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Process coordinator in NUMA environment
- 1. Process Coordinator in NUMA Environment Song Chi Young stchuck@dankook.ac.kr Dankook UNIV. Jongmoo Choi choijm@dankook.ac.kr Dankook UNIV.
- 2. Contents Introduction Design Result Conclusion
- 4. What is NUMA? 1. Introduction
- 5. What is NUMA? 1. Introduction
- 6. What is NUMA? 1. Introduction 1. Core 0 is request to GQ 2. GQ finds form LLC 3. If can not find 1. Find from own DRAM 2. Request to next nodes GQ
- 7. Motivation 1. Introduction 0 50 100 150 200 250 4 4 4 streamcluster canneal facesim ① 1Processor Local ② 1Processor Interleaving ③ 2Processor Interleaving ④ 1Processor Remote
- 8. Motivation 1. Introduction 0 50 100 150 200 250 4 facesim ① 1Processor Local ② 1Processor Interleaving ③ 2Processor Interleaving ④ 1Processor Remote Why memory interleaving policy Better than local memory policy?
- 9. Design Effective Factor How to measure
- 10. Effective Factor 2. Design 0 50 100 150 200 250 0 0.1 0.2 0.3 0.4 0.5 LLC Hit Ratio 0 50 100 150 200 250 0 100000000 200000000 300000000 GQ_data_from_LLC 0 50 100 150 200 250 0 50000000 100000000 150000000 200000000 GQ_data_from_QPI 0 50 100 150 200 250 0 2E+10 4E+10 6E+10 GQ_DATA.FROM_IMC 0 50 100 150 200 250 0 5E+09 1E+10 1.5E+10 2E+10 QHL_REQUEST.REMOTE 0 50 100 150 200 250 0 10000 20000 30000 40000 50000 60000 70000 MBW
- 11. Effective Factor 2. Design 0 50 100 150 200 250 0 0.1 0.2 0.3 0.4 0.5 LLC Hit Ratio 0 50 100 150 200 250 0 100000000 200000000 300000000 GQ_data_from_LLC 0 50 100 150 200 250 0 50000000 100000000 150000000 200000000 GQ_data_from_QPI 0 50 100 150 200 250 0 2E+10 4E+10 6E+10 GQ_DATA.FROM_IMC 0 50 100 150 200 250 0 5E+09 1E+10 1.5E+10 2E+10 QHL_REQUEST.REMOTE 0 50 100 150 200 250 0 10000 20000 30000 40000 50000 60000 70000 MBW LLC, Memory Interleaving, Local/Remote
- 12. Effective Factor 2. Design Shared Data MeMory usage Working Set size Read Write NuMa
- 13. Effective Factor 2. Design Shared Data MeMory usage Working Set size Read Write NuMa (출처: BIENIA, Christian, et al. The PARSEC benchmark suite: Characterization and architectural implications. In: Proceedings of the 17th international conference on Parallel architectures and compilation techniques. ACM, 2008. p. 72-81.)
- 14. Effective Factor 2. Design Shared Data MeMory usage Working Set size Read Write NuMa (출처: BIENIA, Christian, et al. The PARSEC benchmark suite: Characterization and architectural implications. In: Proceedings of the 17th international conference on Parallel architectures and compilation techniques. ACM, 2008. p. 72-81.)
- 15. Effective Factor 2. Design Shared Data MeMory usage Working Set size Read Write NuMa PMU data A : UNC_IMC_NORMAL_READS.ANY B : UNC_IMC_WRITES.FULL.ANY Usage = A + B Read Write 비율 = A / B
- 16. How to Measure 2. Design 𝑃𝑣 𝑡 × 𝑀 × 𝑆 𝑣 ∝ 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 Pv : Performance Vector table for workloads Sv : System Performance Vector table M : Transformation matrix
- 17. How to Measure 2. Design ⓐ ⓑ SD MM WS RW NM × 0 0 0 1 1 0 0 0 1 0 0 1 1 0 0 × LC MI LR ⓐ ⓑ
- 18. How to Measure 2. Design 0 0 0 1 1 0 0 0 1 0 0 1 1 0 0 𝑀 × 𝑆 𝑣 ∝ 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒= LC MI LR SD MM WS RW NM Heuristic method If, one factor affect to the other factor Check this table, 1
- 19. Result
- 20. Performance Vector for Workloads 3. Result Canneal StreamCluster Facesim SD 1 2 1 MM 1 2 3 WS 1 0 1 RW 1 2 1 NM 2 2 2 SD: Shared data, MM : Memory usage, WS : Working Set size, RW : R/W 비율, NM : NUMA 가중치
- 21. Performance Vector for NUMA Configuration 3. Result 1 1_Processor Local 2 1_Processor interleaving 3 2_Processor interleaving 4 1_Processor Remote LC 1 1 2 1 MI 1 2 2 1 LR 2 1 1 0 LC : LLC Size, MI : Memory Interleaving, LR : Local/Remote 비율
- 22. Result 3. Result 1 2 3 4 canneal 10 8 9 6 stream 6 5 6 4 facesim 5 5 6 4 1 2 3 4 canneal 9 7 9 3 stream 12 10 12 4 facesim 13 12 14 5 실제 성능 계산된 성능 가중치
- 23. Result 3. Result 각 워크로드 별 상관계수 canneal stream cluster facesim 0.966092 0.965581 0.9 1 2 3 4 canneal 10 8 9 6 stream 6 5 6 4 facesim 5 5 6 4 1 2 3 4 canneal 9 7 9 3 stream 12 10 12 4 facesim 13 12 14 5 실제 성능 계산된 성능 가중치
- 24. Conclusion
- 25. Conclusion 4. Conclusion Why memory interleaving policy Better than local memory policy? Effective Factor : LLC, Memory Interleaving, Local/Remote SD MM WS RW NM × 0 0 0 1 1 0 0 0 1 0 0 1 1 0 0 × LC MI LR 𝑃𝑣 𝑡 × 𝑀 × 𝑆 𝑣 ∝ 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 canneal stream cluster facesim 0.966092 0.965581 0.9 pearson correlation coefficient Q :