1093 lines
51 KiB
Markdown
1093 lines
51 KiB
Markdown
# Writing and Optimizing Go code
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This document outlines best practices for writing high-performance Go code.
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While some discussions will be made for making individual services faster
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(caching, etc), designing performant distributed systems is beyond the scope
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of this work. There are already good texts on monitoring and distributed
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system design. It encompasses an entirely different set of research and design
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trade-offs.
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All the content will be licensed under CC-BY-SA.
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This book is split into different sections:
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1. Basic tips for writing not-slow software
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* CS 101-level stuff
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1. Tips for writing fast software
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* Go-specific sections on how to get the best from Go
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1. Advanced tips for writing *really* fast software
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* For when your optimized code isn't fast enough
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We can summarize these three sections as:
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1. "Be reasonable"
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1. "Be deliberate"
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1. "Be dangerous"
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## When and Where to Optimize
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I'm putting this first because it's really the most important step. Should
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you even be doing this at all?
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Every optimization has a cost. Generally this cost is expressed in terms of
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code complexity or cognitive load -- optimized code is rarely simpler than
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the unoptimized version.
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But there's another side that I'll call the economics of optimization. As a
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programmer, your time is valuable. There's the opportunity cost of what else
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you could be working on for your project, which bugs to fix, which features
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to add. Optimizing things is fun, but it's not always the right task to
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choose. Performance is a feature, but so is shipping, and so is correctness.
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Choose the most important thing to work on. Sometimes it's not an actual
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CPU optimization, but a user-experience one. Something as simple as adding a
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progress bar, or making a page more responsive by doing computation in the
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background after rendering the page.
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Sometimes this will be obvious: an hourly report that completes in three hours
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is probably less useful that one that completes in less than one.
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Just because something is easy to optimize doesn't mean it's worth
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optimizing. Ignoring low-hanging fruit is a valid development strategy.
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Think of this as optimizing *your* time.
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You get to choose what to optimize and when to optimize. You can move the
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slider between "Fast Software" and "Fast Deployment"
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People hear and mindlessly repeat "premature optimization is the root of all
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evil", but they miss the full context of the quote.
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"Programmers waste enormous amounts of time thinking about, or worrying about,
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the speed of noncritical parts of their programs, and these attempts at
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efficiency actually have a strong negative impact when debugging and
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maintenance are considered. We should forget about small efficiencies, say
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about 97% of the time: premature optimization is the root of all evil. Yet we
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should not pass up our opportunities in that critical 3%." -- Knuth
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Add: https://www.youtube.com/watch?time_continue=429&v=RT46MpK39rQ
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* don't ignore the the easy optimizations
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* more knowledge of algorithms and data structures makes more optimizations "easy" or "obvious"
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"Should you optimize? "Yes, but only if the problem is important, the program
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is genuinely too slow, and there is some expectation that it can be made
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faster while maintaining correctness, robustness, and clarity."
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-- The Practice of Programming, Kernighan and Pike
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[BitFunnel performance estimation](http://bitfunnel.org/strangeloop) has some
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numbers that make this trade-off explicit. Imagine a hypothetical search
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engine needing 30,000 machines across multiple data centers. These machines
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have a cost of approximately $1,000 USD per year. If you can double the speed
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of the software, this can save the company $15M USD per year. Even a single
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developer spending an entire year to improve performance by only 1% will pay
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for itself.
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In the vast majority of cases, the size and speed of a program is not a concern.
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Easiest optimization is not having to do it. The second easiest optimization
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is just buying faster hardware.
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Once you've decided you're going to change your program, keep reading.
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## How to Optimize
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### Optimization Workflow
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Before we get into the specifics, lets talk about the general process of
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optimization.
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Optimization is a form of refactoring. But each step, rather than improving
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some aspect of the source code (code duplication, clarity, etc), improves
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some aspect of the performance: lower CPU, memory usage, latency, etc. This
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improvement generally comes at the cost of readability. This means that in
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addition to a comprehensive set of unit tests (to ensure your changes haven't
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broken anything), you also need a good set of benchmarks to ensure your
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changes are having the desired effect on performance. You must be able to
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verify that your change really *is* lowering CPU. Sometimes a change you
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thought would improve performance will actually turn out to have a zero or
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negative change. Always make sure you undo your fix in these cases.
