Efficient support of concurrent threads in a hybrid dataflow/von Neumann architecture
Bibliographic record
Abstract
Examines the thread support in a multi-threaded processor architecture, called the Super-Actor Machine (SAM). The SAM employs a novel organization of high-speed buffer memory known as the register-cache-a memory device organized both as a register file and a cache, and is used as a buffer between the execution unit and main memory. To characterize the floating-point performance of the machine on scientific benchmarks, a new performance measure is introduced, called the Floating-point Arithmetic and logic operations per machine Beat, or FAB for short. Results from a detailed simulation are very encouraging: for the memory-intensive SAXPY scientific loop, a processing element of the SAM can attain a .85 FAB rating as compared to a value of nearly 1 for the IBM RS/6000 which employs the combined floating-point add-multiply operation (this type of instruction has not been implemented on the SAM yet). Furthermore, the SAM can attain this rating with less execution resources (i.e. high-speed registers) than typical multi-threaded architectures.>
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".