TY - GEN
T1 - Deploying OpenMP task parallelism on multicore embedded systems with MCA task APIs
AU - Sun, Peng
AU - Chandrasekaran, Sunita
AU - Zhu, Suyang
AU - Chapman, Barbara
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/23
Y1 - 2015/11/23
N2 - Heterogeneous multicore embedded systems are rapidly growing with cores of varying types and capacity. Programming these devices and exploiting the hardware has been a real challenge. The programming models and its execution are typically meant for general purpose computation, they are mostly too heavy to be adopted for the resource-constrained embedded systems. Embedded programmers are still expected to use low-level and proprietary APIs, making the software built less and less portable. These challenges motivated us to explore how OpenMP, a high-level directive-based model, could be used for embedded platforms. In this paper, we translate OpenMP to Multicore Association Task Management API (MTAPI), which is a standard API for leveraging task parallelism on embedded platforms. Our results demonstrate that the performance of our OpenMP runtime library is comparable to the state-of-the-art task parallel solutions. We believe this approach will provide a portable solution since it abstracts the low-level details of the hardware and no longer depends on vendor-specific API.
AB - Heterogeneous multicore embedded systems are rapidly growing with cores of varying types and capacity. Programming these devices and exploiting the hardware has been a real challenge. The programming models and its execution are typically meant for general purpose computation, they are mostly too heavy to be adopted for the resource-constrained embedded systems. Embedded programmers are still expected to use low-level and proprietary APIs, making the software built less and less portable. These challenges motivated us to explore how OpenMP, a high-level directive-based model, could be used for embedded platforms. In this paper, we translate OpenMP to Multicore Association Task Management API (MTAPI), which is a standard API for leveraging task parallelism on embedded platforms. Our results demonstrate that the performance of our OpenMP runtime library is comparable to the state-of-the-art task parallel solutions. We believe this approach will provide a portable solution since it abstracts the low-level details of the hardware and no longer depends on vendor-specific API.
KW - Heterogeneous Multicore Embedded Systems
KW - MTAPI
KW - OpenMP
KW - Parallel Computing
UR - https://www.scopus.com/pages/publications/84961704415
U2 - 10.1109/HPCC-CSS-ICESS.2015.88
DO - 10.1109/HPCC-CSS-ICESS.2015.88
M3 - Conference contribution
AN - SCOPUS:84961704415
T3 - Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015
SP - 843
EP - 847
BT - Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015
Y2 - 24 August 2015 through 26 August 2015
ER -