Parallel Algorithm Design and Performance Evaluation of FDTD on 3 Different Architectures: Cluster, Homogeneous Multicore and Cell/B.E.
Bibliographic record
Abstract
Clusters built from single-core systems are cost-effective as for the performance improvement and availability. However, the hardware constraints put limitations on the performance of single-core systems. Hence, it is difficult to meet with the increasing high performance requirements of diversified applications at different levels for general purpose computing. A promising feasible solution is the novice multi-core systems which extend the parallelism to CPU level by integrating multiple processing units on a single die. This paper uses finite-difference time-domain (FDTD) algorithm as a case study, designing suitable parallel FDTD algorithms for three architectures: distributed-memory machines with single-core processors, shared-memory machines with dual-core processors, and the Cell Broadband Engine (Cell/B.E.) processor with nine heterogeneous cores. The experiment results show that the Cell/B.E. processor using 8 SPEs achieves a significant speedups of 7.05 faster than AMD single-core Opteron processor and 3.37 than AMD dual-core Opeteron processor at the processor level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".