TY - GEN
T1 - Prometheus
T2 - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018
AU - Umar, Mariam
AU - Moore, Shirley V.
AU - Vetter, Jeffrey S.
AU - Cameron, Kirk W.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/7
Y1 - 2018/11/7
N2 - With the dramatic increase in scale expected for Exascale computing, there is a dire need for tuning of hardware configurations and software optimizations such that they are in unison. However, the expected increase in tunable hardware parameters makes searching through the design space for optimal hardware-And-software configurations much more challenging. Towards this end, we propose a composable hardware-software optimization framework called Prometheus. Prometheus uses a combination of analytical and machine-learning techniques to capture application characteristics and subsequently determine the hardware-software configuration for near-optimal performance. We evaluate Prometheus for its efficacy using two widely used proxy applications: LULESH and CoMD. We demonstrate that Prometheus identifies near-optimal hardware-software configurations and verify the results via brute-force search of the design space.
AB - With the dramatic increase in scale expected for Exascale computing, there is a dire need for tuning of hardware configurations and software optimizations such that they are in unison. However, the expected increase in tunable hardware parameters makes searching through the design space for optimal hardware-And-software configurations much more challenging. Towards this end, we propose a composable hardware-software optimization framework called Prometheus. Prometheus uses a combination of analytical and machine-learning techniques to capture application characteristics and subsequently determine the hardware-software configuration for near-optimal performance. We evaluate Prometheus for its efficacy using two widely used proxy applications: LULESH and CoMD. We demonstrate that Prometheus identifies near-optimal hardware-software configurations and verify the results via brute-force search of the design space.
KW - Domain specific languages
KW - High performance computing
KW - Performance modeling
KW - Reconfigurable hardware
UR - http://www.scopus.com/inward/record.url?scp=85058288889&partnerID=8YFLogxK
U2 - 10.1109/MASCOTS.2018.00032
DO - 10.1109/MASCOTS.2018.00032
M3 - Conference contribution
AN - SCOPUS:85058288889
T3 - Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018
SP - 244
EP - 250
BT - Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 September 2018 through 28 September 2018
ER -