
FOLLOWUS
School of Computer Science and Engineering, Seoul National University, Seoul 151-744, Korea
[ "Eunsung KIM, E-mail: eskim@dcslab.snu.ac.kr" ]
Hyeonsang EOM, E-mail: hseom@cse.snu.ac.kr
[ "Heon Y. YEOM, E-mail: yeom@snu.ac.kr" ]
收稿:2011-07-04,
修回:2012-;5-;4,
纸质出版:2012-06
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Asymmetry-aware load balancing for parallel applications in single-ISA multi-core systems[J]. 信息与电子工程前沿(英文), 2012,13(6):413-427.
Eunsung KIM, Hyeonsang EOM, Heon Y. YEOM. Asymmetry-aware load balancing for parallel applications in single-ISA multi-core systems[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(6): 413-427.
Asymmetry-aware load balancing for parallel applications in single-ISA multi-core systems[J]. 信息与电子工程前沿(英文), 2012,13(6):413-427. DOI: 10.1631/jzus.C1100198.
Eunsung KIM, Hyeonsang EOM, Heon Y. YEOM. Asymmetry-aware load balancing for parallel applications in single-ISA multi-core systems[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(6): 413-427. DOI: 10.1631/jzus.C1100198.
Contemporary operating systems for single-ISA (instruction set architecture) multi-core systems attempt to distribute tasks equally among all the CPUs. This approach works relatively well when there is no difference in CPU capability. However
there are cases in which CPU capability differs from one another. For instance
static capability asymmetry results from the advent of new asymmetric hardware
and dynamic capability asymmetry comes from the operating system (OS) outside noise caused from networking or I/O handling. These asymmetries can make it hard for the OS scheduler to evenly distribute the tasks
resulting in less efficient load balancing. In this paper
we propose a user-level load balancer for parallel applications
called the 'capability balancer'
which recognizes the difference of CPU capability and makes subtasks share the entire CPU capability fairly. The balancer can coexist with the existing kernel-level load balancer without detrimenting the behavior of the kernel balancer. The capability balancer can fairly distribute CPU capability to tasks with very little overhead. For real workloads like the NAS Parallel Benchmark (NPB)
we have accomplished speedups of up to 9.8% and 8.5% in dynamic and static asymmetries
respectively. We have also experienced speedups of 13.3% for dynamic asymmetry and 24.1% for static asymmetry in a competitive environment. The impacts of our task selection policies
FIFO (first in
first out) and cache
were compared. The use of the cache policy led to a speedup of 5.3% in overall execution time and a decrease of 4.7% in the overall cache miss count
compared with the FIFO policy
which is used by default.
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