Special issue on parallel architectures and bioinspired algorithm: guest editors message
Bibliographic record
Abstract
This special issue includes the extended version of selected papers presented at the fourth Parallel Architectures and Bioinspired Algorithms Workshop held in Galveston Island (TX, USA) on October 14, 2011 in conjunction with Parallel Architectures and Compilation Techniques (PACT).This workshop follows the success of the three previous workshops held in conjunction with PACT 2008 in Toronto, Canada [1], PACT 2009 in Raleigh, USA [2], and PACT 2010 in Vienna [3], Austria and the two previous Workshops on Parallel Bioinspired Algorithms [4] held in Oslo, 2005, (together with IEEE International Conference on Parallel Processing (ICPP)) [5] and London, 2007 (together with Association for Computing Machinery (ACM) Genetic and Evolutionary Computation Conference (Gecco) 2007) [6].These series of workshops has shown that knowledge fields such as parallel computer architectures and Parallel and Distributed Computing and Bioinspired Algorithms, which could seem quite different in a first approach, are able to find transversal elements that enrich them.Bioinspired Algorithms comprise a set of heuristics that can help to optimize a wide range of problems, including many tasks faced by parallel architectures designers, such as balancing computer load, fault-tolerance and dependability, thermal-aware design, and NoC design.In addition, Bioinspired Algorithms may help in finding solutions related to compilation and resource sharing issues, which are interesting problems for parallel architectures.Parallel architecture designers may also propose infrastructures that allow the improvement of computing performance of Bioinspired Algorithms, which usually face real-world problems that manage huge amounts of data.Alternatives to the classical sequential solution are needed by this community.Therefore, topics such as P2P, cluster and grid computing, cloud computing, and Graphics Processing Unit (GPUs) implementation of Bioinspired Algorithms are very interesting to this field.We have therefore considered this opportunity to give a broader view on the application of nature inspired computing techniques to hardware design and parallel architectures problem solving.We have also considered the interest of including an overview of the available bioinspired techniques based tools that have been used so far to solve problems related to automatic hardware design.We thus invited Professors Oscar Garnica and Juan Lanchares to work with us with this aim.Therefore, this special issue includes the paper entitled A review of bioinspired CAD tools for parallel architectures and hardware design [7] together with four papers carefully selected for
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.096 | 0.060 |
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".