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Hardware Is Cheap, Programmers Are Expensive
Sportsqs points out a story at Coding Horror which begins:
"Given the rapid advance of Moore's Law, when does it make sense to throw hardware at a programming problem? As a general rule, I'd say almost always. Consider the average programmer salary here in the US. You probably have several of these programmer guys or gals on staff. I can't speak to how much your servers may cost, or how many of them you may need. Or, maybe you don't need any — perhaps all your code executes on your users' hardware, which is an entirely different scenario. Obviously, situations vary. But even the most rudimentary math will tell you that it'd take a massive hardware outlay to equal the yearly costs of even a modest five person programming team."
Recently my boss reviewed my schematic and asked me to replace 1% resistors with 2 or 5% "because they are cheaper". Yes true, but I spend most of the day doing that, so he spent about $650 on the task, thereby spending MORE not less.
So yeah I agree with the article that's it's often cheaper to specify faster hardware, or more-expensive hardware, than to spend hours-and-hours on expensive engineers/programmers trying to save pennies.
Or as Benjamin Franklin said, "Some people are penny-wise, but pound foolish." You try to save pennies and waste pounds/dollars instead.
FOX NEWS.com should be BANNED from television and internet. Have the Congress take it over and give us Truespeak.
"10,000! We could almost buy our own ship for that!" "Yeah, but who's going to fly it kid? You?"
We'll see. The good developers probably won't be in the first wave of folks looking for jobs. I know our company is still in the "we have to figure out how to hire fast enough to do next year's work" mode.
Where having good engineering really helps, though, is in version 2.0 and 3.0 of the product, and when you try to leverage embedded devices with some of the same code, and when you try to scale it up a few orders of magnitude... basically, it buys you flexibility and nimbleness on the market that the "throw more hardware at the problem" folks can't match.
Despite Moore's Law being exponential over time (so far), adding additional hardware is still sub-linear for any snapshot in time. So it's not going to automatically solve most hard scalability problems.
E pluribus unum
From someone who has been there, done that. I can say that throwing hardware at a problem rarely works.
If nothing else, faster hardware tend to increase the advantage of good algorithms over poorer ones.
Say I have an alghorithm who runs at O(N) and another one functionally equivalent that runs at O(N^2). Now let's say that you need to double the size of the input keeping the execution time constant. For the first algorithm you will need a machine which is 2X faster than the current one, for the second O(N^2) you'll need a 10X times faster machine.
Let's not forget that you need not only things to run fast, but to run correctly, and the absurdity of choosing less skilled programmers with more expensive hardware will become painfully evident.
PS: Sorry for the typos and other errors: english is not my native language, and I've got a bit too much beer last night.
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This only works for certain cases. Some your problems are too many orders of magnitude too big to throw hardware at them.
Before you do anything: Profile, analyze, understand.
It might be useless to spend a month of development effort on a problem that you can solve by upgrading the hardware. It's also useless to spend the money on new hardware and the administrator time setting it up and migrating programs and data, when you could've just known that wouldn't have helped in the first place.
Two questions I used to ask when giving talks: "Okay, who here has used a profiler? [hands go up] Now who has never been surprised by the results? [almost no hands]"
Before you spend money or expend effort, just take some easy steps to make sure you're not wasting it. Common sense.
Everyone knows a blind mapmaker will finish his work much faster on a motorcycle than he will on foot. This is basically the same thing...
-1 Uncomfortable Truth
When I was programmer, we once had a programming job at a large bank. One of our main reports was running across all booked loans and calculated the futural finance stream (interest and amortization) either until the debt was paid off, or up to 40 years at current interest rates. This report was sent to the Federal Bank for control, and to the department tasked with managing the bonds to get enough capital for further loans.
This report took 200 processor hours to complete. To get it done, it was split into 18 tranches, each running 11 hours. So it was possible to complete the job during a weekend run on 18 processors, and restart it twice in case of errors.
A colleague of mine took the task to rewrite the report to speed it up. For that she hooked into each booking that changed the amount of loan or the interest rate, repayment, end-of-contract or amortization and modified it so it wrote a flag into a table.
Then she rewrote the central report to store the calculated finance stream each time it was calculated. Loans that were unchanged since the last calculation didn't have a flag set, so the report took the old calculation. This sped up the report about 150 times: Instead of 200 processor hours now it completed within 1:20 h.
It allowed to put four large RS/6000 out of service, cancelling of the service contracts, rescheduling the report to run daily instead on weekends and saving on weekend man hours. With the daily report to the bond managment department also the finance controlling unit became interested and used the report results to refine their own tools. This together easily paid the amount of programming time put into the report.
As you can see: There are programming task where just throwing more computing power at doesn't solve the problem. It hasn't even to be some high level programming job, sometimes it's a dull task (finding all points in a bookkeeping system where the booking changes the finance stream of a loan is a dull task!), but if someone gets it done, it pays off easily.