TY - GEN
T1 - A Hierarchical Task Scheduler for Heterogeneous Computing
AU - Miniskar, Narasinga Rao
AU - Liu, Frank
AU - Young, Aaron R.
AU - Chakraborty, Dwaipayan
AU - Vetter, Jeffrey S.
N1 - Publisher Copyright:
© 2021, UT-Battelle, LLC.
PY - 2021
Y1 - 2021
N2 - Heterogeneous computing is one of the future directions of HPC. Task scheduling in heterogeneous computing must balance the challenge of optimizing the application performance and the need for an intuitive interface with the programming run-time to maintain programming portability. The challenge is further compounded by the varying data communication time between tasks. This paper proposes RANGER, a hardware-assisted task-scheduling framework. By integrating RISC-V cores with accelerators, the RANGER scheduling framework divides scheduling into global and local levels. At the local level, RANGER further partitions each task into fine-grained subtasks to reduce the overall makespan. At the global level, RANGER maintains the coarse granularity of the task specification, thereby maintaining programming portability. The extensive experimental results demonstrate that RANGER achieves a 12.7 × performance improvement on average, while only requires 2.7 % of area overhead.
AB - Heterogeneous computing is one of the future directions of HPC. Task scheduling in heterogeneous computing must balance the challenge of optimizing the application performance and the need for an intuitive interface with the programming run-time to maintain programming portability. The challenge is further compounded by the varying data communication time between tasks. This paper proposes RANGER, a hardware-assisted task-scheduling framework. By integrating RISC-V cores with accelerators, the RANGER scheduling framework divides scheduling into global and local levels. At the local level, RANGER further partitions each task into fine-grained subtasks to reduce the overall makespan. At the global level, RANGER maintains the coarse granularity of the task specification, thereby maintaining programming portability. The extensive experimental results demonstrate that RANGER achieves a 12.7 × performance improvement on average, while only requires 2.7 % of area overhead.
UR - http://www.scopus.com/inward/record.url?scp=85111432079&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111432079&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78713-4_4
DO - 10.1007/978-3-030-78713-4_4
M3 - Conference contribution
AN - SCOPUS:85111432079
SN - 9783030787127
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 57
EP - 76
BT - High Performance Computing - 36th International Conference, ISC High Performance 2021, Proceedings
A2 - Chamberlain, Bradford L.
A2 - Chamberlain, Bradford L.
A2 - Varbanescu, Ana-Lucia
A2 - Ltaief, Hatem
A2 - Luszczek, Piotr
PB - Springer Science and Business Media Deutschland GmbH
T2 - 36th International Conference on High Performance Computing, ISC High Performance 2021
Y2 - 24 June 2021 through 2 July 2021
ER -