Complete framework for memory consistency with applications to the itanium architecture
Bibliographic record
Abstract
Multiprocessor computers are now common in the marketplace and will soon be on every desktop. A vital part of a multiprocessor computer's architecture is a definition of how the processors access common storage, that is, its memory consistency model. In order for programmers to write code that makes full use of the power of these computers they must comprehend completely the memory consistency model. The memory consistency model is usually complex, difficult to describe completely, and very difficult for programmers to use. The memory consistency models of the various architectures are often described using very diverse language. Attempts to simplify the language that describe the memory consistency models so that they are either easier for the programmer to use or so that similar descriptions can be used for various architectures generally fail to model all components of the system. In this thesis a previous uniform framework for describing memory consistency models is extended, allowing a general description of the models of any architecture that completely captures the memory features. It can model the relationship between programs, written in whatever language the programmer chooses, and the computations that arise from executions of these programs on the chosen architecture. Also, it precisely models dependency relationships. Using this framework two systems are defined; Itanium+A which is stronger than and Itanium+B which is weaker than the specifications of the memory consistency model from Intel's Itanium architecture. Itanium+A and Itanium+B differ very little. Itanium+A and Itanium+B are used to define the requirements for processor coordination on Itanium and to define and prove coordination algorithms for Itanium. Outside of the framework, Sun's Sparc architecture and Intel's Itanium architecture are compared, using descriptions that follow each architecture's specification. This found similarities when resticted to very few memory access instructions, but general incomparability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".