Great! There were so many The Kittens Game uses this philosophy of “diminishing returns” to make sure that game play doesn’t zoom up exponentially in the player’s favor.). at the critical code; but only after that code has been identified. I am currently working on a side project that has to do with content marketing and measuring its performance. programs. a &= \zeta - 0.5 \cr Does PostgreSQL perform better than MySQL? Anyway, managers love when you give them options that are all wrapped up and ready to go. used in cooking to be cleaned as soon as food preparation is complete, before serving, and if there’s more people than usual at the meal, start washing dishes as soon as possible after the first people are done eating), it’s an easy optimization for the programmer to create, taking only a second or two, it poses little or no risk to correctness, it poses little or no risk to readability or maintainability, I am trying to produce 300,000 slabs, which I need for another upgrade, I have a bunch of miner kittens and upgrades, producing 16973 minerals per second, My crafting bonus is +618% (multiplier of 7.18), so I can transform 250 minerals into 7.18 slabs, The minerals bonus from buildings is +2095% (multiplier of 21.95), I have 13 quarries, and can build a 14th quarry with an additional additive minerals bonus of 35% for 228.048 scaffolds, 684.144 steel, and 4560.959 slabs, As is: 295,000 slabs will take $$295\text{K} / 7.18 \times 250 = 10.272\text{M}$$ minerals, which will take 605.2 seconds (a little over 10 minutes) to produce, Minerals building multiplier will increase from +2095% to +2130%, increasing our production to 17244 minerals per second, We’ll need to produce 295,000 + 4561 = 299561 slabs to make up for the slabs we have to spend for the 14th quarry. want? Summary: Potential for execution time optimization was analyzed, but actual optimization effort deferred (less important than other work). Again: it’s speculative optimization. (for some calculations, this meant standard deviation on the order of 0.01 to 0.1 of the mean value.). But some were not. spontaneous gut decision — chose the best car Not quite. Join us for a 15 minute, group Retrace session, How to Troubleshoot IIS Worker Process (w3wp) High CPU Usage, How to Monitor IIS Performance: From the Basics to Advanced IIS Performance Monitoring, SQL Performance Tuning: 7 Practical Tips for Developers, Looking for New Relic Alternatives & Competitors? It is not uncommon, though, that the root problem lies in the basic structure of the program, and if a better structure had been chosen at the outset, it might yield an order of magnitude improvement, and no optimization would be required. So they run simulations and analyze and build scale models in a wind tunnel and rely on decades of past engineering work in order to come up with a good wing design. A good programmer will not be lulled into complacency by such reasoning, he will be wise to look carefully at the critical code; but only after that code has been identified. This project involves collecting potentially a very large amount of data. A few years ago, I was working on a web game called GeoArena Online (I’ve since sold it, and the new owners rebranded to geoarena.io). This was a good read. Communications: I wrote a Java program to relay messages between the serial link and my Python application, via ZeroMQ sockets. The sentiment of premature optimization is still very valid though and the concept applies to modern development. So You Want To Be An Embedded Systems Developer, Important Programming Concepts (Even on Embedded Systems), You might also like... (promoted content), Premature optimization is the root of all evil, responding to someone who was concerned about excessive connections to a database, the difference between tactics and strategy, one study on several processors showed a speed handicap in the 1.1 - 3.0 range, trick question about the locked room with the broken window and the two dead bodies on the floor surrounded by water and broken glass, Is it faster to cast something and let it throw an exception than calling. Yes, it costs me time to do this, but the cost of a simple performance trial is much less expensive than trying it out on the real thing. What you’re trying to do is not worth doing, it’s just going to make your life harder. be in a restaurant and eavesdrop on the couple one table researchers, pastoral counselors, and graduate students in In this case, the results were identical (0x408AAAAB ≈ 4.3333335), but because 1/3 isn’t exactly representable in floating-point, multiplying by (1/3.0) and dividing by 3.0 aren’t guaranteed to have the exact same result. clinical psychology, as well as newlyweds, people who If this happens too often, your Golden Goose will strain herself and have a prolapsed oviduct. Retrace Overview | January 6th at 10am CST. No thanks. We know the system is better if the time decreases, but how do I know when it’s good enough? I’m not a marriage counselor. Now along the way, Don Bluth met a number of odd characters. In established engineering disciplines a 12% improvement, easily obtained, categories. “Premature optimization is the root of all evil ” — Donald Knuth. But the tone of what I hear in the programming community on the topic of premature optimization seems to imply that nobody has time for anything, unless it can help what they’re working on right now. Each test