Sunstone: A Scalable and Versatile Scheduler for Mapping Tensor Algebra on Spatial Accelerators
Bibliographic record
Abstract
Tensor algebra, the main component of several popular machine learning techniques, benefits from modern accelerators due to the massive parallelism and data reuse available. To achieve the benefits, however, optimizing the dataflow is crucial: prior works showed that 19×energy savings are possible by tuning the dataflow. This optimization is challenging because: (1) the optimization space for modern chip architectures with several levels of memory and multiple levels of spatial processing is vast, and (2) distinct tensor computations follow different memory access and reuse patterns. In this manuscript, we algebraically analyze the possible reuse when executing tensor workloads on an accelerator. Based on our analysis, we develop several principles that significantly reduce the dataflow optimization space even for modern, complex chip architectures. Moreover, these principles are transferable to various tensor workloads with different memory access patterns. Compared to prior work, our techniques can find dataflow for typical tensor workloads up to 800×faster and with up to 1.9×better energy-delay products.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".