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<cite>[What is the best comment in source code you have ever encountered? - Stack Overflow](https://stackoverflow.com/questions/184618/what-is-the-best-comment-in-source-code-you-have-ever-encountered)</cite>:
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<pre>
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//
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// Dear maintainer:
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//
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// Once you are done trying to 'optimize' this routine,
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// and have realized what a terrible mistake that was,
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// please increment the following counter as a warning
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// to the next guy:
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//
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// total_hours_wasted_here = 42
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//
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</pre>
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The benchmarks you are using must be correct and provide reproducible numbers
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on representative workloads. If individual runs have too high a variance, it
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will make small improvements more difficult to spot. You will need to use
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[benchstat](https://golang.org/x/perf/benchstat) or equivalent statistical tests and won't be able just eyeball it.
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(Note that using statistical tests is a good idea anyway.) The steps to run
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the benchmarks should be documented, and any custom scripts and tooling
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should be committed to the repository with instructions for how to run them.
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Be mindful of large benchmark suites that take a long time to run: it will
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make the development iterations slower.
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Note also that anything that can be measured can be optimized. Make sure
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you're measuring the right thing.
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The next step is to decide what you are optimizing for. If the goal is to
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improve CPU, what is an acceptable speed? Do you want to improve the current
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performance by 2x? 10x? Can you state it as "a problem of size N in less than
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time T"? Are you trying to reduce memory usage? By how much? How much slower
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is acceptable for what change in memory usage? What are you willing to give
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up in exchange for lower space requirements?
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Optimizing for service latency is a trickier proposition. Entire books have
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been written on how to performance test web servers. The primary issue is
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that for a single function, performance is fairly consistent for a
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given problem size. For webservices, you don't have a single number. A proper
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web-service benchmark suite will provide a latency distribution for a given
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reqs/second level. This talk gives a good overview of some of the issues:
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["How NOT to Measure Latency" by Gil Tene](https://youtu.be/lJ8ydIuPFeU)
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TODO: See the later section on optimizing web services
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The performance goals must be specific. You will (almost) always be able to
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make something faster. Optimizing is frequently a game of diminishing returns.
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You need to know when to stop. How much effort are you going to put into
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getting the last little bit of work. How much uglier and harder to maintain
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are you willing to make the code?
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Dan Luu's previously mentioned talk on [BitFunnel performance
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estimation](http://bitfunnel.org/strangeloop) shows an example of using rough
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calculations to determine if your target performance figures are reasonable.
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TODO: Programming Pearls has "Fermi Problems". Knowing Jeff Dean's slide helps.
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For greenfield development, you shouldn't leave all benchmarking and
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performance numbers until the end. It's easy to say "we'll fix it later", but
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if performance is really important it will be a design consideration from the
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start. Any significant architectural changes required to fix performance
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issues will be too risky near the deadline. Note that *during* development,
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the focus should be on reasonable program design, algorithms, and data
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structures. Optimizing at lower-levels of the stack should wait until later
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in the development cycle when a more complete view of the system performance
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is available. Any full-system profiles you do while the system is incomplete
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will give a skewed view of where the bottlenecks will be in the finished system.
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TODO: How to avoid/detect "Death by 1000 cuts" from poorly written software.
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Benchmarking as part of CI is hard due to noisy neighbours and even different
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CI boxes if it's just you. Hard to gate on performance metrics. A good middle
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ground is to have benchmarks run by the developer (on appropriate hardware) and
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included in the commit message for commits that specifically address
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performance. For those that are just general patches, try to catch performance
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degradations "by eye" in code review.
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TODO: how to track performance over time?
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Write code that you can benchmark. Profiling you can do on larger systems.
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Benchmarking you want to test isolated pieces. You need to be able to extract
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and setup sufficient context that benchmarks test enough and are
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representative.
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The difference between what your target is and the current performance will
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also give you an idea of where to start. If you need only a 10-20%
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performance improvement, you can probably get that with some implementation
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tweaks and smaller fixes. If you need a factor of 10x or more, then just
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replacing a multiplication with a left-shift isn't going to cut it. That's
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probably going to call for changes up and down your stack.
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Good performance work requires knowledge at many different levels, from
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system design, networking, hardware (CPU, caches, storage), algorithms,
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tuning, and debugging. With limited time and resources, consider which level
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will give the most improvement: it won't always be algorithm or program
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tuning.
