Tolerating Dependences Between Large Speculative Threads Via Sub-Threads
Bibliographic record
Abstract
Thread-level speculation (TLS) has proven to be a promising method of extracting parallelism from both integer and scientific workloads, targeting speculative threads that range in size from hundreds to several thousand dynamic instructions and have minimal dependences between them. Recent work has shown that TLS can offer compelling performance improvements for database workloads, but only when targeting much larger speculative threads of more than 50,000 dynamic instructions per thread, with many frequent data dependences between them. To support such large and dependent speculative threads, hardware must be able to buffer the additional speculative state, and must also address the more challenging problem of tolerating the resulting cross-thread data dependences In this paper we present hardware support for large speculative threads that integrates several previous proposals for TLS hardware. We also introduce support for subthreads: a mechanism for tolerating cross-thread data dependences by checkpointing speculative execution. When speculation fails due to a violated data dependence, with sub-threads the failed thread need only rewind to the checkpoint of the appropriate sub-thread rather than rewinding to the start of execution; this significantly reduces the cost of mis-speculation. We evaluate our hardware support for large and dependent speculative threads in the database domain and find that the transaction response time for three of the five transactions from TPC-C (on a simulated 4- processor chip-multiprocessor) speedup by a factor of 1.9 to 2.9.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".