Rename CUOPT_DISTRIBUTED_PDLP_PARTITIONER to CUOPT_MULTIGPU_PDLP_PARTITIONER; remove CUOPT_USE_DISTRIBUTED_PDLP - #1984
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…ITIONER; remove CUOPT_USE_DISTRIBUTED_PDLP Per team decision (Chris Maes, Bulle Mostovoi): - CUOPT_DISTRIBUTED_PDLP_PARTITIONER -> CUOPT_MULTIGPU_PDLP_PARTITIONER (avoids confusion with the existing D-PDLP solver name). Renames the parameter string, the three enum-value constants, the C++ enum type (distributed_pdlp_partitioner_t -> multigpu_pdlp_partitioner_t), and the pdlp_solver_settings_t field. - CUOPT_USE_DISTRIBUTED_PDLP removed entirely. It was never meant to be user-facing (already hidden from --help) - the C++ struct field (use_distributed_pdlp) stays as an internal implementation detail that pdlp.cu/solve.cu/cuopt_cli.cpp use for their own bookkeeping, but it is no longer part of the settable-parameter registry, so it can no longer be read/set via cuOptSetParameter, set_parameter, or any CLI flag. Multi-GPU PDLP dispatch is controlled solely by method == PDLP && (num_gpus == -1 || num_gpus > 1). Also fixes the Java bindings (already merged via NVIDIA#1961), which referenced both removed/renamed constants directly: drops setUseDistributedPdlp (no longer has a backing parameter) and renames setDistributedPdlpPartitioner to setMpdlpPartitioner. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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📝 WalkthroughWalkthroughPDLP dispatch now uses the method and GPU count to select multi-GPU solving. The separate distributed-PDLP enable setting is removed. The partitioner setting and Java API use multi-GPU names. ChangesMulti-GPU PDLP
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Refactor Suggested reviewers: Merge Risk: 🟡 Moderate · up to Multi-GPU PDLP can receive invalid MPS models without the requested checks, while API descriptions may mislead callers. Resolve the validation gap and outstanding documentation concerns before merging. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🧪 Generate unit tests (beta)
🛠️ Fix failing CI checks 💡
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Inline comments:
In `@cpp/include/cuopt/mathematical_optimization/constants.h`:
- Line 98: Add migration guidance to the C, C++, and CLI parameter documentation
under docs/cuopt/source, using CUOPT_MULTIGPU_PDLP_PARTITIONER as the
new-setting reference. Document that distributed_pdlp_partitioner and
CUOPT_DISTRIBUTED_PDLP_PARTITIONER map to multigpu_pdlp_partitioner and
CUOPT_MULTIGPU_PDLP_PARTITIONER, respectively, and that
CUOPT_USE_DISTRIBUTED_PDLP was removed; note the C API invalid-argument response
and CLI error-and-exit behavior for the old key.
In `@docs/cuopt/source/cuopt-java/convex/convex-api.rst`:
- Around line 105-107: Update the multi-GPU PDLP description near `setNumGpus`
to state that multi-GPU solves require Stable3 PDLP mode, default precision, and
no initial primal or dual solution; clarify that unsupported combinations are
rejected rather than implying they run. Add the same prerequisites to the
`setMpdlpPartitioner` Javadoc.
In
`@java/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/SolverSettings.java`:
- Around line 83-85: Before documenting multi-GPU PDLP behavior in
SolverSettings, update the problem-based solve path used by Java so PDLP with
num_gpus set to 2 or more dispatches to the multi-GPU solver; alternatively,
route Java solves through the MPS-model overload that already performs this
dispatch. Do not use use_distributed_pdlp as the trigger. Add a solve-level test
that verifies actual multi-GPU dispatch rather than only checking stored
settings.
