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The Impact of Performance Asymmetry in Multicore Architectures

UW-Madison and , Intel Corp. The Impact of Performance Asymmetry in Multicore Architectures. Saisanthosh Ravi Michael Konrad. Balakrishnan Rajwar Upton Lai. 32 nd Annual International Symposium on Computer Architecture. F. F. S. S. Performance asymmetry.

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The Impact of Performance Asymmetry in Multicore Architectures

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  1. UW-Madisonand , Intel Corp. The Impact of Performance Asymmetry in Multicore Architectures Saisanthosh Ravi Michael Konrad Balakrishnan Rajwar Upton Lai 32nd Annual International Symposium on Computer Architecture

  2. F F S S Performance asymmetry ... difference in compute power of processors • Architectural differences • Micro-architectural parameters • Other • Heat: Thermal throttling Why need asymmetry now? • CMP/ Many cores as commodity systems • Run variety of workloads • Good serial performance and high throughput • Optimal energy consumption Assume an asymmetric multicore system

  3. N procs. Same config. F F Performance S S Same/Many Runs Stable? N procs. Diff configs. Performance S S Need to utilize asymmetry. perform better Need predictable and robust performance S S Compute power Scalable? Asymmetry & MT workloads

  4. Algorithm, Correctness, Thread Partitioning The problems Programmers Don’t reason about asymmetry Characteristics of threads Partitioning, Synchronization barriers, Interference, Lifetime Scheduling of threads OS Kernel, Library, Application, DB/Web servers, Managed runtime systems (Java, .NET)

  5. F F F F F F S S F F S S S S F S F S S S Configurations all fast all slow 1 slow 2 slow 3 slow Symmetric Asymmetric F = Full frequency S = one-eighth of Full frequency (in talk and paper) S = one-fourth of Full frequency (in paper)

  6. Studying impact Scalability Stability Perf. Metric Perf. Metric Same or Many runs 2 slow 1 slow 3 slow all slow all fast (Asymm)

  7. Fix P P O P P Impact of asymmetry SPECjbb SPECjAppServer Apache Zeus TPC-H SPECOMP H.264 PMake Workloads Scalable Stable O P P P P O O O P O O O P P P P

  8. SPECjbb P O Scalable? Stable? with kernel fix • Fix: Kernel scheduler moves jobs from slow to fast if free Stability (JRockit/Gencon GC) on 2 slow 4 runs • Problem: Interference from runtime system (JVM, GC)

  9. P O Scalable? Stable? Apache Scalability & Stability (light load) • Problem:light load - threads can be on fast/slow • No issues under heavy load • Fixes:Kernel scheduler or shorter lifetime of threads

  10. O O Scalable? Stable? Zeus Scalability & Stability • Under heavy and light loads: unpredictable • Superior perf. on symmetric configs. • Problem: Aggressive application-level scheduling

  11. O O Scalable? Stable? with app. fix • Fix:Change scheduling of tasks to on-demand • Downside:Overheads SPECOMP Scalability • OpenMP schedules tasks assuming equal perf. procs. • Problem: Fast processors are held by slow

  12. P P Scalable? Stable? PMake • PMake linearly scalable on all configurations H.264 & PMake H.264 • H.264 slows down significantly with 1 slow proc. • Speeds up with 1 fast proc.

  13. Conclusions Asymmetric systems - Good for energy and performance - But can introduce unpredictability Software to understand asymmetry - Evaluate application’s work partitioning - Scheduling of tasks. Mostly no other changes. - May be, feedback based Suitable asymmetry - Many slow & few fast processors

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