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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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Presentation Transcript
slide1
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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