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cpp/cuopt_cli.cppcpp/include/cuopt/mathematical_optimization/constants.hcpp/include/cuopt/mathematical_optimization/pdlp/solver_settings.hppcpp/src/math_optimization/solver_settings.cucpp/src/pdlp/pdlp.cudocs/cuopt/source/cuopt-java/convex/convex-api.rstjava/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/SolverSettings.javajava/cuopt/src/test/java/com/nvidia/cuopt/mathematicaloptimization/NativeIntegrationTest.java
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| @@ -26,16 +26,12 @@ void settingsExposeTypedValues() { | |||
| settings.setSetting(CuOptConstants.CUOPT_TIME_LIMIT, 12.5); | |||
| settings.setOptimalityTolerance(1.0e-6); | |||
| settings.setNumGpus(-1); | |||
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This is unrelated to this PR. But do we want specific APIs like setNumGpus? That creates a big API burden. I think we should stick with setSetting(CuOptConstants.CUOPT_NUM_GPUS, -1)
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Fair point, and out of scope for this rename PR since setNumGpus/setMpdlpPartitioner already shipped in #1961. Happy to open a follow-up if we want to trim these named wrappers in favor of setSetting.
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It would be great to have this fixed, could you open an issue ?
… only Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Inline comments:
In `@cpp/src/pdlp/solve.cu`:
- Line 2498: Before distributed dispatch via solve_lp_distributed_from_mps,
reject MPS models with quadratic objective values or quadratic constraints when
PDLP is selected and num_gpus is -1 or greater than 1; report a validation error
directing users to barrier. Preserve dispatch for models without quadratic
terms.
- Around line 812-813: Update the problem-object PDLP call paths guarded by this
`settings` check to use one GPU, including batch and MIP-internal calls; route
ordinary distributed PDLP solves through the MPS-data-model overload instead.
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cpp/cuopt_cli.cppcpp/include/cuopt/mathematical_optimization/pdlp/solver_settings.hppcpp/src/pdlp/pdlp.cucpp/src/pdlp/solve.cucpp/src/pdlp/solve.cuhcpp/tests/linear_programming/pdlp_distributed_test.cu
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- cpp/tests/linear_programming/pdlp_distributed_test.cu
- cpp/include/cuopt/mathematical_optimization/pdlp/solver_settings.hpp
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…ves to 1 GPU, reject QP/QCQP in distributed dispatch, document new partitioner constant, correct Java docs Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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looks good to me, thanks for the work ! |
| @@ -101,11 +101,11 @@ The settings API also includes: | |||
| * the static setting accessors; | |||
| * ``setMethod`` and ``setPDLPSolverMode``; | |||
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Not for this PR. But all of these special case method (e.g setMethod, setPDLPSolverMode, setNumGpus, and setMpdlpPartinioner) should be removed from the API.
The only one I think we can keep is setOptimalityTolerance.
| ^^^^^^^^^^^^^^ | ||
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| ``CUOPT_NUM_GPUS`` controls the number of GPUs to use for the solve. This setting is only relevant for LP problems that uses concurrent mode and supports up to 2 GPUs at the moment. Using this mode will run PDLP and barrier in parallel on different GPUs to avoid sharing single GPU resources. | ||
| ``CUOPT_NUM_GPUS`` controls the number of GPUs to use for the solve. In concurrent mode, this |
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In concurrent mode -> When solving an LP in concurrent mode
| With ``CUOPT_METHOD`` set to PDLP, ``CUOPT_NUM_GPUS`` set to ``-1`` (all GPUs visible to the | ||
| process) or a value greater than 1 instead dispatches to multi-GPU PDLP, which shards the | ||
| problem across GPUs. See ``CUOPT_MULTIGPU_PDLP_PARTITIONER`` below for how the problem is | ||
| partitioned. Multi-GPU PDLP requires the C API's MPS/QPS-model solve entry point and does not |
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I would remove the sentence beginning with
'Multi-GPU PDLP requires the '
This may be true now, but won't be in the future.
