A knowledge discovery methodology for the performance evaluation of scientific software
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
In this paper we define a knowledge discovery in databases (KDD) methodology to automatically generate metadata (i.e., knowledge rules) from software/machine pair performance databases. This metadata can be used to characterize the computational behavior of various classes of software or machines. The core and the most computationally intensive part of the KDD methodology is the data mining phase which identifies "interesting" patterns from the performance data. The discovery patterns are expressed in a high level representation to be used to summarize and predict the computational behavior of the targeted software/machine. This paper presents an implementation and evaluation of the proposed KDD process for a class of scientific software together with three data mining algorithms (ID3, HOODG, and CN2). For this case study we have selected a set of software that implements the "mesh/grid partitioning " phase of the domain decomposition approach used for the parallel processing of partial differential equation (PDEs) computations. The raw performance database is generated from a population of elliptic PDEs and PELLPACK [HRW 98] solvers by varying the PDE domain, mesh, and domain partitioning (DP) algorithm. The goal of the KDD process here is to evaluate the performance of PELLPACK, CHACO [HL95c], METIS [KK95c], and PARTY [PD96] algorithms/software. This case study shows that (a) the three data mining algorithms used are qualitatively and quantitatively equally effective, (b) the knowledge discovered for the DP algorithms by this KDD process is quantitatively similar to that deduced by purely experimental observations [VH97], and (c) the KDD process is not limited by the size of the performance data and its dimensionality.
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 | 0.007 | 0.001 |
| 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 | 0.002 | 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