Parallelizing Soft-Synths with Soft Real-Time Requirements
Bibliographic record
Abstract
Though parallel processing of applications related to video processing and rendering are widespread, parallel processing of audio synthesis software (also known as soft-synths) is not much researched. This paper addresses our research and experiments in parallelizing digital audio synthesizers on a commodity multicore platform where these synthesizers are most commonly used. Soft real-time requirements and overheads of parallelization are two of the competing forces in this research. As a case study, the ALSA (Advanced Linux Sound Architecture) Modular audio Synthesizer (AMS) is evaluated. AMS employs a modular approach to digital music synthesis, is often part of a standard Linux installation package, has a GUI for user interactions, and like other audio synthesizers it has a soft real-time requirement. The main intentions of parallelization are for enhancing throughput and hence stability, whereby more complex and higher quality audio can be generated. The GUI based interactive approach adds an extra challenge of a dynamic call graph that can change on-the-fly. The paper compares the pros and cons of the different techniques adopt-ed, and highlights the advantages of parallelization. The lessons learnt can also be used in parallelizing other existing audio synthesizers and designing new parallel synthesizers from scratch.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".