On the Performance, Energy, and Power of Data-Access Methods in Heterogeneous Computing Systems
Bibliographic record
Abstract
Graphics processing units (GPUs) have delivered promising speedups in data-parallel applications. A discrete GPU resides on the PCIe interface and has traditionally required data to be moved from the host memory to the GPU memory via PCIe. In certain applications, the overhead of these data transfers between memory spaces can nullify any performance gains achieved from faster computation on the GPU. Recent advances allow GPUs to directly access data from the host memory across the PCIe bus, thereby alleviating the data-transfer bottlenecks. Another class of accelerators called accelerated processing units (APUs) mitigate data-transfer overhead by placing CPU and GPU cores on the same physical die. However, APUs in the current form provide several different data paths between the CPU and GPU, all of which can differently affect application performance. In this paper, we explore the effects of different available data paths on both GPUs and APUs in the context of a broader set of computation and communication patterns commonly referred to as dwarfs.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".