Enabling Flexible Network FPGA Clusters in a Heterogeneous Cloud Data Center
Why this work is in the frame
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Bibliographic record
Abstract
We present a framework for creating network FPGA clusters in a heterogeneous cloud data center. The FPGA clusters are created using a logical kernel description describing how a group of FPGA kernels are to be connected (independent of which FPGA these kernels are on), and an FPGA mapping file. The kernels within a cluster can be replicated with simple directives within this framework. The FPGAs can communicate to any other network device in the data center, including CPUs, GPUs, and IoT devices (such as sensors). This heterogeneous cloud manages these devices with the use of OpenStack. We observe that our infrastructure is limited due to the physical infrastructure such as the 1~Gb Ethernet connection. Our framework however can be ported to other physical infrastructures. We tested our infrastructure with a database acceleration application. This application was replicated six times across three FPGAs within our cluster and we observed a throughput increase of six times as this scaled linearly. Our framework generates the OpenStack calls needed to reserve the compute devices, creates the network connections (and retrieve MAC addresses), generate the bitstreams, programs the devices, and configure the devices with the appropriate MAC addresses, creating a ready-to-use network device that can interact with any other network device in the data center.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it