GPU Plan: Fix Density Math, Add Semiring/Masking, Adopt Two-Phase SpGEMM, and Add UVM Tuning (suggestions from GPT5-pro) #55
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- Created detailed planning documents for GPU acceleration - Updated for DGX Spark (Grace Hopper) with unified memory - Optimized for upper triangular matrices (50% savings) - Target: 1B×1B matrices at 0.1% density on single H100 - Timeline: 8-12 weeks with two-tier approach Key documents: - 00_UPDATED_CONTEXT.md: Impact analysis of DGX/triangular/billion-scale - IMPLEMENTATION_STRATEGY.md: Concrete implementation approach - QUICK_START.md: Quick reference guide - 01-06: Detailed architecture, kernels, API, testing, phases - EXECUTIVE_SUMMARY.md: High-level recommendations Changes from original plan: - Unified memory simplifies architecture (30% less code) - Triangular optimization for 50% performance gain - Single-GPU first (Tier 1), multi-GPU later if needed (Tier 2) - Same timeline but better outcome
…UVM tuning; deterministic mode; perf/tests updates
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From Sonnet 4.5 (Thinking) VerdictLGTM - Ready to Merge 🚀 This PR transforms the GPU implementation plan from a preliminary sketch into a production-ready technical specification. The math corrections alone justify the merge, and the formal abstractions (SemiringSpec, MaskSpec) will pay dividends during implementation and testing. No blocking issues identified. The plan is technically sound, well-documented, and properly scoped. |
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Summary
This PR updates the GPU implementation plans under
.plans/gpu_implementationto address correctness and completeness issues identified in the initial draft. It corrects scaling assumptions, introduces formal semiring and masking abstractions, replaces the kernel sketch with a proper two-phase sparse matrix multiplication (SpGEMM) design, and adds unified memory (UVM) performance guidance.Key Updates
SemiringSpecandMaskSpecas first-class concepts to define the avos semiring and structural masks (e.g., upper-triangular).cudaMemPrefetchAsyncandcudaMemAdvisefor performance on GH200/H100 systems.indptrto int64 for billion-scale workloads.Impact
No code changes yet—this PR revises the planning documents only. These updates make the GPU plan mathematically correct, technically credible, and aligned with state-of-the-art sparse GPU practice.
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