Parallel Processing on FPGAs: The Effect of Profiling on Performance
Bibliographic record
Abstract
The processing elements, logic resources, and on-chip block RAMs of modern FPGAs can not only be used for prototyping custom hardware modules, but also for parallel processing purposes by implementing multiple processors for a single task. This paper compares the performance of a single-processor implementation with two types of dual-processor implementations for a widely used radix-2 n-point FFT algorithm (Kooley and Tuckey, 1965) in terms of processing speed and FPGA resource utilization. In the first dual-processor implementation, the partitioning is performed based on the computation complexity - O(nlog(n)) of the radix-2 FFT algorithm. In the second implementation, the partitioning is based on a detailed profiling procedure applied to each line of the code in the single-processor implementation. Results obtained show that the speedup of the first dual-processor implementation is on average 1.3times faster than the single-processor implementation, whereas the second dual-processor implementation is about 1.9times faster which is very close to the expected speedup. This result shows that detailed profiling is crucial in identifying the bottlenecks of an algorithm (i.e., all the factors are taken into consideration) and consequently the algorithm can be efficiently mapped on a multiprocessor system based on the correct decision
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".