Performance analysis of broadcasting algorithms on the Intel Single-Chip Cloud Computer
Bibliographic record
Abstract
Efficient broadcasting is essential for good performance on distributed or multiprocessor systems. Broadcasts are commonly used to implement message passing synchronization primitives, such as barriers, and also appear frequently in the set up stage of scientific applications. The Intel Single-Chip Cloud Computer (SCC), an experimental processor, uses synchronous message passing to facilitate communication between its 48 cores. RCCE, the SCC's message passing library, implements broadcasting in a traditional way: sending n-1 unicast messages, where n is the number of cores participating in the broadcast. This implementation can hinder performance as the number of cores participating in the broadcast increases and if the data being sent to each core is large. Also in the RCCE implementation, the broadcasting core is blocked from doing any useful work until all cores receive the broadcast. This paper explores several broadcasting schemes that take advantage of the resources of the SCC and the RCCE library. For example, we explore a scheme that propagates a broadcast to multiple cores in parallel and a scheme that parallelizes off-chip memory accesses which would otherwise need to be done sequentially. Our best broadcast scheme achieves a 35× speedup over the RCCE implementation. We also demonstrate that our improved broadcasting substantially reduces the time spent on communication in some benchmarks. While the broadcast schemes presented in this paper are implemented specifically for the SCC, they provide insight into the more general problem of broadcast communication and could be adapted to other types of distributed and multiprocessor systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".