BitBlender: Scalable Bloom Filter Acceleration on FPGAs with Dynamic Scheduling
Bibliographic record
Abstract
The Bloom filter is one of the most widely used data structures in big data analytics to efficiently filter out vast amounts of noisy data. Unfortunately, prior Bloom filter designs only focus on single-input-stream acceleration, and can no longer match the increasing data rates offered by modern networks.To support large Bloom filters with low false-positive rate and high throughput, we present BitBlender, a configurable and scalable multi-input-stream Bloom filter acceleration framework in HLS. To effectively share one large bit-vector on chip among all streams, we design and implement the novel arbiter and unshuffle modules to dynamically schedule conflicting accesses to execute sequentially and non-conflicting accesses to execute in parallel. To support different user configurations of the Bloom filter, we also develop an automation flow, together with an accurate performance estimator, to automatically generate the best BitBlender design. Experimental results show that, on the AMD/Xilinx Alveo U280 FPGA, BitBlender achieves a throughput up to 2,194 MQueries/s (i.e., $8.8 \mathrm{~GB} / \mathrm{s}$) for a $\mathrm{9 6 M b}$ bit-vector with 0.01% false-positive rate. It achieves up to 10.4x speedup over a 24-thread CPU implementation and up to 4.9x speedup over a naively-duplicated multi-stream FPGA design. BitBlender will be released soon at https://github.com/SFU-HiAccel/BitBlender.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".