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In general, optimizations should proceed from top to bottom. Optimizations at
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the system level will have more impact than expression-level ones. Make sure
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you're solving the problem at the appropriate level.
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This book is mostly going to talk about reducing CPU usage, reducing memory
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usage, and reducing latency. It's good to point out that you can very rarely
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do all three. Maybe CPU time is faster, but now your program uses more
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memory. Maybe you need to reduce memory space, but now the program will take
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longer.
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[Amdahl's Law](https://en.wikipedia.org/wiki/Amdahl%27s_law) tells us to focus
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on the bottlenecks. If you double the speed of routine that only takes 5% of
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the runtime, that's only a 2.5% speedup in total wall-clock. On the other hand,
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speeding up routine that takes 80% of the time by only 10% will improve runtime
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by almost 8%. Profiles will help identify where time is actually spent.
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When optimizing, you want to reduce the amount of work the CPU has to do.
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Quicksort is faster than bubble sort because it solves then same problem
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(sorting) in fewer steps. It's a more efficient algorithm. You've reduced the
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work the CPU needs to do in order to accomplish the same task.
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Program tuning, like compiler optimizations, will generally make only a small
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dent in the total runtime. Large wins will almost always come from an
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algorithmic change or data structure change, a fundamental shift in how your
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program is organized. Compiler technology improves, but slowly. [Proebsting's
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Law](http://proebsting.cs.arizona.edu/law.html) says compilers double in
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performance every 18 *years*, a stark contrast with the (slightly
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misunderstood interpretation) of Moore's Law that doubles processor
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performance every 18 *months*. Algorithmic improvements work at larger magnitudes.
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Algorithms for mixed integer programming [improved by a factor of 30,000
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between 1991 and 2008](https://agtb.wordpress.com/2010/12/23/progress-in-algorithms-beats-moore%E2%80%99s-law/).
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For a more concrete example, consider [this breakdown](https://medium.com/@buckhx/unwinding-uber-s-most-efficient-service-406413c5871d)
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of replacing a brute force geo-spacial algorithm described in an Uber blog post with
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more specialized one more suited to the presented task. There is no compiler switch
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that will give you an equivalent boost in performance.
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TODO: Optimizing floating point FFT and MMM algorithm differences in gttse07.pdf
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A profiler might show you that lots of time is spent in a particular routine.
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It could be this is an expensive routine, or it could be a cheap routine that
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is just called many many times. Rather than immediately trying to speed up
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that one routine, see if you can reduce the number of times it's called or
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eliminate it completely. We'll discuss more concrete optimization strategies
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in the next section.
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The Three Optimization Questions:
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* Do we have to do this at all? The fastest code is the code that's never run.
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* If yes, is this the best algorithm.
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* If yes, is this the best *implementation* of this algorithm.
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## Concrete optimization tips
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Jon Bentley's 1982 work "Writing Efficient Programs" approached program
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optimization as an engineering problem: Benchmark. Analyze. Improve. Verify.
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Iterate. A number of his tips are now done automatically by compilers. A
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programmers job is to use the transformations compilers *can't* do.
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There are summaries of the book:
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* <http://www.crowl.org/lawrence/programming/Bentley82.html>
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* <http://www.geoffprewett.com/BookReviews/WritingEfficientPrograms.html>
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and the program tuning rules:
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* <https://web.archive.org/web/20080513070949/http://www.cs.bell-labs.com/cm/cs/pearls/apprules.html>
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When thinking of changes you can make to your program, there are two basic options:
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you can either change your data or you can change your code.
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### Data Changes
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Changing your data means either adding to or altering the representation of
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the data you're processing. From a performance perspective, some of these
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will end up changing the O() associated with different aspects of the data
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structure.
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Ideas for augmenting your data structure:
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* Extra fields
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For example, store the size of a linked lists rather than iterating when asked
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for it. Or storing additional pointers to frequently needed other nodes to
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multiple searches (for example, "backwards" links in a doubly-linked list to
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make removal O(1) ). These sorts of changes are useful when the data you need
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is cheap to store and keep up-to-date.
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* Extra search indexes
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Most data structures are designed for a single type of query. If you need two
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different query types, having an additional "view" onto your data can be large
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improvement. For example, `[]struct`, referenced by ID but sometimes
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`string` -> `map[string]id` (or `*struct`).
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* Extra information about elements
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For example, a bloom filter. These need to be small and fast to not overwhelm
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the rest of the data structure.
