged.usage.rstgets a sentence on it.cuml_accel_tests/test_core.py,test_cli.py,test_basic_estimators.py: 380 passed with the four subprocess/loky tests deselected; those four hang identically on unmodifiedmainin my environment (KVM guest), so they are unrelated to this change.Benchmark, in isolation
cuml.accelproxy, float32 NumPy in / NumPy out, one warm-up, median of three, RTX PRO 6000 Blackwell. "before" is currentmain(identicalcore.pyto the 26.12a117 wheel). Published CPU is the Threadripper 7975WX number from the benchmarks page.Per-call fixed cost, 1,000 x 100 float32 input, median of 200 calls:
Published workload shapes:
The gains are a fixed 5 to 10 ms per call and 25 ms at the large size, so this is a small share of the published slowdowns on its own (those are the host-side copies addressed by the sibling PRs) but it is the whole story for small inputs, and it is what every other improvement sits on top of.
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