ZHW: A Numerical CODEC for Big Data Scientific Computation
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
Distributed big data in scientific computing presents a major I/O performance bottleneck when exploiting data paral-lelism. Consumer and producer compute nodes are often throttled by saturated data channels when processing large numerical data. We describe ZHW, a hardware implementation of the ZFP numerical CODEC that can greatly reduce I/O pressure caused by large scientific datasets. Our ZHW design overcomes barriers that have prevented prior ZFP-like hardware accelerators from obtaining maximum compression in their implementations. The SystemC ZHW hardware library is available in an open source public repository. We demonstrate the practicality of ZHW by synthesizing our CODEC on an Ultrascale+ FPGA and analyzing performance.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.005 |
| 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