It would be fine to replace it with. Multi-GPU PDLP currently only support linear programs.
| ``0`` Auto (default; RoundRobin on 1 GPU, KaMinPar otherwise), ``1`` KaMinPar (multi-threaded | ||
| graph partitioner, better balanced shards at the cost of extra partitioning time), or ``2`` | ||
| RoundRobin (no partitioning graph built). This constant was previously named | ||
| ``CUOPT_DISTRIBUTED_PDLP_PARTITIONER``; the separate ``CUOPT_USE_DISTRIBUTED_PDLP`` toggle has |
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Remove the rest of the sentence after "; the separate CUOPT_USE_DISTRIBUTED_PDLP toggle has been removed ... "
| */ | ||
| public SolverSettings setDistributedPdlpPartitioner(int partitioner) { | ||
| return setSetting(CuOptConstants.CUOPT_DISTRIBUTED_PDLP_PARTITIONER, partitioner); | ||
| public SolverSettings setMpdlpPartitioner(int partitioner) { |
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Don't add special case code that can be handled by existing settings framework. Please remove all of this from the Java API.
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Tracked in #1998 along with setMethod/setPDLPSolverMode/setNumGpus, not touching this in the current PR per your other comment.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
|
@chris-maes, apart from removing java APIs (will be taken care in follow-up PR), all other reviews have been addressed. May I get another round of review. |
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Inline comments:
Review comments at @docs/cuopt/source/convex-settings.rst:
- Around line 132-139: Update the CUOPT_METHOD and CUOPT_NUM_GPUS paragraph to
scope multi-GPU PDLP support to CLI and MPS-based entry points, and state that C
cuOptSolve and Python Problem.solve use one GPU for PDLP. Preserve the
partitioner reference and linear-program limitation.
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| ``CUOPT_NUM_GPUS`` controls the number of GPUs to use for the solve. When solving an LP in | ||
| concurrent mode, this supports up to 2 GPUs, running PDLP and barrier in parallel on different | ||
| GPUs to avoid sharing single GPU resources. | ||
|
|
||
| With ``CUOPT_METHOD`` set to PDLP, ``CUOPT_NUM_GPUS`` set to ``-1`` (all GPUs visible to the | ||
| process) or a value greater than 1 instead dispatches to multi-GPU PDLP, which shards the | ||
| problem across GPUs. See ``CUOPT_MULTIGPU_PDLP_PARTITIONER`` below for how the problem is | ||
| partitioned. Multi-GPU PDLP currently only supports linear programs. |
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🚀 Performance & Scalability | 🟡 Minor | ⚡ Quick win
🔎 Supported by static analysis
🏁 Script executed:
git show 8e78d3e115d32bbb8c0513d620134c9eb139a39e:docs/cuopt/source/convex-settings.rst | sed -n '90,160p'
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git grep -n -i -E 'num_gpus|multi.?gpu|single.?gpu|PDLP' 8e78d3e115d32bbb8c0513d620134c9eb139a39e -- python/cuopt/cuopt/linear_programming cpp/include/cuopt docs/cuopt/source/cuopt-pythonRepository: NVIDIA/cuopt
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Scope the multi-GPU claim to supported entry points.
cuOptSolve and Python Problem.solve() materialize a problem object. That path resets PDLP requests with num_gpus == -1 or num_gpus > 1 to one GPU. Only the MPS overload dispatches these requests to distributed PDLP. The current unqualified text therefore promises multi-GPU solving for C and Python calls that run single-GPU. Limit this paragraph to CLI/MPS entry points or document the C/Python limitation here.
Suggested fix
-With ``CUOPT_METHOD`` set to PDLP, ``CUOPT_NUM_GPUS`` set to ``-1`` (all GPUs visible to the
-process) or a value greater than 1 instead dispatches to multi-GPU PDLP, which shards the
-problem across GPUs. See ``CUOPT_MULTIGPU_PDLP_PARTITIONER`` below for how the problem is
-partitioned. Multi-GPU PDLP currently only supports linear programs.