It can run at 300 nips per second, I’m sure of it.”, “I believe you,” said Austere Joe. And they’ll catch up to that, and then we’ll sell equipment that goes up to 300 nips per second. — is something I try to keep in mind, as an amusing fallacy, when the topic of premature optimization comes up; the overhead of HTML tags really doesn’t add a whole lot when you consider that data compression can reduce the overhead of human-readable English, and it’s possible to create HTML files with very high information content and not much formatting. It’s rare to find systems where one component determines the overall performance. And gather as much evidence as you can, since that can help reduce the uncertainty. He has found The best way to explain this is with a simple story. handled poorly. Principle is what you have to return to, if you’re ever going to do the job right. But it’s a different kind of difficulty from software engineering. Let’s figure this out. Then the magnetics got cheaper and more readily available, because all of a sudden there was this market for low-power switched-mode converters, and the economies of scale kicked in. the best choice for $$x$$ is near $$x=1$$ if $$\zeta$$ is less than 0.5, the best choice for $$x$$ is near $$x=-1$$ if $$\zeta$$ is greater than 0.5, when $$\zeta$$ is close to 0.5, the best choice for $$x$$ is near $$x=0$$, where there’s a very small increase in $$f(x)$$ relative to other choices of $$x$$, If you complete a working software prototype, and can measure your software’s execution speed, and it’s not adequate, If you haven’t completed a working software prototype yet, but there’s strong evidence that it’s not going to be adequately fast, If you have software that is adequate with modest inputs, but it doesn’t scale well, so that when it is given very large inputs, it will not be adequate*, If you’re working on software whose value to the customer is correlated to its processing throughput and/or latency (mathematical computation, video rendering, real-time trading, etc.) and then was asked for an answer. One of the hardest parts of software development is knowing what to work on. Run a profiler, and hope there is a pattern where some bottleneck presents itself as the execution hog. Donald Knuth. Two people work at the Hobart, one at the sink handling incoming items and the other handling outgoing items to let them dry and put away. If we don’t do any performance tuning or optimization, our new product launch could be a complete disaster. One aspect I would make sure to understand, before jumping into a new project, is in which of the following categories that project falls: If the last of these is true — project needs to get to market as fast as possible, and it needs to also meet some challenging performance criteria — then I would run away and find another project, unless you’ve got a high appetite for risk and don’t mind failure. Premature optimization is the root of all evil (or at least most of it) in programming. One of the biggest challenges is making sure we are making good use of our time. Here’s the thing. He just ran into a logging problem that sounds similar.” Or maybe it’s keeping the team focused or prioritizing things: “Okay, we can’t have both speed and low cost… from what I understand, this customer says he wants low cost, but if push comes to shove, he needs speed. In the U.S., AOL alone had over 2 million dialup users in 2015. test, except that this time, after giving them all of the information, But sometimes it’s not. I don’t know the exact numbers, but let’s say it was improved from 120 nips per second to 300 nips per second. Or just brute-force it numerically by using a solver function like scipy.optimize.minimize_scalar: There we go, not very interesting. Donald Ervin Knuth is an American computer scientist, mathematician, and professor emeritus at Stanford University. Who knows where I’ll end up. In the future, I will have to figure these out. I write publicly because I have something to say, and because some people seem to find my articles educational and entertaining. the time, which is just above chance. Presumably there’s more than one potential performance improvement, so you’ve got a number of different options $$p_2, p_3, \dots p_n$$ — which aren’t necessarily exclusive, and may not have independent effects on the overall timing. Keep the optimization blinders on, and don’t take them off until you run into a problem. Then when that happens, we’re going to make the same machine but change the nameplate and allow it to run up to 200 nips per second. In part, this is because the cost of making errors is so high. When we automate it, or oversimplify it, or turn it into a set of predetermined rules, we shirk our responsibility as engineers, doing so at our own peril. Some of the preprocessing I had to do, before calling the methods contained in the Python/Jython package, involved finding the root of a nonlinear scalar equation. Another was the Phantom Downvoter, who would throw -1 votes without comment, leaving chaos and disapproval in his wake. After all, he says it “is often a mistake” which doesn’t mean it’s always a mistake. Chances are, if you’re a software engineer, you work with an already-impossible-to-meet deadline, so the cost of extra development time for performance improvements is extremely high, and you’ll need to ignore those urges to optimize. Project A was a program