Parallelization of a Commercial Streamline Simulator and Performance on Practical Models
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
Summary We present the parallelization of a commercial streamline simulator to multicore architectures based on the OpenMP programming model and its performance on various field examples. This work is a continuation of recent work by Gerritsen et al. (2009) in which a research streamline simulator was extended to parallel execution. We identified that the streamline-transport step represents approximately 40-80% of the total run time. It is exactly this step that is straightforward to parallelize owing to the independent solution of each streamline that is at the heart of streamline simulation. Because we are working with an existing large serial code, we used specialty software to quickly and easily identify variables that required particular handling for implementing the parallel extension. Minimal rewrite to existing code was required to extend the streamline-transport step to OpenMP. As part of this work, we also parallelized additional run-time code, including the gravity-line solver and some simple routines required for constructing the pressure matrix. Overall, the run-time fraction of code parallelized ranged from 0.50 to 0.83, depending on the transport physics being considered. We tested our parallel simulator on a variety of large models including SPE 10, Forties-a UK oil/water model, Judy Creek-a Canadian waterflood/water-alternating-gas (WAG) model, and a South American black-oil model. We noted overall speedup factors from 1.8 to 3.3x for eight threads. In terms of real time, this implies that large-scale streamline simulation models as tested here can be simulated in less than 4 hours. We found speedup results to be reasonable when compared with Amdahl's ideal scaling law. Beyond eight threads, we observed minimal speedups because of memory bandwidth limits on our test machine.
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 | 0.001 | 0.001 |
| 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 it