Scalable Graph Analytics on Emerging Cloud Infrastructure

Graphs are powerful tools for representing real-world networked data in a wide range of scientific and engineering domains. Understanding graph properties and deriving hidden information by performing analytics on graphs at extreme scale is critical for the progress of science across multiple domains and solving real-world impactful problems. Widespread adoption of cloud platforms for graph analytics has led to an exponential increase in the workloads while at the same time the rate of performance improvements of cloud platforms has slowed down. To address this, cloud platforms are being augmented with accelerators. However, the expertise required to realize high performance from such accelerator enhanced cloud platforms will limit their accessibility to the broader scientific and engineering community.

This project will research and develop a toolkit to provide Graph Analytics as a Service (GAaaS) to enable researchers to easily perform extreme-scale graph analytics workflows on accelerator enhanced cloud platforms. We will develop high-performance graph analytics algorithms and software for key graph workflows targeting accelerator enhanced cloud platforms. We will develop memory optimizations and partitioning and mapping techniques to exploit the heterogeneity and the high bandwidth provided by HBM.

AREAS OF INTEREST:

Graph Analytics, Cloud Computing, Memory Optimization, Acceleration on Heterogeneous Architectures, FPGA IP Core development

RECENT PUBLICATIONS:

Disclaimer: The following papers may have copyright restrictions. Downloads will have to adhere to these restrictions. They may not be reposted without explicit permission from the copyright holder. Any opinions, findings, and conclusions or recommendations expressed in these materials are those of the author(s) and do not necessarily reflect the views of the sponsors including National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), and any other sponsors listed in the publications.

1. Zhang, Bingyi; Kuppannagari, Sanmukh; Kannan, Rajgopal; Prasanna, Viktor, Efficient Neighbor-Sampling-based GNN Training on CPU-FPGA Heterogeneous Platform, IEEE High Performance Extreme Computing Conference, 2021  (Outstanding Student Paper)
2. Lin, Yi Chien; Zhang, Bingyi; Prasanna, Vikor K., GCN Inference Acceleration using High-Level Synthesis, IEEE High Performance Extreme Computing Conference, 2021
3. Zhang, Bingyi; Kannan, Rajgopal; Prasanna, Viktor, BoostGCN: A Framework for Optimizing GCN Inference on FPGA, 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2021
4. Yang, Yang; Kuppannagari, Sanmukh; Prasanna, Viktor K., A High Throughput Parallel Hash Table Accelerator on HBM-enabled FPGAs, IEEE ICFPT 2020, 2020
5. Zhang, Ruizhi; Wijeratne, Sasindu; Yang, Yang; Kuppannagari, Sanmukh; Prasanna, Viktor, A High Throughput Parallel Hash Table on FPGA using XOR-based Memory, IEEE HPEC 2020, 2020
6. Zhang, Bingyi; Zeng, Hanqing; Prasanna, Viktor, Hardware acceleration of large scale gcn inference, The 31st IEEE International Conference on Application-specific Systems, Architectures and Processors, 2020
7. Zhang, Bingyi; Zeng, Hanqing; Prasanna, Viktor K., Accelerating Large Scale GCN Inference on FPGA, The 28th IEEE International Symposium on Field-Programmable Custom Computing Machines, 2020

 

Click here for the complete list of publications of all Labs under Prof. Viktor K. Prasanna.