TY - GEN
T1 - Graphicionado
T2 - 49th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2016
AU - Ham, Tae Jun
AU - Wu, Lisa
AU - Sundaram, Narayanan
AU - Satish, Nadathur
AU - Martonosi, Margaret Rose
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/14
Y1 - 2016/12/14
N2 - Graphs are one of the key data structures for many real-world computing applications and the importance of graph analytics is ever-growing. While existing software graph processing frameworks improve programmability of graph analytics, underlying general purpose processors still limit the performance and energy efficiency of graph analytics. We architect a domain-specific accelerator, Graphicionado, for high-performance, energy-efficient processing of graph analytics workloads. For efficient graph analytics processing, Graphicionado exploits not only data structure-centric datapath specialization, but also memory subsystem specialization, all the while taking advantage of the parallelism inherent in this domain. Graphicionado augments the vertex programming paradigm, allowing different graph analytics applications to be mapped to the same accelerator framework, while maintaining flexibility through a small set of reconfigurable blocks. This paper describes Graphicionado pipeline design choices in detail and gives insights on how Graphicionado combats application execution inefficiencies on general-purpose CPUs. Our results show that Graphicionado achieves a 1.76-6.54x speedup while consuming 50-100x less energy compared to a state-of-The-Art software graph analytics processing framework executing 32 threads on a 16-core Haswell Xeon processor.
AB - Graphs are one of the key data structures for many real-world computing applications and the importance of graph analytics is ever-growing. While existing software graph processing frameworks improve programmability of graph analytics, underlying general purpose processors still limit the performance and energy efficiency of graph analytics. We architect a domain-specific accelerator, Graphicionado, for high-performance, energy-efficient processing of graph analytics workloads. For efficient graph analytics processing, Graphicionado exploits not only data structure-centric datapath specialization, but also memory subsystem specialization, all the while taking advantage of the parallelism inherent in this domain. Graphicionado augments the vertex programming paradigm, allowing different graph analytics applications to be mapped to the same accelerator framework, while maintaining flexibility through a small set of reconfigurable blocks. This paper describes Graphicionado pipeline design choices in detail and gives insights on how Graphicionado combats application execution inefficiencies on general-purpose CPUs. Our results show that Graphicionado achieves a 1.76-6.54x speedup while consuming 50-100x less energy compared to a state-of-The-Art software graph analytics processing framework executing 32 threads on a 16-core Haswell Xeon processor.
UR - http://www.scopus.com/inward/record.url?scp=85009380806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009380806&partnerID=8YFLogxK
U2 - 10.1109/MICRO.2016.7783759
DO - 10.1109/MICRO.2016.7783759
M3 - Conference contribution
AN - SCOPUS:85009380806
T3 - Proceedings of the Annual International Symposium on Microarchitecture, MICRO
BT - MICRO 2016 - 49th Annual IEEE/ACM International Symposium on Microarchitecture
PB - IEEE Computer Society
Y2 - 15 October 2016 through 19 October 2016
ER -