Brief Announcement: Faster Data Structures in Transactional Memory using Three Paths
Bibliographic record
Abstract
With the introduction of Intel’s restricted hardware transactional memory (HTM) in commodity hardware, the transactional memory abstraction has fi-nally become practical to use. Transactional memory allows a programmer to easily implement safe concurrent code by specifying that certain blocks of code should be executed atomically. However, Intel’s HTM implementation does not offer any progress guarantees. Even in a single threaded system, a transaction can repeatedly fail for complex reasons. Consequently, any code that uses HTM must also provide a non-transactional fallback path to be executed if a trans-action fails. Since the primary goal of HTM is to simplify the task of writing concurrent code, a typical fallback path simply acquires a global lock, and then runs the same code as the transaction. This is essentially transactional lock eli-sion (TLE). Changes made by a process on the fallback path are not atomic, so transactions that run concurrently with a process on the fallback path may see inconsistent state. Thus, at the beginning of each transaction, a process reads the state of the global lock and aborts the transaction if it is held.
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 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 | 0.006 | 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 | 0.001 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".