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* If queries are expensive, add a cache.
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We're all familiar with memcache, but there are in-process caches.
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* Over the wire, the network + cost of serialization will hurt.
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* In-process caches, but now you need to worry about expiration and added GC pressure
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* Even a single item can help (logfile time parse example).
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TODO: "cache" might not even be key-value, just a pointer to where you were
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working. This can be as simple as a "search finger"
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These are all clear examples of "do less work" at the data structure level.
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They all cost space. Most of the time if you're optimizing for CPU, your
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program will use more memory. This is the classic [space-time trade-off](https://en.wikipedia.org/wiki/Space%E2%80%93time_tradeoff).
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If your program uses too much memory, it's also possible to go the other way.
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Reduce space usage in exchange for increased computation. Rather than storing
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things, calculate them every time. You can also compress the data in memory
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and decompress it on the fly when you need it.
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[Small Memory Software](https://gamehacking.org/faqs/Small_Memory_Software.pdf) is a book available
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online covering techniques for reducing the space used by your programs.
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While it was originally written targeting embedded developers, the ideas are
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applicable for programs on modern hardware dealing with huge amounts of data.
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* Rearrange your data
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Eliminate structure padding. Remove extra fields. Use a smaller data type.
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* Change to a slower data structure
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Simpler data structures frequently have lower memory requirements. For
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example, moving from a pointer-heavy tree structure to use slice and
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linear search instead.
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* Custom compression format for your data
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[]byte (snappy, gzip, lz4), floating point (go-tsz), integers (delta, xor + huffman)
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Lots of resources on compression. Do you need to inspect the data or can it stay compressed?
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Do you need random access or only streaming? Compress blocks with extra index.
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If not just in-process but written to disk, what about migration or adding/removing fields.
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You'll now be dealing with raw []byte instead of nice structured Go types.
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We will talk more about data layouts later.
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Modern computers and the memory hierarchy make the space/time trade-off less
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clear. It's very easy for lookup tables to be "far away" in memory (and
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therefore expensive to access) making it faster to just recompute a value
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every time it's needed.
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This also means that benchmarking will frequently show improvements that are
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not realized in the production system due to cache contention (e.g., lookup
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tables are in the processor cache during benchmarking but always flushed by
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"real data" when used in a real system.
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Google's [Jump Hash paper](https://arxiv.org/pdf/1406.2294.pdf) in fact
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addressed this directly, comparing performance on both a contented and
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uncontended processor cache. (See graphs 4 and 5 in the Jump Hash paper)
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TODO: how to simulate a contented cache, show incremental cost
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Another aspect to consider is data-transfer time. Generally network and disk
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access is very slow, and so being able to load a compressed chunk will be
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much faster than the extra CPU time required to decompress the data once it
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has been fetched. As always, benchmark. A binary format will generally
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be smaller and faster to parse than a text one, but at the cost of no longer
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being as human readable.
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For data transfer, move to a less chatty protocol, or augment the API to
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allow partial queries. For example, an incremental query rather than being
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forced to fetch the entire dataset each time.
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### Algorithmic Changes
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If you're not changing the data, the other main option is to change the code.
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The biggest improvement is likely to come from an algorithmic changes. This
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is the equivalent of replacing bubble sort (`O(n^2)`) with quicksort (`O(n log n)`)
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or replacing a linear scan through an array (`O(n)`) that used to be small
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with a map lookup (`O(1)`).
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This is how software becomes slow. Structures originally designed for one use
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is repurposed for something it wasn't designed for. This happens gradually.
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It's important to have an intuitive grasp of the different big-O levels.
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Choose the right data structure for your problem. You don't have to always
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shave cycles, but this just prevents dumb performance issues that might not
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be noticed until much later.
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The basic classes of complexity are:
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* O(1): a field access, array or map lookup
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Advice: don't worry about it
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* O(log n): binary search
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Advice: only a problem if it's in a loop
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* O(n): simple loop
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Advice: you're doing this all the time
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* O(n log n): divide-and-conquer, sorting
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Advice: still fairly fast
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* O(n\*m): nested loop / quadratic
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Advice: be careful and constrain your set sizes
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* Anything else between quadratic and subexponential
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Advice: don't run this on a million rows
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* O(b ^ n), O(n!): exponential and up
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Advice: good luck if you have more than a dozen or two data points
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Link: <http://bigocheatsheet.com>
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Let's say you need to search through of an unsorted set of data. "I should
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use a binary search" you think, knowing that a binary search is O(log n) which
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is faster than the O(n) linear scan. However, a binary search requires that
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the data is sorted, which means you'll need to sort it first, which will take
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O(n log n) time. If you're doing lots of searches, then the upfront cost of
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sorting will pay off. On the other hand, if you're mostly doing lookups,
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maybe having an array was the wrong choice and you'd be better off paying the
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O(1) lookup cost for a map instead.
