A Streaming Accelerator for Heterogeneous CPU-FPGA Processing of Graph Applications
Bibliographic record
Abstract
We explore the heterogeneous acceleration of graph processing on a platform that tightly integrates an FPGA with a multicore CPU to share system memory in a cache-coherent manner. We design an accelerator for the scatter phase of scatter-gather vertex-centric iterative graph processing. The accelerator accesses graph data exclusively from system memory, sharing it at the cache line granularity with the CPU, thus enabling the concurrent use of both the accelerator and software threads. We implement and evaluate the accelerator on the second generation Intel Heterogeneous Architecture Research Platform (HARPv2). Our evaluation, using two key graph processing kernels and both synthetically-generated and real-world graphs, shows that: (1) our accelerator delivers a performance improvement of about 2.4X over a single CPU thread, (2) our concurrent use of software and hardware is efficient and delivers speedups over the use of just software threads or just the accelerator, and (3) heterogeneous hardware-software acceleration delivers high graph processing throughputs. These results demonstrate the viability and promise of combined CPU-FPGA processing in contrast to the traditional offload model that leaves the CPU idle during acceleration.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".