L08 - 18-447 Lecture 8: Performance -how to summarize &...

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CMU 18-447 S’08 L8-1 © 2008 J. C. Hoe 18-447 Lecture 8: Performance -- James C. Hoe Dept of ECE, CMU February 11, 2008 Announcements: Midterm 2/18 in class, Lectures 1~8 Lab 2 due this week (5% bonus for Tuesday) HW 2 due Friday at noon, HH-A304 Handouts: Practice Midterm and Solutions CMU 18-447 S’08 L8-2 © 2008 J. C. Hoe Latency vs. Throughput ± Latency (a time measure) ­ time between start and finish of a single task ­ most applicable in interactive applications ± Throughput (a rate measure) ­ number of tasks finished in a given unit of time ­ most applicable in batch applications ± Throughput is not always 1/latency when concurrency is involved (think bus vs. F1 race car) ­ improve latency ?? improve throughput ­ improve throughput ?? improve latency ± Not completely distinct when different granularity are considered ­ increasing throughput of component processing shortens the latency of the overall task
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CMU 18-447 S’08 L8-3 © 2008 J. C. Hoe It is all about time ± Performance = 1 / Time ­ shorter latency higher performance ­ higher throughput (job/time) higher performance ± UNIX “time” command ­ user CPU time: time spent running your code ­ system CPU time: time spent running other code on behalf of your code ­ elapsed time: wall-clock time ­ elapsed time – user CPU time – system CPU time = time running other code unrelated to your code 1. Be precise about what you measured when reporting 2. Rule of thumb: measure and report wall-clock time on unloaded system CMU 18-447 S’08 L8-4 © 2008 J. C. Hoe IPC, MIPS and GHz ± The metrics you are most likely to see in marketing are IPC (instruction per cycle), MIPS (million instruction per second) and GHz How are they incomplete? ± Iron Law on Performance wall clock time = (time/cyc) (cyc/inst) (inst/program) ­ MIPS and IPC are averages which instructions matter ­ GHz can be boosted artificially by design (lower the other 2 terms) e.g., 1.4GHz P4 1.0GHz P3 1/GHz 1/MIPS 1/IPC
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CMU 18-447 S’08 L8-5 © 2008 J. C. Hoe Pseudo FLOPS ± Scientific computing community often use pseudo FLOPS as performance metric nominal # of floating point operations program runtime ­ e.g. FFT of size N has nominally 5N log 2 (N) FP operations ± Is this a good, fair metric to compare machine + algorithm combinations? ­ not all FFT algorithms have the same FP OP count
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This note was uploaded on 04/07/2008 for the course ECE 18447 taught by Professor Hoe during the Spring '08 term at Carnegie Mellon.

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L08 - 18-447 Lecture 8: Performance -how to summarize &...

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