in Java that worked along with a set of Jython scripts to generate C code. For example, I could pursue $$p_1$$ and $$p_5$$ and $$p_7$$, and the execution time savings might be different than the sum of the savings from individual improvements $$p_1$$, $$p_5$$, and $$p_7$$, perhaps because $$p_1$$ interferes with the other two. Let’s say you’re proposing some type of software optimization. Premature optimization is the root of all evil. Let’s say we had a similar mathematical situation as before, but with a slightly different equation: Perhaps all I know is that $$\zeta$$ is equally likely to be any value between 0 and 1; if this is the case then I can use numerical analysis to compute an approximate expected value of $$f(x)$$, which we’ll graph along with the worst-case value of $$f(x)$$ and the 5th percentile: And in this case, my best choices to maximize expected value are around $$x \approx \pm1.155$$. 4561 slabs can be crafted from $$4561 / 7.18 \times 250 = 158809$$ minerals, which takes 158809 / 271 = 586 seconds. So what the heck does a Hobart dishwasher have to do with programming? This project used a proprietary communications protocol. Dan Luu writes about the use of the Internet in areas with slow transmission speeds: More recently, I was reminded of how poorly the web works for people on slow connections when I tried to read a joelonsoftware post while using a flaky mobile connection. I have been focusing on getting user feedback to iterating on the final product features and functionality. If it meets the test, keep the original code in as comments, Do your designated job, and stay out of each other’s way. Got it?”. battle makes but few calculations beforehand. Here’s the page for COM’s IUnknown::QueryInterface, which you may remember from my “Garden Rakes” article, profiled with client-side caching disabled, using Google Chrome’s Developer Tools: On my computer we’re down to about 1.3 seconds to get to the DOMContentLoaded event — and I’ve got a reasonably fast computer with a reasonably fast connection (≈ 10 megabit/second) to the Internet. The difference in software optimization has to do with the complexity. Validating product feedback and perfecting the product feature set is an order of magnitude more difficult (and important) than figuring out any type of performance optimization or scaling issues. I finally ended up choosing a number of samples based on the time I had available, typically 2 - 60 minutes, depending on what I could do while I was waiting. It is by attention to this point that I can foresee who is likely to win or lose.” — Sun Tzu, The Art of War. Aside from the fact that it would ruin the pace of the action, if they did hesitate, they would almost certainly lose, because the other person isn’t going to hesitate. Otherwise, Test optimized code. specifically turned off. In this particular case, the optimum value of $$x$$ is very sensitive to $$\zeta$$ when $$\zeta$$ is near 0.5. The hot-spot code in our motor control ISR (which might be 40% of the entire codebase) typically looks like this. This site uses cookies to deliver our services and to show you relevant ads and job listings. Many of the “premature optimization” questions on Stack Overflow are tactical in nature. Or if the game is changed to become Pin the Hoof on the Donkey? I wrote the bulk of this software in Python, for purposes of rapid prototyping, but all of the performance-critical systems leveraged Python modules that are implemented as extension modules outside the Python language (running native code typically compiled from C). Don Not’s philosophy seems to be that only the first of these is important. is never considered marginal; and I believe the same viewpoint should prevail in software That’s what you want project progress to look like: But real projects tend to careen in all sorts of directions, especially when engineering staff is doing something where they’re not quite sure how to get from Point A to Point B, and you end up with this: or sometimes they don’t get to Point B at all, maybe they end up at Point C, or the project is canceled, or the company goes bankrupt. The key here is really staying in tune with your manager and/or chief architect so you know what kind of problems you should be solving. Then there was Don Not, a part-time deputy sheriff and part-time software engineer from somewhere in North Carolina. More nips per second is better, at least if it’s done properly, because this means the machine can produce more of that high-quality cotton thread in any given day. I call this the AB Problem. Avoid premature optimization by getting user feedback early and often from your users. That article was much more polarizing than I expected. So there was a speedup of 3.4× vs. a theoretical increase of 7×. As programmers, we tend to have vision problems: we see the potential improvement much more clearly than the cost tradeoffs that come with it. Its source is credited to Donald Knuth. If you remember Casablanca and Raiders of the Lost Ark, they each showed a travel montage with a line unfolding across a map, as our heroes traveled from Paris to Marseille to Oran to Casablanca, or from San Francisco to Honolulu to Manila to Kathmandu. It can help me see very clearly how long different parts of the program take to execute in one particular environment, and that may give me some ideas — typically at a tactical level, sometimes at a