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1 Presenter: Chien-Chih Chen. 2 Dynamic Scheduler for Multi-core Systems Analysis of The Linux 2.6 Kernel Scheduler Optimal Task Scheduler for Multi-core.

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Presentation on theme: "1 Presenter: Chien-Chih Chen. 2 Dynamic Scheduler for Multi-core Systems Analysis of The Linux 2.6 Kernel Scheduler Optimal Task Scheduler for Multi-core."— Presentation transcript:

1 1 Presenter: Chien-Chih Chen

2 2 Dynamic Scheduler for Multi-core Systems Analysis of The Linux 2.6 Kernel Scheduler Optimal Task Scheduler for Multi-core Processor

3 Many dynamic scheduling algorithms have been proposed in the past. With the advent of multi core processors, there is a need to schedule multiple tasks on multiple cores. The scheduling algorithm needs to utilize all the available cores efficiently. The multi-core processors may be SMPs or AMPs with shared memory architecture. In this paper, we propose a dynamic scheduling algorithm in which the scheduler resides on all cores of a multi-core processor and accesses a shared Task Data Structure (TDS) to pick up ready-to-execute tasks. 3

4 Conversion of sequential code to parallel code or writing parallel applications is not optimal solution. Most of the proposed scheduling algorithms for multi-core processors don’t support dependent task. 4

5 5 Dynamic Scheduler for Multi-core Systems Available data dependency analysis techniques [9] [10] [11] [12] [13] [14] [15] Have proposed dynamic scheduling techniques [1] [2] [3] [4] [5] [6] [7]

6 [1] An improvement OFT algorithm for reducing preemption. [2] A data flow based and discuss data reuse which is intended for numeric computation. [3] Based on recording resource utilization and throughput to change cores. [4] A compile time technique that dynamically extract dependency and schedule parallel tiles on the cores to improve scalability. 6

7 [5] Using FFT language to generate one-dimensional serial FFT schedule, multi-dimensional serial FFT schedule and parallel FFT schedules. [6] Rearranges a long task into smaller subtasks to form another task state graph and then schedule them in parallel. [7] Using sampling of dominant execution phases to converge to the optimal scheduling algorithm. 7

8 The scheduler will reside in the shared memory of the multi-core system to ensures that all the cores share the scheduler code. The same scheduler code will be executing on different cores and maintain a shared task data structure (TDS) that contains task information. 8

9 9

10 T i unique number identifying the task i T is status of task i  Ready (1)  Running (2)  Not ready (-1) T id number of dependency on task i T ia list of tasks that become available due to run task i T ip priority number of task i T idp data pointer of task i T isp stack pointer of task i T ix execution time of task i 10

11 Duration of task (Tix). Total number of other tasks dependent on the task (Tid). 11

12 T ij :Task j can be start after Task i finished T ij time  i: row numberj: column number  T 01 : T 1 will start after T 0 run 100 seconds 12

13 {T1, T2, T3} {T5} {T2, T3, T5} {T3, T4, T5} {T4, T5}

14 Attempt to increase utilization of multi-core processors. Tasks execution can not be limit in one core. Addition wait time for cores since involves accessing shared task structure through lock. 14


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