Communication Characteristics of Message-Passing Scientific and Engineering Applications.
Why this work is in the frame
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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
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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 | 0.000 | 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 it