Writing study: FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPU
← ICML 2026 oral writing corpus
Felix X.-F. Ye, Xingjie Li, An Yu, Ming-Ching Chang, Linsong Chu, Davis Wertheimer · Official ICML 2026 oral record
Figure, table and section ledger
Editorial contribution type: Efficiency, inference & systems (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 36 physical pages
- Before references / appendix
- ≈ 9.16 page-equivalents
- Main figures / tables
- 5 / 3
- Attached appendix figures / tables
- 3 / 21
- Attached appendix span
- ≈ 23.91 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 1 | ≈ 0.55 pp. | 1.53% |
| 2. Background | 2 | ≈ 1.11 pp. | 3.08% |
| 3. FlashSinkhorn: Algorithm and Analysis | 3 | ≈ 2.95 pp. | 8.19% |
| 4. Experiments | 6 | ≈ 2.57 pp. | 7.14% |
| 5. Related Work | 8 | ≈ 0.97 pp. | 2.69% |
| 6. Conclusion | 9 | ≈ 0.29 pp. | 0.81% |
| Acknowledgements | 10 | ≈ 0.07 pp. | 0.19% |
| Impact Statement | 10 | ≈ 0.05 pp. | 0.14% |
| References | 10 | ≈ 2.92 pp. | 8.11% |
| A. Notation | 13 | ≈ 1 pp. | 2.78% |
| B. Derivation of Sinkhorn algorithm | 14 | ≈ 1.32 pp. | 3.67% |
| C. Differentiation of EOT with respect to data | 15 | ≈ 2.06 pp. | 5.72% |
| D. Proof in Section 3.1 | 17 | ≈ 2.24 pp. | 6.22% |
| E. Proof in Section 3.2 | 19 | ≈ 1.38 pp. | 3.83% |
| F. Streaming Hessian-Vector Products Derivation and Proof of Theorem 5 | 21 | ≈ 2.27 pp. | 6.31% |
| G. Implementation Details | 23 | ≈ 1.27 pp. | 3.53% |
| H. Full Experiment Results | 24 | ≈ 12.37 pp. | 34.36% |
All detected figure and table locations
Main text
Attached appendix
- Table 4 · p. 13
- Table 5 · p. 27
- Table 6 · p. 27
- Table 7 · p. 28
- Table 8 · p. 28
- Table 9 · p. 29
- Table 10 · p. 29
- Table 11 · p. 30
- Table 12 · p. 30
- Table 13 · p. 31
- Table 14 · p. 31
- Figure 6 · p. 32
- Table 15 · p. 32
- Table 16 · p. 33
- Table 17 · p. 33
- Table 18 · p. 34
- Table 19 · p. 34
- Table 20 · p. 35
- Table 21 · p. 35
- Table 22 · p. 36
- Table 23 · p. 36
- Table 24 · p. 36
- Figure 7 · p. 36
- Figure 8 · p. 36
Argument and organization
- 1 Introduction; 2 Background
Identifies memory traffic and establishes stabilized OT and differentiation notation.
- 3 Algorithm and Analysis
Builds streaming updates, transport application and Hessian-vector products from a shared attention correspondence.
- 4 Experiments; 5 Related Work; 6 Conclusion
Separates kernel benchmarks, numerical accuracy, downstream tasks and cost-class limitations.
Problem, claim and contribution
The bottleneck is GPU memory traffic for squared-Euclidean entropic OT, not a claim to remove the quadratic interaction arithmetic or support every transport cost.
Abstract, p.1; Conclusion, p.9 · Main paper
Argument and section order
Exact update identities precede the two-level memory model, and transport application distinguishes arbitrary potentials from converged optimal coupling.
Section 3.1, p.3; Section 3.2, p.5 · Main paper
Figures, captions and visual explanation
The kernel diagrams expose on-chip tiling and streaming, while the benchmark grid separates forward, backward, HVP, size, dimension and memory axes with a shared method legend.
Figures 1-2, p.4; Figure 3, p.7 · Main paper
Tables, comparisons and evidence
Comparisons align alternating versus symmetric schedules, and low-regularization results distinguish fixed-iteration throughput from total iterations needed for convergence.
Section 3.1, p.3; Table 3 and low-epsilon discussion, p.7 · Main paper
Appendix and reproducibility
The notation table separates induced and optimal couplings, while full experimental setup records numerical and timing choices needed to interpret kernel gains.
Appendix A, p.13; Appendix H.1, p.24 · Main paper
Clarity and writing conventions
Maximum forward-plus-backward speedup is not a uniform pipeline gain; the downstream saddle-escape panel separately shows the much smaller total wall-time change.
Table 3, p.7; Figure 5, p.9 · Main paper
What to reuse—and what not to infer
Transferable lesson: Carry exact algebra, memory complexity, finite precision and convergence budget through separate reporting layers instead of collapsing them into one speedup.
Boundary: Linear resident memory is not linear arithmetic; streaming identities can be exact while finite iterations and approximate CG still limit final numerical accuracy, and the largest gain is baseline/configuration-specific.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-10-07
- Reading status
- Writing-focused manuscript scan + selected visuals inspected
- Main text
- Writing-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 5, 6, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
- Appendix / supplement
- Appendix organization and main-text pointers were checked; focused text inspection of physical pages 13, 20, 24. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
- Visually inspected PDF pages
- 4, 7
This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.