Bibliographic record
Abstract
To exploit parallelism and scalability of multiple GPUs in a system, it is critical to place compute and data together. However, two key techniques that have been used to hide memory latency and improve thread-level parallelism (TLP), memory interleaving, and thread block scheduling, in traditional GPU systems are at odds with efficient use of multiple GPUs. Distributing data across multiple GPUs to improve overall memory bandwidth utilization incurs high remote traffic when the data and compute are misaligned. Nondeterministic thread block scheduling to improve compute resource utilization impedes co-placement of compute and data. Our goal in this work is to enable co-placement of compute and data in the presence of fine-grained interleaved memory with a low-cost approach. To this end, we propose a mechanism that identifies exclusively accessed data and place the data along with the thread block that accesses it in the same GPU. The key ideas are (1) the amount of data exclusively used by a thread block can be estimated, and that exclusive data (of any size) can be localized to one GPU with coarse-grained interleaved pages; (2) using the affinity-based thread block scheduling policy, we can co-place compute and data together; and (3) by using dual address mode with lightweight changes to virtual to physical page mappings, we can selectively choose different interleaved memory pages for each data structure. Our evaluations across a wide range of workloads show that the proposed mechanism improves performance by 31% and reduces 38% remote traffic over a baseline system.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.401 | 0.331 |
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".