Application of High Performance Asynchronous Acoustic Wave Equation Stencil Solver into a Land Survey
Bibliographic record
Abstract
Abstract This paper describes the application of high performance asynchronous stencil computations for 3D acoustic modeling on a synthetic land survey. Using the Finite-Difference Time-Domain (FDTD) method, a parallel Multicore Wavefront Diamond-tiling (MWD) stencil kernel (Malas et al. 2015, Malas et al. 2017) drives the high performance execution using temporal blocking to maximize data locality, while reducing the expensive horizontal data movement. As absorbing boundary conditions, we use Convolutional Perfectly Matched Layer (CPML), which have to be redesigned to not interrupt the asynchronous execution flow engendered by the MWD stencil kernel for the inner-domain points. The main idea consists in weakening the data dependencies by moving the CPML computations into the inner-computational loop of the MWD stencil kernel (Akbudak et al. 2019). In addition to handling the absorbing boundary conditions, applying the asynchronous MWD with CPML kernels to a realistic land survey requires the extraction of the wavefield value at each receiver position. We revisit the default extraction process and make it also compliant with the overall asynchrony of the 3D acoustic modeling. We report performance improvement up to 24% against the standard spatial blocking algorithm on Intel multicore chips using the synthetic land survey, which is representative of an area of interest in Saudi Arabia. While these results concur with previous performance campaign assessment, we can actually produce and assess the resulting 3D shot gather accuracy. To our knowledge, this is the first time the effectiveness of asynchronous MWD stencil kernel with CPML absorbing boundary conditions is demonstrated in an industrial seismic application.
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.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".