strategic level — for how to improve the overall execution time, but it will not tell me much about which sections of the code are the best candidates for that improvement. This takes some detective work, and you should do it, to the extent it is possible. measured improvement in server performance. For comparison of execution speeds, I have a Lenovo W520 laptop with an Intel Core i7 2760QM quad-core processor (8 threads). just by focusing on what he calls the Four Horsemen: defensiveness, So when is Donald Knuth wrong? (A side note: this little exercise has brought back some personal memories. No problem, just swap out the database layer! In the end I did some profiling, and concluded that it wouldn’t have much benefit based on the calculations I needed. It was a custom system and protocol encoded in a very terse format, kind of like Gopher but more obscure. Or just pretend that $$f(x)$$ is your net profit from running an ice cream business. I have no idea whether the best I can do for each of them is 13.5 microseconds, or 10 microseconds, or 2 microseconds, or 130 nanoseconds. One question to ask: is “critical code” for tactical optimization only about 3%? Optimization is rarely easy (otherwise it would have been done in the original circuit design) but it is something that a circuit designer usually keeps in mind with proper perspective — each optimization has tradeoffs and should prove demonstrable gain. Hence the advice that premature optimization is evil. If I was using it to gather data, then I’d run it longer. As Donald Knuth famously once said, “… Premature optimization is the root of all evil.” System designers must consider a myriad of aspects of application and underlying hardware architecture when bringing a new application or technology to market. So what do we do? A lesson that we software engineers learn early in our careers is that “premature optimization is the root of all evil.” This gem of advice from the inimitable Donald Knuth … notice. Don Bluth knew that Don Not was quoting Don Knuth out of context. If one of those areas is improvement in execution time, then you should still be using some kind of profiler to measure that improvement quantitatively, but be creative and think of different ways to make software better. Premature optimization is the root of all evil-- DonaldKnuth. So what’s your appetite for risk? that he considers the most important of all: contempt. Then I would realize I forgot to include some important variables, and I’d have to run it again. He has figured If they do produce a useful benefit, then you’re ahead of the game. But fun != readable). you’ll make the wrong choice. I worked on the team developing the Jython scripts. It applies just as much today as it did in the days of mainframes and punch cards. Let’s hold off on that issue for a moment, and look at another quantitative example. One of the things I learned over the years is that as an engineer, it is a real privilege if you get to make a major engineering decision. This improved the boot-load time to under a minute, which was deemed acceptable. married for a long time — in other words, almost two In a recent article I analyzed this error for a 10-bit ADC and found that worst-case error including DNL and INL was 0.244% of fullscale. A classical example of this is a startup that spends an enormous amount of time trying to figure out how to scale their software to handle millions of users. Yeah, there might be only a few areas where I find room for improvement without a huge implementation cost, but the critical code in our situation is much larger than 3%. The Scipy library has a function called scipy.optimize.brentq which is written in C to be lightning-fast, but it calls your Python function some number of times (usually in the 10-30 range), so its speed of execution depends on the execution time of that function, and I wasn’t sure whether this would be a bottleneck in the overall time of execution in my analysis. Donald Knuth — p. 671 Premature optimization is the root of all evil.Variant in Knuth, "Structured Programming with Goto Statements". Right? I figured that profiling would slow my program down by a factor of 5-10, perhaps enough to impair the validity of the profiling result itself, so I was wrong about that. You and your frat buddies are all married with young kids, and nobody wants to revisit some of the inner crufty code that worked fine on 56K dialup but not so well on broadband high-speed Internet access. So if you have a retirement plan with a big investment firm, most likely they will ask how long your time horizon is (in other words, when you plan to retire) and then suggest a mix of stocks and bonds that gradually moves from more aggressive (stocks) to more conservative (bonds) over time. Knuth is a first-rate mathematician and computer scientist who has been writing and curating The Art of Computer Programming over the past 55 years. Don Bluth felt that Don Not should have at least stated the entire sentence of this Don Knuth quote: We should forget about small efficiencies, say about 97% of the time: premature My biggest concern right now isn’t performance or scale. Great! Of course I wouldn’t bother making such optimizations on a one-shot If you want a risk-free investment… well, there ain’t no such thing, but you could put your money into government bonds of stable countries. (The truel at the end of The Good, the Bad, and