1. Scheduling of Reducing Cache Pollution in Multicore Department of Embedded software, Korea university 2011610012 Juho Lim

2. Problem Multicore Shared memory, shared cache Contention Need to efficiently scheduling method in multicore Performance down + increase energy consumption

3. Problem of Existing scheduling way Regardless of task and reduce cache contention Cache miss, cache pollution in Multicore Division- I/O task, general purpose task for performance improvement

4. Animal Classification Turtle- simply do not make much use of shared last –level cache Sheep- task that low -cache access and cache miss rate Rabbits: low cache miss rate but frequently cache access tasks Devils: high cache miss rate and frequently cache access tasks. How much effect to each core when scheduling with core pair which shared cache

5. Animal Classification

6. Resource-conscious scheduling Task Activity Vector Task1: Frequently use resource Task2: rarely use resource Task Activity Vector divide Task1 and Task2 for simultaneous run.

7. Resource-conscious scheduling Task1: Frequently use resource Task2: rarely use resource Run Queue Core Division two task type by degree how use resource Create Two run queue Insert two type task into run queue According to Epoch, two run queue put the tasks to core by turns This technique reduce resource contention so cores use fair resource

8. Resource-conscious scheduling

9. Devil Classification Division – Devil area, Normal area Devil- Disk I/O intensive tasks Normal- other general tasks IF Disk I/O > shared cache -> almost cache space became pollution state -> this make difficult to use of cache So To find devil task is important!

10. Devil Classification Count I/O disk system call! -frequent? Devil -normal? Normal So we classified scheduling task! Division! Devil or Normal

11. Devil Classification Core2 Duo T9400 processor architecture L2 cache state when run merge sort at core0 L2 cache state when run disk I/O at core1 L2 cache state when simultaneous execute each core

12. Test & Estimation Cache miss rate(%)

13. Test & Estimation Runtime (ms)

14. Conclusion Papers propose division tasks according to degree how use resource So Task division techniques are proposed Animal Classification Resource-conscious scheduling Devil Classification For Effective Usage of cache, divide I/O intensive cache And Schedule using divided task We confirmed performance improvement

15. Reference [1] Deukhyon An, Junghan Kim, and Young Ik Eom, “Scheduling of Reducing Cache Pollution with I/O Tasks in Multicore Environment”, 2011. [2] Sergey Zhuravlev, Sergey Blagodurov, Alexandra Fedorova, “Addressing Shared Resource Contention in Multicore Processors via Scheduling, In ASPLOS, p.129-142, 2010. [3] Yuejian Xie and Fabriel H.Loh, “Dynamic Classification of Program Memory Behaviors in CMPs”, In Proc. Of CMP-MSI, held in conjunction with ISCA- 35,2008 [4] Andreas Merkel, Jan Stoess, Frank Bellosa, “Resource-conscious Scheduling for EnergyEfficiency on Multicore Processors”, EuroSys, p.153-166, 2010