Opportunistic computing in GPU architectures
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
Data transfer overhead between computing cores and memory hierarchy has been a persistent issue for von Neumann architectures and the problem has only become more challenging with the emergence of manycore systems. A conceptually powerful approach to mitigate this overhead is to bring the computation closer to data, known as Near Data Computing (NDC). Recently, NDC has been investigated in different flavors for CPU-based multicores, while the GPU domain has received little attention. In this paper, we present a novel NDC solution for GPU architectures with the objective of minimizing on-chip data transfer between the computing cores and Last-Level Cache (LLC). To achieve this, we first identify frequently occurring Load-Compute-Store instruction chains in GPU applications. These chains, when offloaded to a compute unit closer to where the data resides, can significantly reduce data movement. We develop two offloading techniques, called LLC-Compute and Omni-Compute. The first technique, LLC-Compute, augments the LLCs with computational hardware for handling the computation offloaded to them. The second technique (Omni-Compute) employs simple bookkeeping hardware to enable GPU cores to compute instructions offloaded by other GPU cores. Our experimental evaluations on nine GPGPU workloads indicate that the LLC-Compute technique provides, on an average, 19% performance improvement (IPC), 11% performance/watt improvement, and 29% reduction in on-chip data movement compared to the baseline GPU design. The Omni-Compute design boosts these benefits to 31%, 16% and 44%, respectively.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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