the Ugly is a rare exception.) It is better to meander a little bit, understand when you’re getting off track, and make quick corrections, than it is to zoom full-bore in a straight line in the wrong direction. I would be curious to see how their benchmarks have improved with each small change, and I’d be willing to bet that many of those improvements have had only a small effect. The in door is typically connected via a stainless steel shelf to a sink and sprayer; the out door is typically connected to another stainless steel shelf. efficiencies, say about 97% of the time: premature optimization is the root of all evil. He likes making things spin. Great, anyone can use the profiler. as having good mileage and a large trunk but was old and And then another more important one, Problem B, comes along, and now you have to put Problem A on hold, figure out how to deal with Problem B, then figure out how to re-engage with Problem A. Or read one of the other related articles on this subject, most of which are shorter. Before we try to answer that, consider the difference between tactics and strategy. What Donald Knuth said was: “Premature optimization is the root of all evil (or at least most of it) in programming.” It’s always worth bearing YAGNI in mind, it sits quite nicely next to a … Because sometimes you don’t know whether you need to optimize something; there are uncertainties and risks and opinions but no definite answers. On the other hand, look what so-and-so did in his blog! This is usually a red flag for avoiding optimization effort, because it yields low ROI. Developers are also expensive and in short supply. Premature optimization is spending a lot of time on something that you may not actually need. of the children. Yaay!”. Premature optimization was coined by Professor Donald Knuth, who argued that optimization in the early stages of software development was detrimental to success 97% of the time. But how could it not be? It’s really hard to make much of difference in efficiency if you only attack one aspect of the system. This process leaves behind short cotton fibers and other impurities, and the thread produced by the combing process is very soft and is suitable for high-thread-count fabric. The origin of premature optimization. He did this hundreds of times. So when Donald Knuth says you should forget about small efficiencies 97% of the time, you should take this advice very seriously. Or there’s a small potential improvement, but it’s so widely applicable that the implications become very large. I’ve probably got some of the details wrong, because I’m telling it third-hand. On the one hand, don’t fall prey to a fool’s errand in a gold rush; some will succeed but many will fail. Of the four, one was clearly the best. Sometimes it’s just backed by the authority of Donald Knuth, the original author of the quote. Translations of the phrase DONALD KNUTH from german to english and examples of the use of "DONALD KNUTH" in a sentence with their translations: ...arbeitete er unter anderem mit donald knuth an dessen tex-softwaresystemen. (Just ask Nicholas Leeson.) But there are a couple things that I think get missed, and this article skips over them as well: First is that there is an implicit assumption that programmers will be able to optimize when optimization is called for. The downsides here are. In essence, a good portion of Blink is about this phenomenon, that the best experts can tune into key parts of a problem to analyze — even if they may not know how their brain goes about doing it. In the meantime it’s uncertain, and you’re essentially making a bet, one way or the other. One was to decouple my numerical analysis from visualization of it. I could review most circuit designs completely in the matter of a few hours, to look at things like power dissipation or cost, and identify some potential avenues for improvement all by myself. Get it done right? You know about Premature optimization is the root of all evil, so you dutifuly profile your application and figure out where the hot-spots are, based on PCs with the latest Pentium II processors and 56K dial-up connections. Digital photography can be judged by many aspects of image quality. Knuth has been called the "father of the analysis of algorithms". Ultra-compact switchers have enough appeal, and are well-established enough that consumers are willing to pay slightly more to support a market for them. But I did have to run the script frequently during development, performing edit-run-debug-test cycles. A page of plaintext in 5 seconds is better than a page of glitzy images and AJAX that takes 5 minutes to load. He heard a quote from Tony Hoare and liked it so much that everyone now attributes it to him: “Premature optimization is the root of all evil.” Many people have run up against and been frustrated by misinterpretation or overapplication of this quote. For example: They’re tactical because they’re very concrete questions dealing with localized implementation details, and have very little to do with the overall software architecture. Anything else you do is trying to outsmart the compiler. (I wouldn’t have gotten into motor drive design if I hadn’t taken such a risk… but that’s a story for another time.) Once real people use it for real purposes, you can do benchmarks to see where you really need to optimize. Dijksterhuis gave the test to eighty volunteers, flashing the — this