A.R. Alameldeen, M.M.K. Martin, C.J. Mauer,
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A.R. Alameldeen, M.M.K. Martin, C.J. Mauer, K.E. Moore, M. Xu, D.J. Sorin, M.D. Hill, D.A. Wood Presented By: Derek Hower. Simulating a $2M Commercial Server on a $2K PC. Contributions. Develop a cost and time efficient simulation methodology for multiprocessor systems.

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A.R. Alameldeen, M.M.K. Martin, C.J. Mauer,

K.E. Moore, M. Xu, D.J. Sorin, M.D. Hill,

D.A. Wood

Presented By:

Derek Hower

Simulating a $2M Commercial Server on a $2K PC


Contributions
Contributions

  • Develop a cost and time efficient simulation methodology for multiprocessor systems.

    • Tuned and scaled benchmarks

    • Dealing with variability

    • Extended timing simulator


Workload tweaking
Workload Tweaking

  • Wisconsin Commercial Workload Suite

    • OLTP – On-Line Transaction Processing

    • SPECjbb – Java Middleware

    • Apache – Static Web Server

    • Slashcode – Dynamic Web Server

  • Scaled to reduce memory and disk usage

  • Tuned on an actual multiprocessor server to discover bottlenecks


Case study oltp
Case Study: OLTP

  • Based on TPC-C v3.0, using IBM DB2 V7.2 EEE

  • Scaled to 3 sales districts per warehouse, 30 customers per district, and 100 items per warehouse

    • Compared to 10, 30,000 and 100,000 required by TPC

  • Set up on a Sun E5000

    • Disk images were moved to simulator


Case study oltp cont
Case Study: OLTP cont

  • Initial Scaling -

    • Reduced entire simulation to fit in 1GB of memory (10 100MB warehouses)

  • Kernel/device tuning

    • Changed limits on semaphore usage, threads, locks, etc

    • Database separated from kernel and spread out over 5 physical disks

  • Reducing contention

    • increased # of warehouses, keeping db size constant


Case study oltp cont1
Case Study: OLTP cont

  • Additional Concurrency

    • Added more users


Simulation
Simulation

  • Shorten simulations as much as possible while still maintaining accuracy

    • Start with warm workloads using snapshots

    • Fixed simulation length based on # of transactions

    • Account for variability by introducing random memory access delays and by averaging multiple simulation runs


Timing
Timing

  • Added proc and memory timing models to Simics

    • Timing-first simulation

  • Memory model:

    • cache coherence

    • cache latencies and bandwidth

    • memory

    • interconnection network


Evaluation
Evaluation

  • Simulated system using Bandwidth Adaptive Snooping Hybrid (BASH)


Thoughts
Thoughts

  • Validation

    • Mentioned briefly but skirted the issue

    • Can we trust the data?

  • Is there a loss of generality when scaling and tuning workloads?


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