Presentation
Accelerating Range-Joins for Big Data Genomic Variant Annotation on HBM-enabled FPGAs
DescriptionRange join-based variant annotation is an essential stage in genomic big data analysis, often requiring complex conditional joins with databases spanning Terabytes in size. However, its performance on multi-threaded CPUs/GPUs have been bottlenecked by both the memory-access bandwidth and instruction/data dependencies. Furthermore, massive data-accesses involved in range joins for variant annotations drastically affect energy efficiency, and pose serious challenges to commercial adoption of fast-evolving genomic big data analysis. In this work, we present an efficient hardware-software co-design for range join-based variant annotations on clusters of HBM-enabled FPGAs. Our highly-scalable in-memory processing system achieves up-to 1.98x/6.51x/38.1x speedup/energy improvements/memory access reductions compared to state-of-the-art CPU solution, while being highly extensible to other big data applications of range join.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security