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CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
CSTalks-Polymorphic heterogeneous multicore systems-17Aug
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CSTalks-Polymorphic heterogeneous multicore systems-17Aug

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  • 1. blog.nus.edu.sg/cstalks
  • 2. Polymorphic Heterogeneous Multi-Core Systems Mihai Pricopi CSTalks August 17, 2011
  • 3. Motivation Single-core performance (complexity) increaseMihai Pricopi CSTalks 3
  • 4. Motivation Instruction-level parallelism (ILP) I 2 1: e = a + b 2: f = c + d 3: g = e * f 4: h = f * 2 3 4Mihai Pricopi CSTalks 4
  • 5. Motivation 2006 2007Mihai Pricopi CSTalks 5
  • 6. Motivation Thread-level parallelism (TLP) Multi-threaded applications Multi-programmed jobs Process Process0 Process1 P0 P1 P0 P1Mihai Pricopi CSTalks 6
  • 7. Motivation nVidia Tesla many-core: up to 960 simple and identical cores. Massively exploiting the TLP. Sequential programs suffer from limited ILP exploitation. A gap between TLP and ILP. Solution: heterogeneous systems to accommodate the gap between TLP and ILP.Mihai Pricopi CSTalks 7
  • 8. Heterogeneous Chip Multi-processors Multi-core systems that use cores with different performance parameters. Existing results show that heterogeneous systems are more efficient than homogeneous ones in terms of performance, power, area and delay. Heterogeneity can be reached by using: ◦ Asymmetric chip multi-processors (ACMPs) ◦ Multiprocessor system-on-chip (MPSoC) ◦ Architectures that dynamically reconfigure the internal structure in order to adapt to different software requests (polymorphic)Mihai Pricopi CSTalks 8
  • 9. Heterogeneous Chip Multi-processors Asymmetric chip multi-processors (ACMPs) P0 P0 P1 P4 P1 P3 P2 P3 P2Mihai Pricopi CSTalks 9
  • 10. Heterogeneous Chip Multi-processors Multiprocessor system-on-chip (MPSoC) ARM memory controller bridges DSP video acceleratorMihai Pricopi CSTalks 10
  • 11. Program Phase Behavior - gzipMihai Pricopi CSTalks 11
  • 12. Program Phase Behavior - gccMihai Pricopi CSTalks 12
  • 13. Polymorphic Heterogeneous Multi-CoreSystems • General propose applications • Novel architecture that can be tailored according to the software requirements • Base system: homogeneous P0 P1 P2 P3 processor RF • Reconfigurable capabilities • Internal structure P4 P5 P6 P7 adaptation • Core-coalition P8 P9 P10 P11 • Memory P12 P13 P14 RF P15Mihai Pricopi CSTalks 13
  • 14. Polymorphic Heterogeneous Multi-CoreSystems – Reconfigurable Fabric • Reconfigurable hardware shared by different processors • RF implements custom instructions • Dynamic reconfiguration at runtime – speedup I 2 1: e = a + b P0 2: f = c + d RF 3: g = e * f P1 4: h = f * 2 3 4 Custom InstructionMihai Pricopi CSTalks 14
  • 15. Polymorphic Heterogeneous Multi-CoreSystems – Reconfigurable Fabric • Challenging Problems: • The amount of RF is limited. • Decide when to reconfigure the RF (scheduling) • What is the best set of Custom Instructions that will give the highest speedup. • Overhead of the dynamic reconfiguration.Mihai Pricopi CSTalks 15
  • 16. Polymorphic Heterogeneous Multi-CoreSystems – Core Structure Adaptation • Similar performance can be achieved by using smaller processor internal units. • Instruction fetch window size, issue width, instruction window size, frequency can be dynamically changed. • Power and thermal concerns.Mihai Pricopi CSTalks 16
  • 17. Polymorphic Heterogeneous Multi-CoreSystems – Core-Coalition • Coalition helps creating “stronger” cores using the already existing light cores: • accelerates serial applications by extracting more ILP (if available). • uses limited amount of shared hardware between cores. • up to 4-core coalition can be formed. 2-core coalition P0 P1 P (2-way) (2-way) ≡ (4-way)Mihai Pricopi CSTalks 17
  • 18. Polymorphic Heterogeneous Multi-Core Systems – Core-Coalition Execution Model Time SF RF EX CM SF RF EX CM B0 B1 SF: Sentinel Instruction B0 B0 B1 fetch and global B0 renaming B3 B1 RF: Regular instruction B4 fetch, decode andB1 B2 B0 renaming B3 B4 EX: Regular instruction execution B3 B3 B1 B4 CM: Regular instruction commit B3 B4 B4CFG Core 0 Core 1 Mihai Pricopi National University of Singapore 18
  • 19. Experimental Results - SpeedupMihai Pricopi National University of Singapore 19
  • 20. Experimental Results – Load BalanceMihai Pricopi National University of Singapore 20
  • 21. Proposed directions  Next steps: ◦ Implement Coalition on FPGA. ◦ More study on the overhead and power consumption determined by the shared resources. ◦ Implement a dynamic scheduler for Coalition.Mihai Pricopi National University of Singapore 21
  • 22. ?Mihai Pricopi National University of Singapore 22
  • 23. Next Week’s TalkA Unified Framework for Recommendations inthe Social Network by Chen Wei Join us next Wednesday! Wednesday, 31 August, 2011 23

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