TopADDPi: An Affordable and Sustainable Raspberry Pi Cluster for Parallel-Computing Topology Optimization
Bibliographic record
Abstract
Parallel-Computing Topology Optimization (PCTO) has gained importance, especially with the advancement of additive manufacturing (AM), due to its ability to tackle high-dimensional, high-resolution challenges. PCTO is highly relevant to sustainable manufacturing processes and technologies, enabling resource-efficient designs, reduced emissions, and advancements in Industry 4.0 integration. However, PCTO poses difficulties for newcomers or researchers, mainly because of its reliance on non-traditional computing environments and the limited availability of high-performance computing (HPC) resources. Addressing this, the study introduces TopADDPi, a Raspberry Pi-based cluster system, which has been purpose-built to facilitate learning and research in PCTO. It provides detailed instructions for assembling and configuring a Raspberry Pi cluster, with a focus on cost-effectiveness and ease of use. The study thoroughly investigates how different hardware and software configurations affect computing efficiency. In addition, through extensive numerical testing, the performance, energy consumption, and environmental impact of the Raspberry Pi cluster are benchmarked against conventional computing systems. The findings demonstrate the cluster’s advantages in handling parallel computing, its indispensable role in debugging, its remarkable energy efficiency, and its significantly reduced carbon footprint compared to conventional systems. These attributes establish the Raspberry Pi cluster as an invaluable tool for both educational and research applications in structural engineering, offering an affordable, sustainable, and indispensable solution for PCTO.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".