High-Throughput Scientific Computation with Heterogeneous Clusters: A Kitchen-Sink Approach using the Actor Model
Bibliographic record
Abstract
Scientific discovery has become increasingly reliant on high-throughput computation (HTC). HTC can be hindered, however, by issues such as a lack of accessibility to high-performance computing infrastructure or a lack of reliability (e.g., from volunteer computing). In this paper, we demonstrate how the actor model of concurrent computation offers the necessary tools to create customizable, robust, and scalable distributed HTC environments via a kitchen-sink approach, whereby all available computing resources are thrown at a given batch-based computation with the goal of maximizing throughput by maximizing accessibility. We assess the effectiveness of the kitchen-sink approach by applying it to a hydrological model, the Structure for Unification of Multiple Modeling Alternatives (SUMMA), to perform a simulation involving over half a million independent sub-simulations. We evaluate the proposed approach in two scenarios: one without node failures and one with multiple node failures. Our results affirm that the kitchen-sink approach not only successfully navigates these scenarios, but it also offers a novel and appealing approach to HTC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".