Fast critical sections via thread scheduling for FPGA-based multithreaded processors
Bibliographic record
Abstract
As FPGA based systems including soft processors become increasingly common, we are motivated to better understand the architectural trade-offs and improve the efficiency of these systems. Previous work has demonstrated that support for multithreading in soft processors can tolerate pipeline and I/O latencies as well as improve overall system throughput-however earlier work assumes an abundance of completely independent threads to execute. In this work we show that for real workloads, in particular packet processing applications, there is a large fraction of processor cycles wasted while awaiting the synchronization of shared data structures, limiting the benefits of a multithreaded design. We address this challenge by proposing a method of scheduling threads in hardware that allows the multithreaded pipeline to be more fully utilized without significant costs in area or frequency. We evaluate our technique relative to conventional multithreading using both simulation and a real implementation on a NetFPGA board, evaluating three deep-packet inspection applications that are threaded, synchronize, and share data structures, and show that overall packet throughput can be increased by 63%, 31%, and 41% for our three applications.
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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 | 0.001 |
| 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 | 0.000 |
| 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".