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Record W4253159728 · doi:10.1504/ijhpcn.2017.084246

pvFPGA: paravirtualising an FPGA-based hardware accelerator towards general purpose computing

2017· article· en· W4253159728 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of High Performance Computing and Networking · 2017
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceField-programmable gate arrayPipeline (software)Embedded systemHardware accelerationFPGA prototypeDirect memory accessContext (archaeology)Computer hardwareOperating systemTransfer (computing)

Abstract

fetched live from OpenAlex

This paper presents an ameliorated design of pvFPGA, which is a novel system design solution for virtualising an FPGA-based hardware accelerator by a virtual machine monitor (VMM). The accelerator design on the FPGA can be used for accelerating various applications, regardless of the application computation latencies. In the implementation, we adopt the Xen VMM to build a paravirtualised environment, and a Xilinx Virtex-6 as an FPGA accelerator. The data transferred between the x86 server and the FPGA accelerator through direct memory access (DMA), and a streaming pipeline technique is adopted to improve the efficiency of data transfer. Several solutions to solve streaming pipeline hazards are discussed in this paper. In addition, we propose a technique, hyper-requesting, which enables portions of two requests bidding to different accelerator applications to be processed on the FPGA accelerator simultaneously through DMA context switches, to achieve request level parallelism. The experimental results show that hyper-requesting reduces request turnaround time by up to 80%.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.324
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it