A Hybrid Architecture With Low Latency Interfaces Enabling Dynamic Cache Management
Bibliographic record
Abstract
The main focus of the dominant technologies in the high performance computation (HPC) market, such as GPU and multicore systems, is put on processing power, while much less attention has been paid to communication delays inside hybrid architectures. To fill this gap, this paper presents an experimental study on Intel's Broadwell Xeon multicore processor with integrated Arria 10 FPGA capabilities to characterize the communication delays between CPUs and the FPGA, using both the low latency cache coherent interface and the two PCIe links offered by this platform. The obtained results show that an FPGA cache access latency can be as low as 25 cycles at 400 MHz and that the platform is capable of reaching a bandwidth over 20 GB/s using an aggregate of the three available links. Furthermore, an FPGA-based cache management mechanism is proposed and implemented in this paper. A case study on a Merkle tree hash function shows that a hardware accelerator can achieve a fivefold data access acceleration in the worst case scenario. This scheme takes advantage of the QPI cache coherency and queuing theory to achieve a low latency and efficient memory management. In addition, design recommendations regarding the use of the CPU-FPGA platform for the implementation of fine-grained memory management schemes are suggested.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".