Two-level utilization-based processor allocation for scheduling moldable jobs

2020 ◽  
Vol 76 (12) ◽  
pp. 10212-10239
Author(s):  
Ying-Jhih Wu ◽  
Shuo-Ting Yu ◽  
Kuan-Chou Lai ◽  
Amit Chhabra ◽  
Hsi-Ya Chang ◽  
...  
Keyword(s):  
Processor Allocation

Performance-driven processor allocation

2005 ◽  
Vol 16 (7) ◽  
pp. 599-611 ◽  
Author(s):  
J. Corbalan ◽  
X. Martorell ◽  
J. Labarta
Keyword(s):  
Processor Allocation

EVALUATION OF TASK ALLOCATION IN MESH-BASED MULTICOMPUTERS USING GENETIC, NEURAL, AND TRADITIONAL ALGORITHMS

1994 ◽  
Vol 03 (01) ◽  
pp. 47-60
Author(s):  
R.A. McCONNELL ◽  
B.L. MENEZES
Keyword(s):  
Task Allocation ◽  
Bin Packing ◽  
Packing Problem ◽  
Allocation Problem ◽  
Hopfield Network ◽  
Processor Allocation ◽  
Classical Level ◽  
Bin Packing Problem ◽  
Residual Space ◽  
Computer Tasks

This article compares three techniques for allocating tasks in a mesh-based multi-computer. Tasks are expressed as rectangles of a certain width and height corresponding to the topology of processors desired. The task allocation problem, is thus a variant of the bin-packing problem, with one major difference: in the bin-packing problem one seeks to minimize the height of the bin, while here we seek to maximize the utilization of processors in a multicomputer. The three techniques compared are a classical level-by-level algorithm, a connectionist simulated annealing variant of the Hopfield network, and a genetic algorithm. An extension to the dynamic processor allocation problem is modeled by fixing some rectangles in place and packing the request rectangles in the residual space on the mesh; this corresponds to a pre-existing condition, i.e., some tasks have already been allocated to the Processor Mesh. Implementation and experimental results are presented.

scholarly journals Location of Processor Allocator and Job Scheduler and Its Impact on CMP Performance

2012 ◽  
Vol 58 (1) ◽  
pp. 9-14 ◽  
Author(s):  
Dawid Zydek ◽  
Grzegorz Chmaj ◽  
Alaa Shawky ◽  
Henry Selvaraj
Keyword(s):  
High Performance ◽  
Chip Multiprocessors ◽  
Energy Savings ◽  
Processor Allocation ◽  
Processing Elements ◽  
Energy Models ◽  
Allocation Process ◽  
On Chip ◽  
Job Scheduler ◽  
Performance Computing

Location of Processor Allocator and Job Scheduler and Its Impact on CMP PerformanceHigh Performance Computing (HPC) architectures are being developed continually with an aim of achieving exascale capability by 2020. Processors that are being developed and used as nodes in HPC systems are Chip Multiprocessors (CMPs) with a number of cores. In this paper, we continue our effort towards a better processor allocation process. The Processor Allocator (PA) and Job Scheduler (JS) proposed and implemented in our previous works are explored in the context of its best location on the chip. We propose a system, where all locations on a chip can be analyzed, considering energy used by Network-on-Chip (NoC), PA and JS, and processing elements. We present energy models for the researched CMP components, mathematical model of the system, and experimentation system. Based on experimental results, proper placement of PA and JS on a chip can provide up to 45% NoC energy savings.

Noncontiguous processor allocation algorithms for mesh-connected multicomputers

1997 ◽  
Vol 8 (7) ◽  
pp. 712-726 ◽  
Author(s):  
V. Lo ◽  
K.J. Windisch ◽  
Wanqian Liu ◽  
B. Nitzberg
Keyword(s):  
Processor Allocation ◽  
Allocation Algorithms

Communication-Aware Processor Allocation for Supercomputers: Finding Point Sets of Small Average Distance

Algorithmica ◽  
2007 ◽  
Vol 50 (2) ◽  
pp. 279-298 ◽  
Author(s):  
Michael A. Bender ◽  
David P. Bunde ◽  
Erik D. Demaine ◽  
Sándor P. Fekete ◽  
Vitus J. Leung ◽  
...  
Keyword(s):  
Average Distance ◽  
Processor Allocation ◽  
Point Sets ◽  
Finding Point

scholarly journals Optimization of processor allocation for domain decomposed Monte Carlo calculations

2019 ◽  
Vol 87 ◽  
pp. 77-86 ◽  
Author(s):  
J. Austin Ellis ◽  
Thomas M. Evans ◽  
Steven P. Hamilton ◽  
C.T. Kelley ◽  
Tara M. Pandya
Keyword(s):  
Monte Carlo ◽  
Processor Allocation ◽  
Monte Carlo Calculations
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