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Choose the simplest reasonable data structure and move on. This is CS 101 for
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writing "not-slow software". This should be your default development
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mode. If you know you need random access, don't choose a linked-list.
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If you know you need in-order traversal, don't use a map.
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Requirements change and you can't always guess the future. Make a reasonable
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guess at the workload.
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<http://daslab.seas.harvard.edu/rum-conjecture/>
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Data structures for similar problems will differ in when they do a piece of
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work. A binary tree sorts a little at a time as inserts happen. A unsorted
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array is faster to insert but it's unsorted: at the end to "finalize" you
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need to do the sorting all at once.
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When writing a package to be used by others, avoid the temptation to
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optimize up front for every single use case. This will result in unreadable
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code. Data structures by design are effectively single-purpose. You can
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neither read minds nor predict the future. If a user says "Your package is
|
|
too slow for this use case", a reasonable answer might be "Then use this
|
|
other package over here". A package should "do one thing well".
|
|
|
|
Sometimes hybrid data structures will provide the performance improvement you
|
|
need. For example, by bucketing your data you can limit your search to a
|
|
single bucket. This still pays the theoretical cost of O(n), but the constant
|
|
will be smaller. We'll revisit these kinds of tweaks when we get to program
|
|
tuning.
|
|
|
|
Two things that people forget when discussion big-O notation:
|
|
|
|
One, there's a constant factor involved. Two algorithms which have the same
|
|
algorithmic complexity can have different constant factors. Imagine looping
|
|
over a list 100 times vs just looping over it once. Even though both are O(n),
|
|
one has a constant factor that's 100 times higher.
|
|
|
|
These constant factors are why even though merge sort, quicksort, and
|
|
heapsort all O(n log n), everybody uses quicksort because it's the fastest.
|
|
It has the smallest constant factor.
|
|
|
|
The second thing is that big-O only says "as n grows to infinity". It talks
|
|
about the growth trend, "As the numbers get big, this is the growth factor
|
|
that will dominate the run time." It says nothing about the actual
|
|
performance, or how it behaves with small n.
|
|
|
|
There's frequently a cut-off point below which a dumber algorithm is faster.
|
|
A nice example from the Go standard library's `sort` package. Most of the
|
|
time it's using quicksort, but it has a shell-sort pass then insertion sort
|
|
when the partition size drops below 12 elements.
|
|
|
|
For some algorithms, the constant factor might be so large that this cut-off
|
|
point may be larger than all reasonable inputs. That is, the O(n^2) algorithm
|
|
is faster than the O(n) algorithm for all inputs that you're ever likely to
|
|
deal with.
|
|
|
|
This also goes the other way: For example, choosing to use a more complicated
|
|
data structure to give you O(n) scaling instead of O(n^2), even though the
|
|
benchmarks for small inputs got slower. This also applies to most lock-free
|
|
data structures. They're generally slower in the single-threaded case but
|
|
more scalable when many threads are using it.
|
|
|
|
The memory hierarchy in modern computers confuses the issue here a little
|
|
bit, in that caches prefer the predictable access of scanning a slice to the
|
|
effectively random access of chasing a pointer. Still, it's best to begin
|
|
with a good algorithm. We will talk about this in the hardware-specific
|
|
section.
|
|
|
|
> The fight may not always go to the strongest, nor the race to the fastest,
|
|
> but that's the way to bet.
|
|
> -- <cite>Rudyard Kipling</cite>
|
|
|
|
Sometimes the best algorithm for a particular problem is not a single
|
|
algorithm, but a collection of algorithms specialized for slightly different
|
|
input classes. This "polyalgorithm" quickly detects what kind of input it
|
|
needs to deal with and then dispatches to the appropriate code path. This is
|
|
what the sorting package mentioned above does: determine the problem size and
|
|
choose a different algorithm. In addition to combining quicksort, shell sort,
|
|
and insertion sort, it also tracks recursion depth of quicksort and calls
|
|
heapsort if necessary. The `string` and `bytes` packages do something similar,
|
|
detecting and specializing for different cases. As with data compression, the
|
|
more you know about what your input looks like, the better your custom
|
|
solution can be. Even if an optimization is not always applicable,
|
|
complicating your code by determining that it's safe to use and executing
|
|
different logic can be worth it.
