HiTC: High-Performance Triangle Counting on HBM-Equipped FPGAs Using HLS
Bibliographic record
Abstract
Triangle counting (TC) is one of the fundamental computing patterns in graph computing and social networks. Due to its high memory-to-computation ratio and random memory access patterns, it is nontrivial to accelerate TC's performance. In this work, we propose a high-performance TC (HiTC) accelerator to speed up triangle counting on high-bandwidth memory (HBM)-equipped FPGAs via software/hardware codesign. First, we propose hardware-friendly reordering, tiling, and encoding techniques to address the random access issue and optimize bandwidth utilization. Based on that, we design a streaming-based FPGA accelerator that leverages HBM to achieve higher bandwidth and customize the computation pipeline for better computing throughput. Experiments using the SuiteSparse dataset show that our HiTC achieves a geomean speedup of 8.6x (up to 24.1x) over the Vitis TC FPGA library on the AMD/Xilinx HBM-based Alveo U280 FPGA. Compared to the software implementation on two 12-core Intel Xeon Silver 4214 CPUs, HiTC achieves a geomean speedup of 18.6x (up to 669.8x).
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".