Parallel Algorithm Design and Performance Evaluation of FDTD on 3 Different Architectures: Cluster, Homogeneous Multicore and Cell/B.E.
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
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.
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