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Computer Architecture Support for Database Applications. Erhan Erdinç Pehlivan. Outline. Introduction Methodology of the Experiment Analysis of OLTP workloads Analysis of DSS workloads Conclusion. Introduction.

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outline
Outline
  • Introduction
  • Methodology of the Experiment
  • Analysis of OLTP workloads
  • Analysis of DSS workloads
  • Conclusion
introduction
Introduction
  • Today Database workloads alone motivatethe sale of vast quantities of symmetric multiprocessor (SMP) machines,
introduction1
Introduction
  • Unfortunately, due to some challenges, commercial applications areoften ignored in preference to technical benchmarks, such as SPEC(Standard Performance Evaluation Corporation)
  • Reasons
    • Complex standardized benchmarks.
    • Large hardware requirements for full scale.
    • Numerous configuration parameters.
    • Lack of useful proprietary information.
what is smp
What is SMP
  • method of work management that treats all processors equally
  • threads that can run concurrently on any available processor
  • improves the total throughput of the system
  • requires applications that can take advantage of multi-threaded parallelism
smp continued
SMP(Continued)
  • Advantages of SMP
    • High performance
    • Simplicity to program
    • Easier load balancing
  • Disadvantages of SMP
    • Low availability
    • Low scalability
database workloads
Database Workloads
  • OLTP(Online transaction processing)
    • Ex : Airline reservation systems
  • DSS(Decision Support Systems)
    • Ex: Datawarehouse systems
characteristics of oltp and dss
Characteristics of OLTP and DSS
  • OLTP
    • uses short, moderately complex queriesthat read and/or modify a relatively small portion of the overall database.
    • have a high degree of multiprogramming,
  • DSS
    • typically long-running, moderately to very complex queries, that scan large portions of the database in a read-mostly fashion.
    • The multiprogramming level in DSS systems is typically

much lower than that of OLTP systems.

motivation
Motivation
  • Since SPEC evaluations don’t hold for DBMS, architectural behavior of two standard database workloads will be investigated in terms of
      • cycles per instruction (CPI) decomposition,
      • cache miss rates,
      • branch behavior.
      • superscalarness,
      • out-of-order execution
methodology experimental platform
Methodology : Experimental Platform
  • a commodity four-processor Intel-based SMP server running Windows NT is chosen.
software architecture oltp
Software Architecture(OLTP)
  • Transaction ProcessingCouncil’s TPC-C benchmark
software architecture dss
Software Architecture(DSS)
  • Transaction Processing Council’s TPC-D benchmark
      • the activity of a wholesale supplier in doing complex businessanalysis.
      • analysis: pricing and promotions, market share study,shipping management, supply and Demandmanagement, profit and revenue management and customer satisfactionstudy.
      • 17 read-only queries and 2 update queries,
potential sources of stalls
Potential sources of stalls
  • misses to the L1 instruction cache
  • a branch misprediction
  • the instruction mix of the workload
  • the out-of-order execution engine
measurement methodology
Measurement Methodology
  • NT performance monitor
  • Pentium Pro hardwarecounters.
  • Intel tool called emon
analysis of oltp workloads
Analysis of OLTP Workloads
  • OLTP does short, moderatelycomplex transactions
  • small, random I/O operations
  • large number of concurrent users,a high degree of multiprogramming.
  • database implements locking,logging
  • The combination of these tasks :
      • Large instruction working set
      • Larger data footprint
experimental results memory system behavior
Experimental Results: Memory System Behavior
  • How do OLTP cache miss rates vary with L2 cache size?
experimental results memory system
Experimental Results: Memory System
  • What effects do larger caches have on OLTP throughput and stall cycles?
slide26

Experimental Results: Processor Issues

How useful is superscalar issue and retire for OLTP?

experimental results processor issues
Experimental Results: Processor Issues
  • How effective is branch prediction for OLTP?
experimental results processor issues1
Experimental Results: Processor Issues
  • Is out-of-order execution successful at hiding stalls for OLTP?
experimental results multiprocessor scaling issues
Experimental Results: Multiprocessor Scaling Issues
  • How well does OLTP performance scale as the number of processors increases?
experimental results multiprocessor scaling issues1
Experimental Results: Multiprocessor Scaling Issues
  • How do OLTP CPI components change as the number of processors is scaled?
experimental results multiprocessor scaling issues2
Experimental Results: Multiprocessor Scaling Issues
  • How prevalent are cache misses to dirty data in other processors’ caches for OLTP?
experimental results multiprocessor scaling issues3
Experimental Results: Multiprocessor Scaling Issues
  • Is the four-state (MESI) invalidation-based cache coherence protocol worthwhile for OLTP?
experimental results multiprocessor scaling issues4
Experimental Results: Multiprocessor Scaling Issues
  • How does OLTP memory system performance scale with increasing cachesizes and increasing processor count?
analysis of decision support workloads
Analysis of Decision SupportWorkloads
  • DSS queries are typically long-running, moderately to very complex queries,
  • Scan large portions of the database in a read-mostly fashion.
  • Largesequential disk I/O read operations.
  • The multiprogramming level in DSS systems is typically lower than thatof OLTP systems.
slide36

Experimental Results:Memory System Behaviour

  • How do DSS cache miss rates vary with L2 cache size?
experimental results memory system behaviour
Experimental Results:Memory System Behaviour
  • What impact do larger L2 caches have on DSS database performance and stallcycles?
experimental results memory system behaviour1
Experimental Results:Memory System Behaviour
  • How prevalent are cache misses to dirty data in other processors’ caches inDSS?
experimental results memory system behaviour2
Experimental Results:Memory System Behaviour
  • Is the four-state (MESI) invalidation-based cache coherence protocolworthwhile for DSS?
experimental results memory system behaviour3
Experimental Results:Memory System Behaviour
  • How does DSS memory system performance scale with increasing cache sizes?
experimental results processor issues2
Experimental Results: Processor Issues
  • How useful is superscalar issue and retire for DSS?

BEHAVES LIKE OLTP

experimental results processor issues3
Experimental Results: Processor Issues
  • How effective is branch prediction for DSS?
experimental results processor issues4
Experimental Results: Processor Issues
  • Is out-of-order execution successful at hiding stalls for DSS?
conclusions for oltp
Conclusions for OLTP
  • out-of-order execution is only somewhat effective for this database workload.
  • increased superscalar width for the out-of-order engine may be helpful.
  • Innovation needed in branch prediction algorithms and hardware structures to better support database workloads.
  • caches are effective at reducing the processor traffic to memory
  • Three-state (MSI) cache coherence protocol would be better
  • the amount of time when the memory system is unavailable decreases with larger caches, increases with # of processors
conclusions for dss
Conclusions for DSS
  • out-of-order execution provides potentially more benefit for DSS than OLTP
  • DSS performance is less sensitive to L2cache size than OLTP performance.
  • Existing branch prediction schemes aremore effective for this workload.
  • Increasing the micro-operation retire width in the Pentium Pro’s out-of-order RISC core may provide performance improvements
  • Dirty misses are less prevalent for DSS than OLTP.