is essentially the same as the previous point, If there is significant risk of any of the preceding items being true, Risk reduction may require some optimization work before it is possible to measure execution time — we talked about this already, Strategic optimization and tactical optimization should be handled differently, Critical code may be more abundant in some systems, Optimization doesn’t always refer to execution time, Not everything can be measured with certainty, Measurements for optimization may not be measuring the right thing, Measurements can tell you how fast code runs now, but not necessarily how fast it, Criteria for measurement may change with time, Criteria for measurement may change from one situation to another, The effects of an optimization effort may increase (or decrease) even after that effort is complete, Small gains may still have high value in some systems, Imperfect attempts at optimization still have value, It’s still possible to make incorrect conclusions from quantitative measurements, Information overload, even if it is good information, can blind us from the best conclusions, Abundance of computing resources makes us assign low value to many types of optimization, other aspects of performance besides speed, Rayovac power supply for camera battery charger: 12V 0.5A (6W), BlackBerry charger (don’t ask me where I got this; I’ve never owned a BlackBerry): 5V 0.7A (3.5W), In 1997, bulky 60Hz transformers were the normal, cost-optimal solution for AC/DC converters in the 3-10W range. He is the 1974 recipient of the ACM Turing Award, informally considered the Nobel Prize of computer science. Even if multiplication is faster, and making such a change did cause an improvement in performance, it wouldn’t be worth doing unless there was substantial evidence that a program was unacceptably slow and the division was a significant performance bottleneck. One important point, which I hope you noticed in the first two of these examples, is that in order to be sure there had a tangible benefit from optimization, I went through some measurement or quantitative analysis to figure out what that benefit would be. Our last philosophical section of this wretched essay will revisit strategic optimization. And if you want to play a game of Grungy Algebra! Don’t let the sound bite keep you from making continuous improvements toward a better world! The foundations of computer science are discussed in depth there. * disclaimer: author is heavily invested in a static site ^generator*. Here’s another answer, in its entirety, in which he responded to a question about which was better, using text.startswith('a') or text[0]=='a': The stock phrase for the questiom is: “Premature optimization is the root of all evil”. But software is different, because the cost to swap out some piece of it is very small, at least in theory. For example, software with lots of branches and error-handling code and special cases might have less than 1% hot-spot code; it could be 10 lines out of a million that impact certain slow calculations. that we couldn’t find the pattern. That was arguably a different time when mainframes and punch cards were common. There’s a story I heard from K., a former colleague and mentor of mine. It is a computer game, after all. “Premature optimization is the root of all evil” is a famous saying among software developers. Object reference not set to an instance of an object, IIS Error Logs and Other Ways to Find ASP.Net Failed Requests, List of .Net Profilers: 3 Different Types and Why You Need All of Them, Windows Server Performance Monitoring Best Practices, Evaluating several storage options for all of the Google Analytics data that I need to collect and query which could be “big data”, How to queue up and scale out a massive number of workers to crawl all the website pages weekly, Evaluating if I should use a multi-cloud deployment to ensure highest availability, Figuring out how to architect a product to host in several data centers internationally for when I go global, Ensuring I have 100% test coverage for all of my code. Do you think people learning software development in remote rural areas of the world enjoy using the MSDN website? Alternatively, to handle the same power, you need a smaller volume of magnetics.) This isn’t always the case. So even if an improvement is small, it may be worth doing. on system startup? Somehow this seems to be the alleged norm in today’s software world, especially when it comes to services offered on the Internet. We ended up trying to err on the high side. Getting there always is what gives you the skill to turn out optimal end product from the beginning. My sequential code using only 1 thread took approximately 6.4 milliseconds per sample. Good engineering managers will look at the problems that are causing engineers to get stuck, and they’ll bring a perspective that as engineers we can’t seem to see. He was also one of the early experts on compilers. At the other extreme, I work on motor control software in embedded systems, where we typically have an interrupt service routine that executes at a regular rate, typically in the 50 - 200 microsecond range. It could be that A has a theoretical minimum execution time of 10 microseconds and B has a theoretical minimum of 3.7 microseconds — in which case, at first glance, it would be better to optimize section B because it has the higher theoretical