Added Concurrency to Improve MPI Performance on Multicore
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
MPI implementations typically equate an MPI process with an OS-process, resulting in a coarse-grain programming model where MPI processes are bound to the physical cores. Fine-Grain (FG-MPI) extends the MPICH2 implementation of MPI and implements an integrated runtime system to allow multiple MPI processes to execute concurrently inside an OS-process. FG-MPI's integrated approach makes it possible to add more concurrency than available parallelism, while minimizing the overheads related to context switches, scheduling and synchronization. In this paper we evaluate the benefits of added concurrency for cache awareness and message size and show that performance gains are possible by using FG-MPI to adjust the grain-size of a program to better fit the cache and potential advantages in passing smaller versus larger messages. We evaluate the use of FG-MPI on the complete set of the NAS parallel benchmarks over large problem sizes, where we show significant performance improvement (20%-30%) for three of the eight benchmarks. We discuss the characteristics of the benchmarks with regards to trade-offs between the added costs and benefits.
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.
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