A Wait-Free Dynamic Storage Allocator by Adopting the Helping Queue Pattern
Bibliographic record
Abstract
Most of the real-time applicable dynamic storage allocators rely on conventional locking strategies for protecting globally accessible data. But it is common that lock compositions do not scale well under high allocation and deallocation rates in parallel scenarios, as they lead to convoy effects. Furthermore, lock compositions lead to jitter, which is often a critical factor in real-time systems. Additionally, it is often desirable to guarantee progress of threads in order to be able to determine the worst-case execution time. This led us designing a wait-free dynamic storage allocator (DSA), which can guarantee progress of threads and does not influence other threads to make progress. Our DSA implementation relies on a kind of buddy strategy with approximate best-fit. Hence, it ensures for this kind of allocation strategy typical memory wastage as a result of internal fragmentation. Preliminary tests show that we can outperform established DSA implementations in terms of predictability, like the famous TLSF memory allocator. To the best of our knowledge, our DSA is the first known approach using a scalable and bounded nonblocking synchronization strategy. Our approach towards a wait-free DSA algorithm is applicable in real-time applications where adequate a priori knowledge about the memory requirements is available because it uses a statically allocated heap. We think that most real-time systems — especially ones with hard timing constraints — fulfill this precondition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".