+For CLI and MPS-based entry points, ``CUOPT_METHOD`` set to PDLP and ``CUOPT_NUM_GPUS`` set to
+``-1`` (all GPUs visible to the process) or a value greater than 1 dispatches to multi-GPU PDLP,
+which shards the problem across GPUs. The C ``cuOptSolve`` and Python ``Problem.solve`` entry
+points materialize problem objects and use one GPU for PDLP. See
+``CUOPT_MULTIGPU_PDLP_PARTITIONER`` below for how the problem is partitioned. Multi-GPU PDLP
+currently only supports linear programs.📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ``CUOPT_NUM_GPUS`` controls the number of GPUs to use for the solve. When solving an LP in | |
| concurrent mode, this supports up to 2 GPUs, running PDLP and barrier in parallel on different | |
| GPUs to avoid sharing single GPU resources. | |
| With ``CUOPT_METHOD`` set to PDLP, ``CUOPT_NUM_GPUS`` set to ``-1`` (all GPUs visible to the | |
| process) or a value greater than 1 instead dispatches to multi-GPU PDLP, which shards the | |
| problem across GPUs. See ``CUOPT_MULTIGPU_PDLP_PARTITIONER`` below for how the problem is | |
| partitioned. Multi-GPU PDLP currently only supports linear programs. | |
| ``CUOPT_NUM_GPUS`` controls the number of GPUs to use for the solve. When solving an LP in | |
| concurrent mode, this supports up to 2 GPUs, running PDLP and barrier in parallel on different | |
| GPUs to avoid sharing single GPU resources. | |
| For CLI and MPS-based entry points, ``CUOPT_METHOD`` set to PDLP and ``CUOPT_NUM_GPUS`` set to | |
| ``-1`` (all GPUs visible to the process) or a value greater than 1 dispatches to multi-GPU PDLP, | |
| which shards the problem across GPUs. The C ``cuOptSolve`` and Python ``Problem.solve`` entry | |
| points materialize problem objects and use one GPU for PDLP. See | |
| ``CUOPT_MULTIGPU_PDLP_PARTITIONER`` below for how the problem is partitioned. Multi-GPU PDLP | |
| currently only supports linear programs. |
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Review comment at @docs/cuopt/source/convex-settings.rst around lines 132 - 139:
Update the CUOPT_METHOD and CUOPT_NUM_GPUS paragraph to scope multi-GPU PDLP
support to CLI and MPS-based entry points, and state that C cuOptSolve and
Python Problem.solve use one GPU for PDLP. Preserve the partitioner reference
and linear-program limitation.
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Inline comments:
Review comments at
@cpp/include/cuopt/mathematical_optimization/pdlp/solver_settings.hpp:
- Line 354: Update the num_gpus parameter description in the solver settings to
clarify that multi-GPU PDLP dispatch applies only to the MPS-model solve_lp
entry point; problem-object solves change these GPU counts to 1.
Review comments at @cpp/src/pdlp/solve.cu:
- Line 2573: Update the MPS overload’s distributed dispatch to run the
equivalent representation and crossing-bounds checks before distributed presolve
and solve when problem_checking is enabled. Preserve the existing behavior when
checking is disabled, and locate the dispatch through
solve_lp_distributed_from_mps.
Review comments at
@java/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/SolverSettings.java:
- Around line 86-87: Revise the `SolverSettings` documentation to scope the
single-GPU statement to PDLP on the Java problem-object path; do not imply that
`setNumGpus` has no effect for other methods, such as Concurrent.
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| // count; -1 selects all visible GPUs. See use_distributed_pdlp. | ||
| // Concurrent LP/MIP: 1–2 GPUs. Multi-GPU PDLP (method=PDLP): up to the visible device | ||
| // count; -1 selects all visible GPUs, which dispatches to the multi-GPU PDLP engine | ||
| // whenever num_gpus == -1 or num_gpus > 1. |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
Limit the dispatch description to the MPS-model entry point.
Line 354 says these GPU counts dispatch to multi-GPU PDLP. For a problem-object solve, cpp/src/pdlp/solve.cu instead changes either count to 1. State that only the MPS-model solve_lp entry point dispatches to multi-GPU PDLP, so C++ callers do not expect a problem-object solve to use multiple GPUs.
