Bibliographic record
Abstract
Algorithmic speculation or high-level speculation is a promising programming paradigm which allows programmers to speculatively branch an execution into multiple independent parallel sections and then choose the best (perhaps fastest) amongst them. The continuing execution after the speculatively branched section sees only the modifications made by the best one. This programming paradigm allows programmers to harness parallelism and can provide dramatic performance improvements. In this paper we present the Multiverse speculative programming model. Multiverse allows programmers to exploit parallelism through high-level speculation. It can effectively harness large amounts of parallelism by speculating across an entire cluster and is not bound by the parallelism available in a single machine. We present abstractions and a runtime which allow programmers to introduce large scale high-level speculative parallelism into applications with minimal effort. We introduce a novel on-demand address space sharing mechanism which provide speculations efficient transparent access to the original address space of the application (including the use of pointers) across machine boundaries. Multiverse provides single commit semantics across speculations while guaranteeing isolation between them. We also introduce novel mechanisms to deal with scalability bottlenecks when there are a large number of speculations. We demonstrate that for several benchmarks, Multiverse achieves impressive speedups and good scalability across entire clusters. We study the overheads of the runtime and demonstrate how our special scalability mechanisms are crucial in scaling cluster wide.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".