A Formal CSP Framework for Message-Passing HPC Programming
Bibliographic record
Abstract
To help programmers of high-performance computing (HPC) systems avoid communication-related errors, we employ a formal process algebra, communicating sequential processes (CSP), which has a strict semantics for interprocess communication and synchronization. Verification tools are available for CSP-specified programs to prove the absence of failures such as deadlock, and to explore potential multiprocess interactions. By introducing a CSP abstraction layer on top of the popular MPI message-passing primitives, we create a framework, called CSP4MPI, designed to largely hide the complexity of parallel programming for HPC. CSP4MPI is comprised of a C++ class library that provides a CSP-based process model, and a "cookbook" of candidate solutions for HPC programmers not trained in CSP. Developers can prototype their systems using CSP, and use verification tools to examine possible points of failure before implementing via the CSP4MPI library. Alternatively, they may choose an existing, verified solution from a number of common parallel application archetypes. By using CSP4MPI, HPC developers leverage the benefits of formal specification and verification in their work, in addition to obtaining an alternate method to developing HPC applications
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".