EECS 252 Graduate Computer ArchitectureLec 3 – Performance + Pipeline Review David PattersonElectrical Engineering and Computer SciencesUniversity of California, Berkeleyhttp://www.eecs.berkeley.edu/~pattrsnhttp://www-inst.eecs.berkeley.edu/~cs2521/25/2006 CS252-s06, Lec 02-intro2Review from last lecture• Tracking and extrapolating technology part of architect’s responsibility• Expect Bandwidth in disks, DRAM, network, and processors to improve by at least as much as the square of the improvement in Latency• Quantify Cost (vs. Price)– IC ≈ f(Area2) + Learning curve, volume, commodity, margins• Quantify dynamic and static power– Capacitance x Voltage2x frequency, Energy vs. power• Quantify dependability– Reliability (MTTF vs. FIT), Availability (MTTF/(MTTF+MTTR)1/25/2006 CS252-s06, Lec 02-intro3Outline• Review• Quantify and summarize performance– Ratios, Geometric Mean, Multiplicative Standard Deviation• F&P: Benchmarks age, disks fail,1 point fail danger• 252 Administrivia• MIPS – An ISA for Pipelining• 5 stage pipelining• Structural and Data Hazards• Forwarding• Branch Schemes• Exceptions and Interrupts• Conclusion 1/25/2006 CS252-s06, Lec 02-intro4Performance(X) Execution_time(Y)n = =Performance(Y) Execution_time(X)Definition: Performance• Performance is in units of things per sec– bigger is better• If we are primarily concerned with response timeperformance(x) = 1 execution_time(x)" X is n times faster than Y" means1/25/2006 CS252-s06, Lec 02-intro5Performance: What to measure• Usually rely on benchmarks vs. real workloads• To increase predictability, collections of benchmark applications-- benchmark suites -- are popular• SPECCPU: popular desktop benchmark suite– CPU only, split between integer and floating point programs– SPECint2000 has 12 integer, SPECfp2000 has 14 integer pgms– SPECCPU2006 to be announced Spring 2006– SPECSFS (NFS file server) and SPECWeb (WebServer) added as server benchmarks• Transaction Processing Council measures server performance and cost-performance for databases– TPC-C Complex query for Online Transaction Processing– TPC-H models ad hoc decision support– TPC-W a transactional web benchmark– TPC-App application server and web services benchmark1/25/2006 CS252-s06, Lec 02-intro6How Summarize Suite Performance (1/5)• Arithmetic average of execution time of all pgms?– But they vary by 4X in speed, so some would be more important than others in arithmetic average• Could add a weights per program, but how pick weight? – Different companies want different weights for their products• SPECRatio: Normalize execution times to reference computer, yielding a ratio proportional to performance =time on reference computer time on computer being rated1/25/2006 CS252-s06, Lec 02-intro7How Summarize Suite Performance (2/5)• If program SPECRatio on Computer A is 1.25 times bigger than Computer B, thenBAABBreferenceAreferenceBAePerformancePerformancimeExecutionTimeExecutionTimeExecutionTimeExecutionTimeExecutionTimeExecutionTSPECRatioSPECRatio====25.1• Note that when comparing 2 computers as a ratio, execution times on the reference computer drop out, so choice of reference computer is irrelevant 1/25/2006 CS252-s06, Lec 02-intro8How Summarize Suite Performance (3/5)• Since ratios, proper mean is geometric mean (SPECRatio unitless, so arithmetic mean meaningless)nniiSPECRatioeanGeometricM∏==1• 2 points make geometric mean of ratios attractive to summarize performance:1. Geometric mean of the ratios is the same as the ratio of the geometric means2. Ratio of geometric means = Geometric mean of performance ratios ⇒ choice of reference computer is irrelevant!1/25/2006 CS252-s06, Lec 02-intro9How Summarize Suite Performance (4/5)• Does a single mean well summarize performance of programs in benchmark suite?• Can decide if mean a good predictor by characterizing variability of distribution using standard deviation• Like geometric mean, geometric standard deviation is multiplicative rather than arithmetic• Can simply take the logarithm of SPECRatios, compute the standard mean and standard deviation, and then take the exponent to convert back:()()()()iniiSPECRatioStDevtDevGeometricSSPECRationeanGeometricMlnexpln1exp1=⎟⎠⎞⎜⎝⎛×=∑=1/25/2006 CS252-s06, Lec 02-intro10How Summarize Suite Performance (5/5)• Standard deviation is more informative if know distribution has a standard form– bell-shaped normal distribution, whose data are symmetric around mean – lognormal distribution, where logarithms of data--not data itself--are normally distributed (symmetric) on a logarithmic scale• For a lognormal distribution, we expect that 68% of samples fall in range 95% of samples fall in range • Note: Excel provides functions EXP(), LN(), and STDEV() that make calculating geometric mean and multiplicative standard deviation easy[]gstdevmeangstdevmean ×,/[]22,/ gstdevmeangstdevmean ×1/25/2006 CS252-s06, Lec 02-intro1102000400060008000100001200014000wupwiseswimmgridapplumesagalgelartequakefacerecammplucasfma3dsixtrackapsiSPECfpRatio137253622712GM = 2712GStDev = 1.98Example Standard Deviation (1/2)• GM and multiplicative StDev of SPECfp2000 for Itanium 2Outside 1 StDev1/25/2006 CS252-s06, Lec 02-intro12Example Standard Deviation (2/2)• GM and multiplicative StDev of SPECfp2000 for AMD Athlon02000400060008000100001200014000wupwiseswimmgridapplumesagalgelartequakefacerecammplucasfma3dsixtrackapsiSPECfpRatio149429112086GM = 2086GStDev = 1.40Outside 1 StDev1/25/2006 CS252-s06, Lec 02-intro13Comments on Itanium 2 and Athlon• Standard deviation of 1.98 for Itanium 2 is much higher-- vs. 1.40--so results will differ more widely from the mean, and therefore are likely less predictable• SPECRatios falling within one standard deviation: – 10 of 14 benchmarks (71%) for Itanium 2– 11 of 14 benchmarks (78%) for Athlon• Thus, results are quite compatible with a lognormal distribution (expect 68% for 1 StDev)1/25/2006 CS252-s06, Lec 02-intro14Fallacies and Pitfalls (1/2)• Fallacies - commonly held misconceptions– When discussing a fallacy, we try to give a counterexample. • Pitfalls - easily made mistakes. – Often generalizations of principles true in limited context– Show Fallacies and Pitfalls to help you avoid these errors• Fallacy: Benchmarks remain valid indefinitely– Once a benchmark becomes popular,
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