|
|
|
|
This also applies to subproblems your algorithm needs to solve. For example,
|
|
being able to use radix sort can have a significant impact on performance, or
|
|
using quickselect if you only need a partial sort.
|
|
|
|
Sometimes rather than specialization for your particular task, the best
|
|
approach is to abstract it into a more general problem space that has been
|
|
well-studied by researchers. Then you can apply the more general solution to
|
|
your specific problem. Mapping your problem into a domain that already has
|
|
well-researched implementations can be a significant win.
|
|
|
|
### Benchmark Inputs
|
|
|
|
Know how big each of your input sizes is likely to be in production.
|
|
|
|
Your benchmarks must use appropriately-sized inputs. As we've seen, different
|
|
algorithms make sense at different input sizes. If your expected input range
|
|
is <100, then your benchmarks should reflect that. Otherwise, choosing an
|
|
algorithm which is optimal for n=10^6 might not be the fastest.
|
|
|
|
Be able to generate representative test data. Different distributions of data
|
|
can provoke different behaviours in your algorithm: think of the classic
|
|
"quicksort is O(n^2) when the data is sorted" example. Similarly,
|
|
interpolation search is O(log log n) for uniform random data, but O(n) worst
|
|
case. Knowing what your inputs look like is the key to both representative
|
|
benchmarks and for choosing the best algorithm. If the data you're using to
|
|
test isn't representative of real workloads, you can easily end up optimizing
|
|
for one particular data set, "overfitting" your code to work best with one
|
|
specific set of inputs.
|
|
|
|
This also means your benchmark data needs to be representative of the real
|
|
world. If repeated requests are sufficiently rare, it's more expensive to
|
|
keep them around than to recompute them. If your benchmark data consists of
|
|
only the same repeated request, your cache will give an inaccurate view of
|
|
the performance.
|
|
|
|
Also note that some issues that are not apparent on your laptop might be
|
|
visible once you deploy to production and are hitting 250k reqs/second on
|
|
a 40 core server.
|
|
|
|
Writing good benchmarks can be difficult.
|
|
|
|
TODO: cases where microbenchmarks show a slow down but macro (real-world)
|
|
performance improves.
|
|
|
|
* <https://timharris.uk/misc/five-ways.pdf>
|
|
|
|
### Program Tuning
|
|
|
|
Program tuning used to be an art form, but then compilers got better. So now
|
|
it turns out that compilers can optimize straight-forward code better than
|
|
complicated code. The Go compiler still has a long way to go to match gcc and
|
|
clang, but it does mean that you need to be careful when tuning and
|
|
especially when upgrading Go versions that your code doesn't become "worse".
|
|
There are definitely cases where tweaks to work around the lack of a particular
|
|
compiler optimization became slower once the compiler was improved.
|
|
|
|
TODO: https://github.com/golang/go/commit/9eb219480e8de08d380ee052b7bff293856955f8)
|
|
|
|
If you are working around a specific runtime or compiler code generation
|
|
issue, always document your change with a link to the upstream issue. This
|
|
will allow you to quickly revisit your optimization once the bug is fixed.
|
|
|
|
Fight the temptation to cargo cult folklore-based "performance tips", or even
|
|
over-generalize from your own experience. Each performance bug needs to be
|
|
approached on its own merits. Even if something has worked previously, make
|
|
sure to profile to ensure the fix is still applicable. Your previous
|
|
work can guide you, but don't apply previous optimizations blindly.
|
|
|
|
Program tuning is an iterative process. Keep revisiting your code and seeing
|
|
what changes can be made. Ensure you're making progress at each step.
|
|
Frequently one improvement will enable others to be made. (Now that I'm not
|
|
doing A, I can simplify B by doing C instead.) This means you need to keep
|
|
looking at the entire picture and not get to obsessed with one small set of
|
|
lines.
|
|
|
|
Once you've settled on the right algorithm, program tuning is the process of
|
|
improving the implementation of that algorithm. In Big-O notation, this is
|
|
the process of reducing the constants associated with your program.
