Parallelization Strategies for DLRM Embedding Bag Operator on AMD CPUs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep learning recommendation models (DLRMs) are deployed extensively to support personalized recommendations and consume a large fraction of artificial intelligence (AI) cycles in modern datacenters with embedding stage being a critical component. Modern CPUs execute a lot of DLRM cycles because they are cost effective compared to GPUs and other accelerators. Our paper addresses key bottlenecks in accelerating the embedding stage on CPUs. Specifically, this work 1) explores novel threading schemes that parallelize embedding bag, 2) pushes the envelope on realized bandwidth by improving data reuse in caches, and 3) studies the impact of parallelization on load imbalance. The new embedding bag kernels have been prototyped in the ZenDNN software stack. When put together, our work on fourth generation EPYC processors achieve up to 9.9x improvement in embedding bag performance over state-of-the-art implementations, and improve realized bandwidth of up to 5.7x over DDR bandwidth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it