Transcript Issues in Business Method Patents

Comments on
The Progress of Computing
William Nordhaus
Iain Cockburn
Boston University
and NBER
Key findings
• Computer performance (per $ or per labor hour)
has increased by 1012 since 1900
• All the gains since 1940: post-war CAGR of
performance about 55%
Conclusion
“Output” measures of quality-adjusted prices decline
much faster than “input-based” hedonics
– standard hedonics “may be far wide of the mark”
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?
Benchmarking
Computer scientists all say
“execution time for your application”
Metrics of Performance
Application
MSOPS
Answers per month
Operations per second
Programming
Language
Compiler
system
architecture
CPU
(millions) of Instructions per second: MIPS
(millions) of (FP) operations per second: MFLOPS
Datapath
Control
Function Units
Transistors Wires Pins
Megabytes per second
Cycles per second (clock rate)
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
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
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
True cost of computing: hardware=20%
In MIS, commonly refer to TCO - “Total Cost of Ownership”
=Hardware cost (of which arch. about 30%) plus:
• Support
• Personnel training
• Application development
• Upgrades
• Consumables
• Downtime
• Security
• Depreciation etc. etc.
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:
Speedupoverall =
ExTimeold
ExTimenew
1
(1 - Fractionenhanced) + Fractionenhanced
Speedupenhanced
e.g. Floating point instructions improved to run 2X; but
only 10% of actual instructions are FP
Speedup overall = 1.053
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?
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?
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