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Computation Spreading: Employing Hardware Migration to Specialize CMP Cores On-the-fly

Computation Spreading: Employing Hardware Migration to Specialize CMP Cores On-the-fly. Koushik Chakraborty Philip Wells Gurindar Sohi {kchak,pwells,sohi}@cs.wisc.edu. Paper Overview. Multiprocessor Code Reuse Poor resource utilization Computation Spreading

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Computation Spreading: Employing Hardware Migration to Specialize CMP Cores On-the-fly

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  1. Computation Spreading: Employing Hardware Migration to Specialize CMP Cores On-the-fly Koushik Chakraborty Philip Wells Gurindar Sohi {kchak,pwells,sohi}@cs.wisc.edu

  2. Paper Overview • Multiprocessor Code Reuse • Poor resource utilization • Computation Spreading • New model for assigning computation within a program on CMP cores in H/W • Case Study: OS and User computation • Investigate performance characteristics Chakraborty, Wells, and Sohi ASPLOS 2006

  3. Talk Outline • Motivation • Computation Spreading (CSP) • Case study: OS and User compution • Implementation • Results • Related Work and Summary Chakraborty, Wells, and Sohi ASPLOS 2006

  4. Homogeneous CMP • Many existing systems are homogeneous • Sun Niagara, IBM Power 5, Intel Xeon MP • Multithreaded server application • Composed of server threads • Typically each thread handles a client request • OS assigns software threads to cores • Entire computation from one thread execute on a single core (barring migration) Chakraborty, Wells, and Sohi ASPLOS 2006

  5. Code Reuse • Many client requests are similar • Similar service across multiple threads • Same code path traversed in multiple cores • Instruction footprint classification • Exclusive – single core access • Common – many cores access • Universal – all cores access Chakraborty, Wells, and Sohi ASPLOS 2006

  6. Multiprocessor Code Reuse Chakraborty, Wells, and Sohi ASPLOS 2006

  7. Implications • Lack of instruction stream specialization • Redundancy in predictive structures • Poor capacity utilization • Destructive interference • No synergy among multiple cores • Lost opportunity for co-operation Exploit core proximity in CMP Chakraborty, Wells, and Sohi ASPLOS 2006

  8. Talk Outline • Motivation • Computation Spreading (CSP) • Case study: OS and User compution • Implementation • Results • Related Work and Summary Chakraborty, Wells, and Sohi ASPLOS 2006

  9. Computation Spreading (CSP) • Computation fragment = dynamic instruction stream portion • Collocate similar computation fragments from multiple threads • Enhance constructive interference • Distribute dissimilar computation fragments from a single thread • Reduce destructive interference Reassignment is the key Chakraborty, Wells, and Sohi ASPLOS 2006

  10. Example A1 B1 C1 B2 C2 A2 C3 A3 B3 C A N O N I C A L time T1 T2 T3 A1 B1 C1 B2 C2 A2 C3 A3 B3 P1 P2 P3 A1 B2 C3 C S P A3 B1 C2 A2 B3 C1 Chakraborty, Wells, and Sohi ASPLOS 2006

  11. Key Aspects • Dynamic Specialization • Homogeneous multicore acquires specialization via retaining mutually exclusive predictive state • Data Locality • Data dependencies between different computation fragments • Careful fragment selection to avoid loss of data locality Chakraborty, Wells, and Sohi ASPLOS 2006

  12. Selecting Fragments • Server workloads characteristics • Large data and instruction footprint • Significant OS computation • User Computation and OS Computation • A natural separation • Exclusive instruction footprints • Relatively independent data footprint Chakraborty, Wells, and Sohi ASPLOS 2006

  13. Data Communication Core 1 Core 2 T2-User T1-User T2 T1 T2-OS T1-OS Chakraborty, Wells, and Sohi ASPLOS 2006

  14. Relative Inter-core Data Communication Apache OLTP OS-User Communication is limited Chakraborty, Wells, and Sohi ASPLOS 2006

