Bibliographic record
Abstract
Multicore architectures have emerged as an avenue to continue the improvement of software performance even as growth in single-core performance has struggled over the past two decades [1] [2].However, designing for such systems is a more complex task than for single-core, sequential computing.To effectively utilize multicore systems, designers must devise communication strategies, coordinate dataflow between processing elements in a way that avoids erroneous behaviour such as race conditions, and the software's general architecture must be designed in a way that takes advantage of advancements in hardware architecture [3] [4].In this thesis, we present an architecture and domain-specific language that allows software developers to rapidly prototype hardware architectures, software partitioning, and inter-core communication strategies at design time.We demonstrate an interpreter for this domain-specific language, and use it to illustrate a process of experimenting with inter-core communication and software partitioning strategies.i 5.1 Diagram illustrating the key components of a function as implemented.Input and output queues are exclusive to stream functions. . . . . . .5.2 An illustration of a stream packet containing two arguments. . . . . .5.3 An abstract syntax tree generated from the code in Code Sample 5.1.5.4 Illustration of code memory and execution stack while executing the program described in Code Sample 5.2 at two separate times. . . . .5.5 An illustration of core scheduling for a system containing 4 cores.Each core is given one turn within a single time unit (TU). . . . . . . . . .6.1 Program flow diagram of sequential function computation. . . . . . .6.2 Program flow diagram of synchronous-parallel function computation.6.3 Program flow of batch-parallel function computation. . . . . . . . . .6.4 Program flow diagram of F1, F2, and F3 running on a single core. . .6.5 Program flow diagram where F3 is offloaded to a separate core. . . .6.6 Program flow diagram where F2 and F3 are both offloaded to separate cores. . . . . . . . . . . .
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".