Abstract
Although mobile devices have been evolved enough to support complex mobile programs, performance of the mobile devices is lagging behind performance of servers. To bridge the performance gap, computation offloading allows a mobile device to remotely execute heavy tasks at servers. However, due to architectural differences between mobile devices and servers, most existing computation offloading systems rely on virtual machines, so they cannot offload native applications. Some offloading systems can offload native mobile applications, but their applicability is limited to well-analyzable simple applications. This work presents automatic cross-architecture computation offloading for general-purpose native applications with a prototype framework that is called Native Offloader. At compile-time, Native Offloader automatically finds heavy tasks without any annotation, and generates offloading-enabled native binaries with memory unification for a mobile device and a server. At run-time, Native Offloader efficiently supports seamless migration between the mobile device and the server with a unified virtual address space and communication optimization. Native Offloader automatically offloads 17 native C applications from SPEC CPU2000 and CPU2006 benchmark suites without a virtual machine, and achieves a geomean program speedup of 6.42× and battery saving of 82.0%.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 48th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2015 |
| Publisher | IEEE Computer Society |
| Pages | 521-532 |
| Number of pages | 12 |
| Volume | 05-09-December-2015 |
| ISBN (Electronic) | 9781450340342 |
| DOIs | |
| Publication status | Published - 2015 Dec 5 |
| Event | 48th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2015 - Waikiki, United States Duration: 2015 Dec 5 → 2015 Dec 9 |
Other
| Other | 48th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2015 |
|---|---|
| Country | United States |
| City | Waikiki |
| Period | 15/12/5 → 15/12/9 |
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All Science Journal Classification (ASJC) codes
- Hardware and Architecture
Cite this
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Architecture-aware automatic computation offload for native applications. / Lee, Gwangmu; Park, Hyunjoon; Heo, Seonyeong; Chang, Kyung Ah; Lee, Hyogun; Kim, Hanjun.
Proceedings - 48th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2015. Vol. 05-09-December-2015 IEEE Computer Society, 2015. p. 521-532.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Architecture-aware automatic computation offload for native applications
AU - Lee, Gwangmu
AU - Park, Hyunjoon
AU - Heo, Seonyeong
AU - Chang, Kyung Ah
AU - Lee, Hyogun
AU - Kim, Hanjun
PY - 2015/12/5
Y1 - 2015/12/5
N2 - Although mobile devices have been evolved enough to support complex mobile programs, performance of the mobile devices is lagging behind performance of servers. To bridge the performance gap, computation offloading allows a mobile device to remotely execute heavy tasks at servers. However, due to architectural differences between mobile devices and servers, most existing computation offloading systems rely on virtual machines, so they cannot offload native applications. Some offloading systems can offload native mobile applications, but their applicability is limited to well-analyzable simple applications. This work presents automatic cross-architecture computation offloading for general-purpose native applications with a prototype framework that is called Native Offloader. At compile-time, Native Offloader automatically finds heavy tasks without any annotation, and generates offloading-enabled native binaries with memory unification for a mobile device and a server. At run-time, Native Offloader efficiently supports seamless migration between the mobile device and the server with a unified virtual address space and communication optimization. Native Offloader automatically offloads 17 native C applications from SPEC CPU2000 and CPU2006 benchmark suites without a virtual machine, and achieves a geomean program speedup of 6.42× and battery saving of 82.0%.
AB - Although mobile devices have been evolved enough to support complex mobile programs, performance of the mobile devices is lagging behind performance of servers. To bridge the performance gap, computation offloading allows a mobile device to remotely execute heavy tasks at servers. However, due to architectural differences between mobile devices and servers, most existing computation offloading systems rely on virtual machines, so they cannot offload native applications. Some offloading systems can offload native mobile applications, but their applicability is limited to well-analyzable simple applications. This work presents automatic cross-architecture computation offloading for general-purpose native applications with a prototype framework that is called Native Offloader. At compile-time, Native Offloader automatically finds heavy tasks without any annotation, and generates offloading-enabled native binaries with memory unification for a mobile device and a server. At run-time, Native Offloader efficiently supports seamless migration between the mobile device and the server with a unified virtual address space and communication optimization. Native Offloader automatically offloads 17 native C applications from SPEC CPU2000 and CPU2006 benchmark suites without a virtual machine, and achieves a geomean program speedup of 6.42× and battery saving of 82.0%.
UR - http://www.scopus.com/inward/record.url?scp=84959916960&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959916960&partnerID=8YFLogxK
U2 - 10.1145/2830772.2830833
DO - 10.1145/2830772.2830833
M3 - Conference contribution
VL - 05-09-December-2015
SP - 521
EP - 532
BT - Proceedings - 48th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2015
PB - IEEE Computer Society
ER -