Unbounded Transactional Memory
Bibliographic record
Abstract
Hardware transactional memory should support unbounded transactions: transactions of arbitrary size and duration. We describe a hardware implementation of unbounded transactional memory, called UTM, which exploits the common case for performance without sacrificing correctness on transactions whose footprint can be nearly as large as virtual memory. We performed a cycle-accurate simulation of a simplified architecture, called LTM. LTM is based on UTM but is easier to implement, because it does not change the memory subsystem outside of the processor. LTM allows nearly unbounded transactions, whose footprint is limited only by physical memory size and whose duration by the length of a timeslice. We assess UTM and LTM through microbenchmarking and by automatically converting the SPECjvm98 Java benchmarks and the Linux 2.4.19 kernel to use transactions instead of locks. We use both cycle-accurate simulation and instrumentation to understand benchmark behavior. Our studies show that the common case is small transactions that commit, even when contention is high, but that some applications contain very large transactions. For example, although 99.9% of transactions in the Linux study touch 54 cache lines or fewer, some transactions touch over 8000 cache lines. Our studies also indicate that hardware support is required, because some applications spend over half their time in critical regions. Finally, they suggest that hardware support for transactions can make Java programs run faster than when run using locks and can increase the concurrency of the Linux kernel by as much as a factor of 4 with no additional programming work.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".