FPGAs in the Cloud: Booting Virtualized Hardware Accelerators with OpenStack
Bibliographic record
Abstract
We present a new approach for integrating virtualized FPGA-based hardware accelerators into commercial-scale cloud computing systems, with minimal virtualization overhead. Partially reconfigurable regions across multiple FPGAs are offered as generic cloud resources through OpenStack (opensource cloud software), thereby allowing users to “boot” custom designed or predefined network-connected hardware accelerators with the same commands they would use to boot a regular Virtual Machine. We propose a hardware and software framework to enable this virtualization. This is a first attempt at closely fitting FPGAs into existing cloud computing models, where resources are virtualized, flexible, and have the illusion of infinite scalability. Our system can set up and tear down virtual accelerators in approximately 2.6 seconds on average, much faster than regular virtual machines. The static virtualization hardware on the physical FPGAs causes only a three cycle latency increase and a one cycle pipeline stall per packet in accelerators when compared to a non-virtualized system. We present a case study analyzing the design and performance of an application-level load balancer using a fully implemented prototype of our system. Our study shows that FPGA cloud compute resources can easily outperform virtual machines, while the system's virtualization and abstraction significantly reduces design iteration time and design complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".