Accession Number:

AD1096530

Title:

Redefining Analytics for Small High-Performance Computing Clusters

Descriptive Note:

Technical Report,01 Apr 2015,31 Dec 2018

Corporate Author:

Brown University Providence United States

Personal Author(s):

Report Date:

2019-07-15

Pagination or Media Count:

12.0

Abstract:

Core contributions we made under this grant include a the development of the Network-Attached-Memory database architecture, b the first scalable RDMA-based transaction protocol, c a novel RDMA-based replication protocol, d the concept of learned index structures, e the first techniques to estimate the impact of Unknown Unknowns on aggregated query results, and f novel UDF compilation techniques. Overall, we were able to address all in the proposal outlined research challenges RC. We analyzed the RDMA performance gains RC I and developed an RDMA-based storage manager RC II, we developed modern query execution techniques for UDFs and reinvented the way indexing is done through our learned indexing approach RC III, we extended our work on data integration in heterogeneous environments RC IV, we studied the impact of data replication for RDMA-enabled networks RC V, we significantly advanced the area of UDF and query compilation for complex analytics RC VI, and developed a novel language to describe ML pipelines RC VII.

Subject Categories:

  • Computer Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE