Survey of Biological High Performance Computing: Algorithms, Implementations and Outlook Research
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
During recent years there has been an explosive growth of biological data coming from genome projects, proteomics, protein structure determination, and the rapid expansion in digitization of patient biological data. Powerful computational techniques are required to understand and analyze biological information encoded by DNA sequences, which are frequently compared and searched for matching or near-matching patterns. Comparison of DNA sequences and genes can be useful to investigate the common functionalities of the corresponding organisms and to get a better understanding of how specific genes or groups of genes are organized. This kind of similarity calculation is known as sequence alignment and its objective is to identify similarities between subsequences of strings. Gene sequence alignment is one such problem that serves as an initial step in many of the problems in bioinformatics. Solving computational biology problems can be accelerated by algorithmic improvements or with the help of high-performance computing architectures. Such architectures include superscalar uniprocessors, parallel systems and dedicated hardware implementations of algorithms. FPGAs have emerged as high-performance computing accelerators, capable of implementing massively parallelized versions of computationally intensive algorithms. Their reprogrammability allows different algorithm-specific computing architectures to be implemented using the same hardware resource. In this article we provide a state of the art review for this field of research. We identify specific algorithmic problems and how hardware architectures can be designed to solve them. We present systems recently reported, describe their main features, and provide a comparison between them. Finally, we offer some directions for future investigations
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 | 0.000 |
| 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 | 0.001 |
| 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