Hyunjoon Park

dblp:173/9794 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 50% Cloud and datacenter computing · 50%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
computation offloading
0.212015
Architecture-aware automatic computation offload for native applications · MICRO 2015
Embedded and real-time systems
mobile computing
0.212015
Architecture-aware automatic computation offload for native applications · MICRO 2015
Compilers and program optimization
compiler analysis
0.112015
Architecture-aware automatic computation offload for native applications · MICRO 2015

Methods — techniques the papers use, named apart from their topics

unified virtual address space · 0.4memory unification · 0.4communication optimization · 0.4
YearPublicationVenuePosition
2026 Consistent Scene Understanding in 3D Gaussian Splatting via Multi-cue Mask Refinement
Hyunjoon Park, Donghyeon Cho
ICPR (9)1
2015 Architecture-aware automatic computation offload for native applications
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%.
Gwangmu Lee, Hyunjoon Park, Seonyeong Heo, Kyung-Ah Chang, Hyogun Lee, Hanjun Kim 0001
MICRO2