Exploiting Data Locality in FFT Using Indirect Swap Network on Cell/B.E.
Bibliographic record
Abstract
Communication and synchronization are two main latency issues in computing FFT on parallel architectures. Both latencies have to be either hidden or tolerated to achieve high performance. One approach to achieve this is by multithreading. Another approach to tolerate latency is to map data efficiently onto the processors' local memory and exploiting data locality. Indirect swap networks, an idea proposed in VLSI circuits can be efficiently used to compute the butterfly computations in FFT. Data mapping in the swap network topology reduces the communication overhead by half at each iteration. Cell broadband engine (Cell/B.E.)processor is a heterogeneous multicoreprocessor for stream data applications and high performance computing. Its eight SIMD processing elements, synergistic processor elements (SPEs), provide multi-folded parallelism. In this paper, we investigate the improved Cooley-Tukey FFT algorithm based on indirect swap network, and design the parallel algorithm taking into consideration all the features of the Cell/B.E. architecture. The performance results show that the new algorithm on Cell/B.E. is 3.7 faster than the cluster for 4K input data size and 6.4 faster than the cluster for 16K input data size 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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".