Cell Processing for Two Scientific Computing Kernels
Bibliographic record
Abstract
This chapter uses two scientific computing kernels to illustrate challenges of designing parallel algorithms for one heterogeneous multi-core processor, the Cell Broadband Engine processor (Cell/B.E.). It describes the limitation of the current parallel systems using single-core processors as building blocks. The limitation deteriorates the performance of applications which have data-intensive and computationintensive kernels such as Finite Difference Time Domain (FDTD) and Fast Fourier Transform (FFT). FDTD is a regular problem with nearest neighbour comminuncation pattern under synchronization constraint. FFT based on indirect swap network (ISN) modifies the data mapping in traditional Cooley- Tukey butterfly network to improve data locality, hence reducing the communication and synchronization overhead. The authors hope to unleash the Cell/B.E. and design parallel FDTD and parallel FFT based on ISN by taking into account unique features of Cell/B.E. such as its eight SIMD processing units on the single chip and its high-speed on-chip bus.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".