To Pack or Not to Pack: A Generalized Packing Analysis and Transformation
Bibliographic record
Abstract
Packing is an essential loop optimization for handcrafting a high-performance General Matrix Multiplication (GEMM). Packing copies a non-contiguous block of data to a contiguous block to reduce the number of TLB entries required to access it, avoiding expensive TLB misses. When copying data, packing can rearrange elements of the block to decrease the stride between consecutive accesses, improving spatial locality. Until now the use of packing has been limited to handcrafted GEMM implementations and to auto-tuning techniques. Existing loop optimizers, such as Polly and Pluto, either only apply packing to GEMM computations (Polly), or not at all (Pluto). This work proposes GPAT, a generalized packing analysis and code transformation that applies packing, when beneficial, to a generic input loop nest. GPAT is implemented in the Affine dialect of MLIR and evaluated on Polybench/C. GPAT applies packing to benchmarks beyond GEMM and obtains significant speedup compared to current loop optimizers that do not apply packing.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".