Name
Abstract | ||
|---|---|---|
Data analytics pipelines increasingly rely on databases to select, filter, and pre-process reams of data. These databases use data structures with irregular control flow like trees and hash tables which map poorly to existing database accelerators, leaving architects with a choice between CPUS— with stagnant performance—or accelerators that handle this complexity by relying on simpler but asymptotically sub-optimal algorithms.To bridge this gap, we propose Aurochs: a reconfigurable dataflow accelerator (RDA) that matches a CPU asymptotically but outperforms it by over 100 × on constant factors. We introduce a threading model for vector dataflow accelerators that extracts massive parallelism from irregular data structures using lightweight thread contexts. To implement this model, we add only a sparse scratchpad to an existing database accelerator— increasing area by 5 %. We reformulate common data structures using dataflow threads and evaluate Aurochs on ridesharing queries—outperforming a GPU by 8 ×. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCA52012.2021.00039 | 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) |
Keywords | DocType | ISSN |
database,dataflow accelerator,CGRA,RDA,Plasticine,Gorgon,Aurochs | Conference | 1063-6897 |
ISBN | Citations | PageRank |
978-1-6654-3334-1 | 1 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Vilim | 1 | 6 | 0.74 |
| Alexander Rucker | 2 | 12 | 2.51 |
| Kunle Olukotun | 3 | 4532 | 373.50 |