TapeFlow: Streaming Gradient Tapes in Automatic Differentiation
Bibliographic record
Abstract
Computing gradients is a crucial task in many domains, including machine learning, physics simulations, and scientific computing. Automatic differentiation (AD) computes gradients for arbitrary imperative code. In reverse mode AD, an auxiliary structure, the tape, is used to transfer intermediary values required for gradient computation. The challenge is how to organize the tape in the memory hierarchy since it has a high reuse distance, lacks temporal locality, and inflates working set by 2-4×. We introduce Tapeflow, a compiler framework to orchestrate and manage the gradient tape. We make three key contributions. i) We introduce the concept of regions, which transforms the tape layout into an array-of-structs format to improve spatial reuse. ii) We schedule the execution into layers and explicitly orchestrate the tape operands using a scratchpad. This reduces the required cache size and on-chip energy. iii) Finally, we stream the tape from the DRAM by organizing it into a FIFO of tiles. The tape operands arrive just-in-time for each layer. Tapeflow, running on the same hardware, outperforms Enzyme, the state-of-the-art compiler, by 1.3-2.5×, reduces on-chip SRAM usage by 5–40 ×, and saves 8× on-chip energy. We demonstrate Tapeflow on a wide range of algorithms written in general-purpose language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".