As per path instructions, “Verify parameter descriptions match actual types/behavior”.
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Review comment at
@cpp/include/cuopt/mathematical_optimization/pdlp/solver_settings.hpp at line
354:
Update the num_gpus parameter description in the solver settings to clarify that
multi-GPU PDLP dispatch applies only to the MPS-model solve_lp entry point;
problem-object solves change these GPU counts to 1.
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| distributed_settings.use_distributed_pdlp = true; | ||
| return solve_lp_distributed_from_mps( | ||
| handle_ptr, mps_data_model, distributed_settings, use_pdlp_solver_mode); | ||
| handle_ptr, mps_data_model, settings, use_pdlp_solver_mode); |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
🔎 Supported by static analysis
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Preserve model validation on the distributed MPS path.
When problem_checking=true, method=PDLP, num_gpus=2, and presolver=None, the MPS overload dispatches directly to solve_lp_distributed_from_mps without forwarding problem_checking. The regular path checks representation and crossing bounds, but the distributed path only validates MPS array sizes before constructing the solver. Its constructor checks the shape-0 placeholder, not the MPS model. Invalid representation or crossing bounds can therefore reach the distributed solver.
Run equivalent representation and crossing-bounds checks before the distributed presolve and solve when problem_checking is enabled.
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Review comment at @cpp/src/pdlp/solve.cu at line 2573:
Update the MPS overload’s distributed dispatch to run the equivalent
representation and crossing-bounds checks before distributed presolve and solve
when problem_checking is enabled. Preserve the existing behavior when checking
is disabled, and locate the dispatch through solve_lp_distributed_from_mps.
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| * gRPC service), which these Java bindings do not yet expose; {@code solve()} here always | ||
| * solves on a single GPU, regardless of {@link #setNumGpus}. |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
Scope the single-GPU statement to PDLP.
Line 86 says Java solve() always uses one GPU. With method=Concurrent and num_gpus=2, cpp/src/pdlp/solve.cu can run Barrier on a second GPU. Say instead that the Java problem-object path runs PDLP on one GPU; do not imply that setNumGpus has no effect on other methods.
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Review comment at
@java/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/SolverSettings.java
around lines 86 - 87:
Revise the `SolverSettings` documentation to scope the single-GPU statement to
PDLP on the Java problem-object path; do not imply that `setNumGpus` has no
effect for other methods, such as Concurrent.
After applying the fix, consider running `coderabbit review --agent` for local
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/merge |
…ner wrappers (#2000) Removes the named setting wrappers Chris flagged as unnecessary API surface, keeping only setOptimalityTolerance. Callers now use setSetting(CuOptConstants.CUOPT_METHOD, ...) etc. directly, same as every other parameter. Stacked on #1984 since it touches the same lines, so the diff currently includes #1984's changes too; it'll shrink once #1984 merges and this rebases onto main. Addresses #1998. Authors: - Ramakrishna Prabhu (https://github.com/ramakrishnap-nv) Approvers: - Trevor McKay (https://github.com/tmckayus) - Chris Maes (https://github.com/chris-maes) URL: #2000
4 participants
Per team naming discussion: CUOPT_DISTRIBUTED_PDLP_PARTITIONER -> CUOPT_MULTIGPU_PDLP_PARTITIONER (avoids confusion with the existing D-PDLP solver), and CUOPT_USE_DISTRIBUTED_PDLP is removed entirely since it was never meant to be user-facing (already hidden from --help). Dispatch is now purely method == PDLP && (num_gpus == -1 || num_gpus > 1); the internal use_distributed_pdlp C++ field stays as implementation bookkeeping. Also fixes the already-merged Java bindings (#1961), which referenced both constants directly.
Breaking change to the C/C++/CLI surface (introduced ~2 months ago in #1391, author-described as internal). #1957, #1962, and #1981 alias to the old names and will need a follow-up once this merges.
Fixes #1931
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