1 / 10

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

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.

annis
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Contributions • Develop a cost and time efficient simulation methodology for multiprocessor systems. • Tuned and scaled benchmarks • Dealing with variability • Extended timing simulator

  3. 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

  4. 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

  5. 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

  6. Case Study: OLTP cont • Additional Concurrency • Added more users

  7. 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

  8. Timing • Added proc and memory timing models to Simics • Timing-first simulation • Memory model: • cache coherence • cache latencies and bandwidth • memory • interconnection network

  9. Evaluation • Simulated system using Bandwidth Adaptive Snooping Hybrid (BASH)

  10. Thoughts • Validation • Mentioned briefly but skirted the issue • Can we trust the data? • Is there a loss of generality when scaling and tuning workloads?

More Related