1. 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

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 - Kernel/device tuning
Reduced entire simulation to fit in 1GB of memory ( MB 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 Memory model:
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?