Name
Abstract | ||
|---|---|---|
Cloud computing is becoming a major trend for delivering and accessing infrastructure on demand via the network. Meanwhile, the usage of FPGAs (Field Programmable Gate Arrays) for computation acceleration has made significant inroads into multiple application domains due to their ability to achieve high throughput and predictable latency, while providing programmability, low power consumption and time-to-value. Many types of workloads, e.g. databases, big data analytics, and high performance computing, can be and have been accelerated by FPGAs. As more and more workloads are being deployed in the cloud, it is appropriate to consider how to make FPGAs and their capabilities available in the cloud. However, such integration is non-trivial due to issues related to FPGA resource abstraction and sharing, compatibility with applications and accelerator logics, and security, among others. In this paper, a general framework for integrating FPGAs into the cloud is proposed and a prototype of the framework is implemented based on OpenStack, Linux-KVM and Xilinx FPGAs. The prototype enables isolation between multiple processes in multiple VMs, precise quantitative acceleration resource allocation, and priority-based workload scheduling. Experimental results demonstrate the effectiveness of this prototype, an acceptable overhead, and good scalability when hosting multiple VMs and processes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2597917.2597929 | Conf. Computing Frontiers |
Keywords | Field | DocType |
design,distributed systems,experimentation,fpga virtualization,reconfiguration and heterogeneous computing,measurement,cloud,gate arrays,performance | Supercomputer,Computer science,Parallel computing,Field-programmable gate array,Real-time computing,Resource allocation,Throughput,Big data,Scalability,Cloud computing,Reconfigurable computing,Embedded system | Conference |
Citations | PageRank | References |
30 | 1.49 | 18 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Chen | 1 | 246 | 19.45 |
| Yi Shan | 2 | 253 | 15.77 |
| Yu Zhang | 3 | 50 | 3.32 |
| Yu Wang | 4 | 2279 | 211.60 |
| Hubertus Franke | 5 | 1257 | 104.86 |
| Xiaotao Chang | 6 | 86 | 6.18 |
| Kun Wang | 7 | 425 | 56.96 |