PolyGraph: Exposing the Value of Flexibility for Graph Processing Accelerators

Because of the importance of graph workloads and the limitations of CPUs/GPUs, many graph processing accelerators have been proposed. The basic approach of prior accelerators is to focus on a single graph algorithm variant (eg. bulk-synchronous + slicing). While helpful for specialization, this leav...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Proceedings - International Symposium on Computer Architecture s. 595 - 608
Hlavní autori: Dadu, Vidushi, Liu, Sihao, Nowatzki, Tony
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2021
Predmet:
accelerators
caches
Computational modeling
dataflow
flexibility
graph processing
Heuristic algorithms
Memory management
Parallel processing
Processor scheduling
reconfigurable
slicing
Switches
synchronous
tasks
Taxonomy
work efficiency
ISSN:2575-713X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Because of the importance of graph workloads and the limitations of CPUs/GPUs, many graph processing accelerators have been proposed. The basic approach of prior accelerators is to focus on a single graph algorithm variant (eg. bulk-synchronous + slicing). While helpful for specialization, this leaves performance potential from flexibility on the table and also complicates understanding the relationship between graph types, workloads, algorithms, and specialization.In this work, we explore the value of flexibility in graph processing accelerators. First, we identify a taxonomy of key algorithm variants. Then we develop a template architecture (PolyGraph) that is flexible across these variants while being able to modularly integrate specialization features for each.Overall we find that flexibility in graph acceleration is critical. If only one variant can be supported, asynchronous-updates/priority-vertex-scheduling/graph-slicing is the best design, achieving 1.93× speedup over the best-performing accelerator, GraphPulse. However, static flexibility per-workload can further improve performance by 2.71×. With dynamic flexibility per-phase, performance further improves by up to 50%.
ISSN:2575-713X
DOI:10.1109/ISCA52012.2021.00053