Expose distributed (multi-GPU) PDLP settings in the Java API - #1961
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The C++ constants (num_gpus, use_distributed_pdlp, distributed_pdlp_partitioner) already flow through the generated CuOptConstants and SolverSettings' generic setSetting/getSetting. This adds a typed DistributedPdlpPartitioner enum and typed convenience setters, mirroring setMethod/setPDLPSolverMode, plus docs and a test. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Understand this PR’s impact Explore downstream dependencies and potential security impact with Blast Radius. 📝 WalkthroughWalkthroughThe Java API adds distributed PDLP configuration for GPU count, enablement, and partitioning. It adds the ChangesDistributed PDLP Java API
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Change: Feature Suggested reviewers: Merge Risk: 🔵 Low · up to The API works, but its documentation can mislead Java users configuring GPU counts or distributed PDLP. This is low risk and straightforward to correct. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 42.86% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 7 functions across 3 files. (1 skipped: 1 unsupported.)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
🧪 Generate unit tests (beta)
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- 🪄 Fix CodeRabbit comments on this PR
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In
`@java/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/SolverSettings.java`:
- Line 72: Update SolverSettings documentation at
java/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/SolverSettings.java:72
to state that positive values select an explicit GPU count, reserving the
greater-than-one requirement for distributed PDLP. Update
docs/cuopt/source/cuopt-java/convex/convex-api.rst:104-105 to specify that
distributed PDLP requires SolverMethod.PDLP and setNumGpus(-1) or a value
greater than 1, with -1 meaning all GPUs visible to the process.
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docs/cuopt/source/cuopt-java/convex/convex-api.rstjava/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/DistributedPdlpPartitioner.javajava/cuopt/src/main/java/com/nvidia/cuopt/mathematicaloptimization/SolverSettings.javajava/cuopt/src/test/java/com/nvidia/cuopt/mathematicaloptimization/NativeIntegrationTest.java
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| * Graph partitioning strategy used to split a problem across GPUs when distributed (multi-GPU) | ||
| * PDLP is used, backed by constants generated from the C++ public header. | ||
| */ | ||
| public enum DistributedPdlpPartitioner { |
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Are you sure you want these to be an enum? The challenge with enums is that they require propagation throughout all the APIs. Whereas a regular integer setting should be automatically propagated through the API. What if we just made the partitioner setting an int?
…stributed PDLP -1 resolves to all visible GPUs, which is only one GPU on a single-GPU host; distributed sharding requires more than one GPU actually selected. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Exposes distributed (multi-GPU) PDLP settings on the Java side — a typed
DistributedPdlpPartitionerenum andsetNumGpus/setUseDistributedPdlp/setDistributedPdlpPartitionerconvenience methods, mirroringsetMethod/setPDLPSolverMode. The underlying C++ constants already flow through automatically via the generatedCuOptConstantsand the genericsetSetting/getSettingpassthrough.Like #1957, actually distributing a solve depends on the C API dispatch fix in #1958.
Fixes #1931
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