1. Comments on The Progress of Computing William Nordhaus Iain Cockburn Boston University and NBER

3. Key findings Computer performance (per $ or per labor hour) has increased by 10 12 since 1900 All the gains since 1940: post-war CAGR of performance about 55%

4. Conclusion “Output” measures of quality-adjusted prices decline much faster than “input-based” hedonics –standard hedonics “may be far wide of the mark”

5. Not so fast… Hedonics not intended to measure productivity, rather changes in WTP for characteristics PPI prices a much bigger bundle than “horsepower” What are appropriate output performance measures? What are the connections between performance, pricing, and productivity?

6. Benchmarking Computer scientists all say “execution time for your application”

7. Metrics of Performance Compiler Programming Language Application Datapath Control TransistorsWiresPins CPU Function Units (millions) of Instructions per second: MIPS (millions) of (FP) operations per second: MFLOPS Cycles per second (clock rate) Megabytes per second Answers per month Operations per second MSOPS system architecture

8. Output measures for computing Computation throughput: “information per second”, MSOPS I/O bandwidth Availability/uptime Latency Transaction processing time/integrity Switching/routing efficiency Accuracy: error rates/correction, rounding, correspondence to physical systems Application execution time –“interface speed” : page down, recalc, redraw –program load –task completion time: database query, matrix inversion, spell check

9. Where do more/cheaper MSOPS make a big difference? MSOPS-constrained scientific computing: –3D fluid dynamics (weather forecasting) –geophysics –engineering structural analysis (airframes) –molecular modeling –bioinformatics –simulation –BLP MSOPS-constrained commercial computing: –animation/graphics –optimization/search problems –“data mining” –reservoir modeling –automotive/aerospace design –protein folding –50% of “Top-500” computer users are now industrial

10. Classes of problems where a faster processor makes little difference High-bandwidth/high overhead networks WWW searches Transaction processing IO-constrained activities –e.g. waiting for user input

11. True cost of computing: hardware=20% In MIS, commonly refer to TCO - “T otal C ost of O wnership ” =Hardware cost (of which arch. about 30%) plus: Support Personnel training Application development Upgrades Consumables Downtime Security Depreciation etc. etc.

12. Moore’s Law vs. Amdahl’s Law Moore’s law: geometric progression in performance measures (so far) Amdahl’s law: diminishing returns to speeding up small fractions of a task: e.g. Floating point instructions improved to run 2X; but only 10% of actual instructions are FP Speedup overall = 1.053 Speedup overall = ExTime old ExTime new 1 (1 - Fraction enhanced ) + Fraction enhanced Speedup enhanced

13. Peak power, unused FLOPs, option value More than 95% of PC computation capacity “idle” Projects to harness idle time through distributed computing –SETI@home, Condor, Entropia, Compute-Against-Cancer, Folding@home, NASA/NSF “grid computing” What has happened to $ per used MSOP? If we are buying an option to use peak performance in bursts, how to think of pricing that?

14. WTP for performance? Decreasing marginal utility of anything MSOPS doesn’t fully capture aspects of performance that matter to users What’s the choice set?

15. Suggestions Think about pricing a richer notion of “output” of computing devices –application execution time –IO capacity (+ connectivity) –portability/scalability Investigate what it is that economic actors value when purchasing computer power –Puzzles: PC vs. time-sharing mainframe “pretty pictures” & the dancing paperclip