EHGRID: An emulator of heterogeneous computational grids
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
Heterogeneous distributed computing is found in a variety of fields including scientific computing, Internet and mobile devices. Computational grids focusing primarily on computationally-intensive operations have emerged as a new infrastructure for high performance computing. Specific algorithms such as scheduling, load balancing and data redistribution have been devised to overcome the limitations of these systems and take full advantage of their processing power. However, experimental validation and fine-tuning of such algorithms require multiple heterogeneous platforms and configurations. We present EHGRID, a computational grid emulator based on the heterogeneous emulator Wrekavoc. EHGRID reshapes the virtual topology of a homogeneous cluster, degrades the performance of the processors and modifies the characteristics of the network links in an accurate, independent and reproducible way. We demonstrate its utility using two parallel matrix-vector programs and selected NAS parallel benchmarks on a series of four emulated grids.
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.
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