Communication Characteristics of Message-Passing Scientific and Engineering Applications.
Bibliographic record
Abstract
Communication performance is an important factor that affects the performance of message-passing parallel applications running on clusters. A proper understanding of communication behaviour of parallel applications will help designing better communication subsystems and MPI libraries in the future. It will also help application developers to maximize their application performance on a target architecture. This paper examines the message passing communication characteristics of three applications (BTMZ, SP-MZ, and LU-MZ) in the NAS Multi-Zone parallel benchmark suite as well as two applications (SPECenv and SPECseis) in the SPEChpc2002 suite. Our study considers both point-to-point and collective communications. For point-to-point communications, we quantify the message type, message frequency, message size, and message destinations. For collectives, we examine their type, frequency, and payload. Our results show that the applications studied have diverse communication patterns and that they are mostly sensitive to the changes in the system size and the problem size. All applications use only a few collective operations, while SPEC applications use them frequently with very large payloads. Overall, our work helps in a better understanding of the communication workloads in the current and emerging parallel applications. Keyword Communication Characteristics, Message-Passing, Parallel Applications, MPI, Clusters
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".