|
|
|
|
All program tuning is either making a slow thing fast, or doing a slow thing
|
|
fewer times. Algorithmic changes also fall into these categories, but we're
|
|
going to be looking at smaller changes. Exactly how you do this varies as
|
|
technologies change.
|
|
|
|
Making a slow thing fast might be replacing SHA1 or hash/fnv1 with a faster
|
|
hash function. Doing a slow thing fewer times might be saving the result of
|
|
the hash calculation of a large file so you don't have to do it multiple
|
|
times.
|
|
|
|
Keep comments. If something doesn't need to be done, explain why. Frequently
|
|
when optimizing an algorithm you'll discover steps that don't need to be
|
|
performed under some circumstances. Document them. Somebody else might think
|
|
it's a bug and needs to be put back.
|
|
|
|
> Empty programs gives the wrong answer in no time at all.
|
|
>
|
|
> It's easy to be fast if you don't have to be correct.
|
|
|
|
"Correctness" can depend on the problem. Heuristic algorithms that are
|
|
mostly-right most of the time can be fast, as can algorithms which guess and
|
|
improve allowing you to stop when you hit an acceptable limit.
|
|
|
|
Cache common cases:
|
|
|
|
* Your cache doesn't even need to be huge.
|
|
* see `time.Parse() example below; just a single value made an impact
|
|
* But beware cache invalidation, thread issues, etc.
|
|
* Random cache eviction is fast and sufficiently effective.
|
|
* Random cache insertion can limit cache to popular items with minimal logic.
|
|
* Compare cost of cache logic to cost of refetching the data.
|
|
* A large cache can increase GC pressure and keep blowing processor cache.
|
|
* At the extreme (little or no eviction, caching all requests to an expensive function) this can turn into [memoization](https://en.wikipedia.org/wiki/Memoization)
|
|
|
|
I've done experiments with a network trace for a service that showed even an optimal
|
|
cache wasn't worth it. Your expected hit ratio is important. You'll want to
|
|
export the ratio to your monitoring stack. Changing ratios will show a
|
|
shift in traffic. Then it's time to revisit the cache size or the
|
|
expiration policy.
|
|
|
|
Program tuning:
|
|
|
|
Program tuning is the art of iteratively improving a program in small steps.
|
|
Egon Elbre lays out his procedure:
|
|
|
|
* Come up with a hypothesis as to why your program is slow.
|
|
* Come up with N solutions to solve it
|
|
* Try them all and keep the fastest.
|
|
* Keep the second fastest just in case.
|
|
* Repeat.
|
|
|
|
Tunings can take many forms.
|
|
|
|
* If possible, keep the old implementation around for testing.
|
|
* If not possible, generate sufficient golden test cases to compare output to.
|
|
* "Sufficient" means including edge cases, as those are the ones likely to get
|
|
affected by tuning as you aim to improve performance in the general case.
|
|
* Exploit a mathematical identity:
|
|
* <https://github.com/golang/go/commit/ed6c6c9c11496ed8e458f6e0731103126ce60223>
|
|
* <https://gist.github.com/dgryski/67e6a7ff94c3a1add30eb26ec0ad8b0f>
|
|
* multiplication with addition
|
|
* use WolframAlpha, Maxima, sympy and similar to specialize, optimize or create lookup-tables
|
|
* (Also, https://users.ece.cmu.edu/~franzf/papers/gttse07.pdf)
|
|
* "pay only for what you use, not what you could have used"
|
|
* zero only part of an array, rather than the whole thing
|
|
* best done in tiny steps, a few statements at a time
|
|
* moving from floating point math to integer math
|
|
* or mandelbrot removing sqrt, or lttb removing abs, `a < b/c` => `a * c < b`
|
|
* cheap checks before more expensive checks:
|
|
* e.g., strcmp before regexp, (q.v., bloom filter before query)
|
|
"do expensive things fewer times"
|
|
* common cases before rare cases
|
|
i.e., avoid extra tests that always fail
|
|
* unrolling still effective: https://play.golang.org/p/6tnySwNxG6O
|
|
* code size. vs branch test overhead
|
|
* using offsets instead of slice assignment can help with bounds checks, data dependencies, and code gen (less to copy in inner loop).
