pvFPGA: accessing an FPGA-based hardware accelerator in a paravirtualized environment
Bibliographic record
Abstract
In this paper we present pvFPGA, the first system design solution for virtualizing an FPGA-based hardware accelerator on the x86 platform. Our design adopts the Xen virtual machine monitor (VMM) to build a paravirtualized environment, and a Xilinx Virtex-6 as an FPGA accelerator. The accelerator communicates with the x86 server via PCI Express (PCIe). In comparison to the recent accelerator virtualization solutions which primarily intercept and redirect API calls to the hosted or privileged domain's user space, pvFPGA virtualizes an FPGA accelerator directly at the lower device driver level. This gives rise to higher efficiency and lower overhead. In pvFPGA, each unprivileged domain allocates a shared data pool for both user-kernel and inter-domain data transfer. In addition, we propose a new component, the coprovisor, which enables multiple domains to simultaneously access an FPGA accelerator. The experimental results have shown that 1) pvFPGA achieves close-to-zero overhead compared to accessing the FPGA accelerator without the VMM layer, 2) the FPGA accelerator is successfully shared by multiple domains, and 3) distributing different maximum data transfer bandwidths to different domains is achieved by regulating the size of the shared data pool at the split driver loading time.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".