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
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