gain in execution speed. As a result, I was able to offload some data processing from Python and get it to run in Java with less than 1% of total CPU usage. When they dug into the data, they found that the reason load times had increased was that they got a lot more traffic from Africa after doing the optimizations. The Rayovac adapter uses a 60Hz transformer, with a rectifier and voltage regulator. I racked my brain trying to think of a situation in which his advice on optimization was inappropriate. By using our site, you acknowledge that you have read and understand our Cookie Policy, Privacy Policy, and our Terms of Service. Let’s assume that you have some potential performance improvement $$p_1$$, and it can be measured. once in a blue moon when someone goes to an advanced UI page and does a manual query for all information? Or maybe my equations for $$a$$ and $$b$$ are estimates, and they might be wrong. single most important sign that the marriage is in trouble. the most; indeed, this feedback should be supplied automatically unless it has been With caching re-enabled, on my computer the amount of data transfered gets reduced to just the initial 19.6K of HTML (everything else cached client-side), although for some reason the server took longer to respond, and DOMContentLoaded took 1.6 seconds: I can’t figure out how to configure webpagetest to show me the results with caching enabled, but I’d assume it’s just the time needed to download that initial 19.6K of HTML, or 7.9 - 11.8 seconds. Another was that I considered implementing Chandrupatla’s method in a vectorized form. enormous amounts of time thinking about, or worrying about, the speed of noncritical At any rate, Dan Luu’s article contains one more little surprise at the end: When I was at Google, someone told me a story about a time that “they” completed a big optimization push only to find that measured page load times increased. (Usage of the numba library deserves some more detail, but I’ll leave that for another time.). It looks like there’s about a 1/3 chance of this happening: Sure enough, 1.0001f (with binary representation 3f800347) produces different answers on the dsPIC33E for these two methods. Occasionally we did have to turn to Java (either because of execution speed, or because of more delicate interdependencies with other aspects of Project A), and in this case, the challenges became how to review and test the implementation, because we didn’t know how to review the Java code, and the Java programmers couldn’t look at their code and decide whether they had implemented our calculations correctly. Tactics in the kitchen might go like this: Strategy is a different thing altogether. I am trying to prevent wasting a lot of time on things that may never be needed. Not as satisfying as I would like, but worth the effort; I’ve run several million samples worth, so it’s saved me several hours of waiting so far (this project is still not complete), and I can get my 1000000-sample runs done in about half an hour, instead of an hour and 45 minutes. On the other hand, there is gold in them thar hills, you just have to do your homework to make the best out of opportunity and luck. ), Put cups, bowls, etc. Incoming items need to be washed at the sink to get the majority of food off. If I was developing the script itself, I would keep runtimes short, so I could iterate quickly, maybe only 2-5 minutes, depending on what I needed. Sometimes optimization doesn’t mean execution speed. There was the Fastest Gun in the West, who was always the first one to answer, even though sometimes he got things wrong, and discouraged those who took their time. Outside of the U.S., there are even more people with slow connections. job, but when it’s a question of preparing quality programs, I don’t want to restrict The people given This will yield incremental speed improvements. This is a very valid concern to be thinking about, but not necessarily acting upon. The main problem I have with overuse of the “premature optimization” quote, as a response to programming questions, is that most people on Stack Overflow seem to view such questions as instances of a tactical problem that must be solved in order to complete a project. I could spend a lot of time working through the items that I listed above. The reason for this wide range is that I couldn’t get an exact measurement; there were Jython callbacks, both into functions my team had written, and into the interpreter itself just to get at data, and I didn’t have any way to intercept those easily to get a sense of how much time they took to run. He showed it on one of our Unix boxes in the test lab; it was interesting, and it had some basic text and formatting features. Let’s say you are working in a kitchen at a summer camp, and your goal is to get everything cleaned up and put away as fast as practical, so you can move on to the next task, whether it’s washing windows or bringing out the trash, or just lying on the grass and watching the clouds float by. Make it work first, then optimize as necessary. 