Bibliographic record
Abstract
Many parallel architectures support a memory model where some memory accesses are local, and thus inexpensive, while other memory accesses are remote, and potentially quite expensive. In order to achieve good parallel performance, it is often necessary to reduce the number of remote memory accesses. This can be done by the programmer, the compiler, or a combination of both. The overall goal is to minimize the work required by the programmer, and have the compiler automate the process as much as possible. The paper reports on compiler techniques for decreasing the number of remote memory accesses using locality analysis for a parallel dialect of C called EARTH-C. The locality analysis uses an algorithm inspired by type inference algorithms for fast points-to analysis. The algorithm estimates when an indirect reference via a pointer can be safely assumed to be a local access. The locality inference algorithm is also used to guide the automatic specialization of functions in order to take advantage of locality scientific to particular calling contexts. The locality analysis and automatic specialization has been implemented in the EARTH-C compiler which produces low level threaded code for the EARTH-C multithreaded architecture. Experimental results are presented for a set of benchmarks that operate on irregular, dynamically allocated data structures. The techniques give moderate to significant speedups and they do lessen the burden on the programmer.
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 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 | 0.001 |
| 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".