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Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Ricardo Portillo, Diana Villa, Patricia J. Teller The University of.

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Presentation on theme: "Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Ricardo Portillo, Diana Villa, Patricia J. Teller The University of."— Presentation transcript:

1 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Ricardo Portillo, Diana Villa, Patricia J. Teller The University of Texas at El Paso Department of Computer Science Bret Olszewski IBM Corporation – Austin, TX Mining Performance Data from Sampled Event Traces

2 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Outline  Motivation  Data Collection Environment Workload & Platform Monitored Events  Sampled Event Traces  Data Analysis & Results  Conclusions & Future Work

3 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Motivation  Capturing event traces System simulation: High overhead Real-time measurement: Capture information about every event  Problem Unmanageable size of full event traces  Goal Use sampled event traces to analyze application behavior

4 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Data Collection Environment  Workload TPC-C benchmark  Commercial, OLTP application  Oracle  Platform IBM eServer pSeries 690 architecture (p690)  8- and 32-processor configurations

5 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 P X XP XP L2 L3 MCM 0 P X XP XP P L2 L3 MCM 1 X XP L2 Platform 8-processor p690 configuration

6 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Platform 32-processor p690 configuration P P PP PP P L2 L3 MCM 0 P P P PP PP P L2 L3 MCM 2 P P P PP PP P L2 L3 MCM 1 P P P PP PP P L2 L3 MCM 3 P

7 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Monitored Events  L2-Cache Data Load Misses L2.5 L2.75 L3 L3.5 MEM  L1-Cache Data Load Misses L2

8 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 P X XP XP L2 L3 MCM 0 P X XP XP P L2 L3 MCM 1 X XP L2 Load Latencies L2 12 cycles L2

9 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 P X XP XP L2 L3 MCM 0 P X XP XP P L2 L3 MCM 1 X XP L2 Load Latencies L2 12 cycles L2.5 73 cycles L2.5

10 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 P X XP XP L2 L3 MCM 0 P X XP XP P L2 L3 MCM 1 X XP L2 Load Latencies L2 12 cycles L2.5 73 cycles L2.75 96 cycles L2.75

11 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 P X XP XP L2 L3 MCM 0 P X XP XP P L2 L3 MCM 1 X XP L2 Load Latencies L2 12 cycles L2.5 73 cycles L2.75 96 cycles L3112 cycles L3

12 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 P X XP XP L2 L3 MCM 0 P X XP XP P L2 L3 MCM 1 X XP L2 Load Latencies L2 12 cycles L2.5 73 cycles L2.75 96 cycles L3112 cycles L3.5143 cycles L3.5

13 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 P X XP XP L2 L3 MCM 0 P X XP XP P L2 L3 MCM 1 X XP L2 Load Latencies L2 12 cycles L2.5 73 cycles L2.75 96 cycles L3112 cycles L3.5143 cycles MEM320 cycles

14 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Data Collection  10-minute observation interval  Performance Monitoring Unit (PMU) Special-purpose registers Programming interface Kernel extension  eprof PMU configuration Event-based sampling

15 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Sampled Event Traces  Sampling Record periodic occurrences of an event 100 events/sec/CPU  Event record 372872 184469 0.328104637 000000000000A8C4 0000000000218880 PIDTIDTimestamp Effective Instruction Address Effective Data Address  Average number of samples collected/event 238,448 for 8-processor data 212,396 for 32-processor data

16 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Performance Framework Database Load DB Java Tool p690TPC-C Data Collection Environment Reports 5 BufferPool 56893 29384 6 Data,BSS,Heap 8799 4855 1 Kernel 23485 9840 Sampled Event Traces Report Generation Java Tool Graphs PID TID Timestamp Instr.Addr. DataAddr.

17 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Data Analysis & Results  Locality of reference at high-penalty resolution sites  Characterization of differences between shared and private data loads  Cost of process migration  False sharing

18 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Data Analysis & Results Goal 1: Identify sources of application performance degradation Identify concentrated areas of locality of reference at high-penalty miss resolution sites

19 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Data Analysis & Results Goal 1: Identify sources of application performance degradation Identify concentrated areas of locality of reference at high-penalty miss resolution sites

20 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Data Analysis & Results Goal 1: Identify sources of application performance degradation Identify concentrated areas of locality of reference at high-penalty miss resolution sites

21 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Goal 2: Study effectiveness of design and policies associated with p690 memory hierarchy w.r.t workload demands Characterize behavioral difference between private and shared data loads Data Analysis & Results Private Distribution of Data Load Hits: Data,BSS,Heap Shared Distribution of Data Load Hits: Buffer Pool

22 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Goal 2: Study effectiveness of design and policies associated with p690 memory hierarchy w.r.t workload demands Data Analysis & Results Private Distribution of Data Load Hits: Data,BSS,Heap Shared Distribution of Data Load Hits: Buffer Pool Good Application/Architecture Match

23 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Goal 2: Study effectiveness of design and policies associated with p690 memory hierarchy w.r.t workload demands Data Analysis & Results Private Distribution of Data Load Hits: Data,BSS,Heap Shared Distribution of Data Load Hits: Buffer Pool Possible Performance Impediment

24 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Goal 2: Study effectiveness of design and policies associated with p690 memory hierarchy w.r.t workload demands Data Analysis & Results Private Distribution of Data Load Hits: Data,BSS,Heap Shared Distribution of Data Load Hits: Buffer Pool MEM Data Load Hits Primarily Due To Compulsory Misses

25 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Data Analysis & Results Goal 3: Study “cost” of intra-MCM migrations Intra-MCM process migration overhead in terms of L2.5 data load hit events

26 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Conclusions - 1 Targets for performance improvement of TPC-C are associated mainly with two regions of the address space: buffer pool data, bss, heap References for private data Satisfied within the MCM Good application/architecture match References for shared data Referenced outside the MCM Target for performance improvement

27 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Conclusions - 2 Main memory accesses primarily associated with compulsory misses Intra-MCM process migration not a possible source of performance degradation Model of TPC-C memory access may be possible Similar reference patterns observed:  8- and 32- processor TPC-C data  8-processor TPC-C/Oracle and TPC-C/Sybase data

28 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Future Work  Suggest ways to improve p690 application performance  Quantify representativeness of sampled event traces  Expand study of application data load behavior e.g., process characterization  Develop synthetic applications Mimic the behavior of key p690 applications  Use these to study application behavior  Experiment with modifications that may affect performance  Enhance performance evaluation framework Virtualization Study performance issues related to POWER5 virtualization

29 Department of Computer Science 6 th Annual Austin CAS Conference – 24 February 2005 Thank You. Questions?

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