  15. Talk Outline • Motivation • Computation Spreading (CSP) • Case study: OS and User compution • Implementation • Results • Related Work and Summary Chakraborty, Wells, and Sohi ASPLOS 2006

  16. Implementation • Migrating Computation • Transfer state through the memory subsystem • ~2KB of register state in SPARC V9 • Memory state through coherence • Lightweight Virtual Machine Monitor • Migrates computation as dictated by the CSP Policy • Implemented in hardware/firmware Chakraborty, Wells, and Sohi ASPLOS 2006

  17. Implementation cont Threads Software Stack Virtual CPUs OS Comp User Comp Physical Cores Baseline OS Cores User Cores Chakraborty, Wells, and Sohi ASPLOS 2006

  18. Implementation cont Threads Software Stack Virtual CPUs Physical Cores OS Cores User Cores Chakraborty, Wells, and Sohi ASPLOS 2006

  19. CSP Policy • Policy dictates computation assignment • Thread Assignment Policy (TAP) • Maintains affinity between VCPUs and physical cores • Syscall Assignment Policy (SAP) • OS computation assigned based on system calls • TAP and SAP use identical assignment for user computation Chakraborty, Wells, and Sohi ASPLOS 2006

  20. Talk Outline • Motivation • Computation Spreading (CSP) • Case study: OS and User compution • Implementation • Results • Related Work and Summary Chakraborty, Wells, and Sohi ASPLOS 2006

  21. Simulation Methodology • Virtutech SIMICS MAI running Solaris 9 • CMP system: 8 out-of-order processors • 2 wide, 8 stages, 128 entry ROB, 3GHz • 3 level memory hierarchy • Private L1 and L2 • Directory base MOSI • L3: Shared, Exclusive 8MB (16w) (75 cycle load-to-use) • Point to point ordered interconnect (25 cycle latency) • Main Memory 255 cycle load to use, 40GB/s Measure impact on predictive structures Chakraborty, Wells, and Sohi ASPLOS 2006

  22. L2 Instruction Reference Chakraborty, Wells, and Sohi ASPLOS 2006

  23. Result Summary • Branch predictors • 9-25% reduction in mis-predictions • L2 data references • 0-19% reduction in load misses • Moderate increase in store misses • Interconnect messages • Moderate reduction (after accounting extra messages for migration) Chakraborty, Wells, and Sohi ASPLOS 2006

  24. Performance Potential Migration Overhead Chakraborty, Wells, and Sohi ASPLOS 2006

  25. Talk Outline • Motivation • Computation Spreading (CSP) • Case study: OS and User compution • Implementation • Results • Related Work and Summary Chakraborty, Wells, and Sohi ASPLOS 2006

  26. Related Work • Software re-design: staged execution • Cohort Scheduling [Larus and Parkes 01], STEPS [Ailamaki 04], SEDA [Welsh 01], LARD [Pai 98] • CSP: similar execution in hardware • OS and User Interference [several] • Structural separation to avoid interference • CSP avoids interference and exploits synergy Chakraborty, Wells, and Sohi ASPLOS 2006

  27. Summary • Extensive code reuse in CMPs • 45-66% instruction blocks universally accessed in server workloads • Computation Spreading • Localize similar computation and separate dissimilar computation • Exploits core proximity in CMPs • Case Study: OS and User computation • Demonstrate substantial performance potential Chakraborty, Wells, and Sohi ASPLOS 2006

  28. Thank You! Chakraborty, Wells, and Sohi ASPLOS 2006

  29. Backup Slides Chakraborty, Wells, and Sohi ASPLOS 2006

  30. L2 Data Reference L2 load miss comparable, slight to moderate increase in L2 store miss Chakraborty, Wells, and Sohi ASPLOS 2006

  31. Multiprocessor Code Reuse Chakraborty, Wells, and Sohi ASPLOS 2006

  32. Performance Potential Chakraborty, Wells, and Sohi ASPLOS 2006

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