261–301, §1. Or I would receive some new information from my colleagues or from the analysis itself, and I’d have to modify it and run it again. Is it with a 10 megabit/second DSL or cable modem connection? The last thing we want is to ship code that our users don’t like or that doesn’t work. Slay it, and all will live happily ever after. Sometimes it quoted in a longer form: “We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.” Both are usually attributed to Donald Knuth, but there also seems to be an idea floating around, that the quote was originally due to C. A. R. Hoare, and Knuth only popularised it. Here is Gladwell talking about Dr. John Gottman’s research on interaction between married couples, through analysis of videotaped conversations: Have you ever tried to keep track of that happens. 20 percent of the time. If you know the technologies you’re working with, you know there’s a right way — and that’s what you are doing. Who knows if any of it would have happened if the engineers in question had dismissed their ideas as one of premature optimization. I guess it’s worth it. Software, on the other hand, can include thousands or even millions of lines of code, which presents a real problem, because there are just too many choices for one person to see them all. As a result, optimization or performance tuning is often performed at the end of the development stage. In the the past I would also have done the programming (in assembly) but the company was transitioning to using C for embedded control and this was among the first. So I looked at the multiprocessing module, which essentially sidesteps the GIL by running separate Python processes, and communicates batches of inputs and outputs between them. Fortunately the purejavacomm library, although it’s somewhat of a niche library with a very small user community, provides a very similar interface and I have found it very robust. Yet we should not pass up our opportunities in that critical 3%. Maybe I don’t know exactly what $$\zeta$$ is, all I know is that it’s likely to be between 0.45 and 0.65. But if we look at the previous graph showing $$f(x,\zeta)$$, that’s also the value of $$x$$ where there is highest sensitivity to $$\zeta$$, and $$f(x,\zeta)$$ can be as low as -0.4 for extreme values of $$\zeta$$. It was taking over an hour to load a few megabytes. So far these examples have dealt with binary choices: either we do something (use multiplication by a fixed reciprocal instead of division, build a 14th quarry) or we do not, and one of the two alternatives is better. We’re going to look at Return on Investment (ROI) of this quarry dilemma. out that he doesn’t need to pay attention to everything Donald Knuth. The responsibility for strategy lies with whoever is in charge of the kitchen, whereas everyone doing the work needs to be aware of the best tactics. Or maybe they can use the part in production, but during development they need to add new features, which won’t fit, so they can’t use it, or they have to work a lot harder during development to get around the lack of memory. Your use of the Related Sites, including DSPRelated.com, FPGARelated.com, EmbeddedRelated.com and Electronics-Related.com, is subject to these policies and terms. You can bet that Google spends time optimizing their code that executes PageRank, and Facebook spends time optimizing their graph-algorithm code, and MathWorks spends time optimizing their matrix, solver, and simulation algorithms. Get your app rolled out. Two people are sometimes slower than one. I suppose some people got tired of all the Kittens Game references. We should forget about small Thus do many calculations lead to victory, and few calculations to defeat: how much more no calculation at all! This is part 3 of my buffet of study techniques. Usually what happened is that I would run it and get some useful statistics. is critical, and the most important thing is making a new product work well enough to be viable, or improving an existing product to improve performance, Allocate part of your staff — either on a permanent, or a rotating basis, whatever — to researching solutions, and avenues toward solutions, to foreseeable problems or improvements, Keep good records of the results of that research, so it can be applied, Allocate the rest of your staff to product development, including applying that research, More samples took longer to compute, but gave better coverage of sample space, Fewer samples ran more quickly, but didn’t cover the sample space as well, and yielded greater errors, you have to write your program so it can be calculated in batches (output batch = function of input batch), everything has to be serializable (some object types in Python are not), this slows down due to communication speeds, if data transmitted/received is very large, you have to figure out a good batch size (large batches = greater memory allocation but lower communications overhead, small batches = less memory allocation but higher communications overhead), Python scripts used in Project A: 14 out of the top 20, totalling 34.6% of CPU time, The top-level script running my Monte Carlo analysis: 2/20 totalling 3.8% of CPU time, Functions involved in root-solving: 2/20 totalling 3.1% of CPU time. I would argue that the concept of premature optimization relates to more than just literal performance optimization concerns. The brentq function also doesn’t work in a vectorized manner; it runs and computes a single scalar root of your equations. But those patterns, and those patterns of working, also reduce the larger challenges to doing a job well/right, the first time. So if I’m analyzing code trying to make it more faster, and there’s some big fat bottleneck in it, then a profiler can probably help me find that.