|
|
* this is where pieces of Hacker's Delight fall
|
|
* consider different number representations: fixed-point, floating-point, (smaller) integers,
|
|
* fancier: integers with error accumulators (e.g. Bresenham's line and circle), multi-base numbers / redundant number systems
|
|
|
|
Many folklore performance tips for tuning rely on poorly optimizing compilers
|
|
and encourage the programmer to do these transformations by hand. Compilers
|
|
have been using shifts instead of multiplying or dividing by a power of two
|
|
for 15 years now -- nobody should be doing that by hand. Similarly, hoisting
|
|
invariant calculations out of loops, basic loop unrolling, common
|
|
sub-expression elimination and many others are all done automatically by gcc
|
|
and clang and the like. Go's compiler does many of these and continues to
|
|
improve. As always, benchmark before committing to the new version.
|
|
|
|
The transformations the compiler can't do rely on you knowing things about
|
|
the algorithm, about your input data, about invariants in your system, and
|
|
other assumptions you can make, and factoring that implicit knowledge into
|
|
removing or altering steps in the data structure.
|
|
|
|
Every optimization codifies an assumption about your data. These *must* be
|
|
documented and, even better, tested for. These assumptions are going to be
|
|
where your program crashes, slows down, or starts returning incorrect data
|
|
as the system evolves.
|
|
|
|
Program tuning improvements are cumulative. 5x 3% improvements is a 15%
|
|
improvement. Making optimizations it's worth it to think about the expected
|
|
performance improvement. Replacing a hash function with a faster one is a
|
|
constant factor improvement.
|
|
|
|
Understanding your requirements and where they can be altered can lead to
|
|
performance improvements. One issue that was presented in the \#performance
|
|
Gophers Slack channel was the amount of time that was spent creating a unique
|
|
identifier for a map of string key/value pairs. The original solution was to
|
|
extract the keys, sort them, and pass the resulting string to a hash
|
|
function. The improved solution we came up was to individually hash the
|
|
keys/values as they were added to the map, then xor all these hashes together
|
|
to create the identifier.
|
|
|
|
Here's an example of specialization.
|
|
|
|
Let's say we're processing a massive log file for a single day, and each line
|
|
begins with a time stamp.
|
|
|
|
```
|
|
Sun 4 Mar 2018 14:35:09 PST <...........................>
|
|
```
|
|
|
|
For each line, we're going to call `time.Parse()` to turn it into a epoch. If
|
|
profiling shows us `time.Parse()` is the bottleneck, we have a few options to
|
|
speed things up.
|
|
|
|
The easiest is to keep a single-item cache of the previously seen time stamp
|
|
and the associated epoch. As long as our log file has multiple lines for a single
|
|
second, this will be a win. For the case of a 10 million line log file,
|
|
this strategy reduces the number of expensive calls to `time.Parse()` from
|
|
10,000,000 to 86400 -- one for each unique second.
|
|
|
|
TODO: code example for single-item cache
|
|
|
|
Can we do more? Because we know exactly what format the timestamps are in
|
|
*and* that they all fall in a single day, we can write custom time parsing
|
|
logic that takes this into account. We can calculate the epoch for midnight,
|
|
then extract hour, minute, and second from the timestamp string -- they'll
|
|
all be in fixed offsets in the string -- and do some integer math.
|
|
|
|
TODO: code example for string offset version
|
|
|
|
In my benchmarks, this reduced the time parsing from 275ns/op to 5ns/op.
|
|
(Of course, even at 275 ns/op, you're more likely to be blocked on I/O and
|
|
not CPU for time parsing.)
|
|
|
|
The general algorithm is slow because it has to handle more cases. Your
|
|
algorithm can be faster because you know more about your problem. But the
|
|
code is more closely tied to exactly what you need. It's much more difficult
|
|
to update if the time format changes.
|
|
|
|
Optimization is specialization, and specialized code is more fragile to
|
|
change than general purpose code.
|
|
|
|
The standard library implementations need to be "fast enough" for most cases.
|
|
If you have higher performance needs you will probably need specialized
|
|
implementations.
|
|
|
|
Profile regularly to ensure to track the performance characteristics of your
|
|
system and be prepared to re-optimize as your traffic changes. Know the
|
|
limits of your system and have good metrics that allow you to predict when
|
|
you will hit those limits.
|
|
|
|
When the usage of your application changes, different pieces may become
|
|
hotspots. Revisit previous optimizations and decide if they're still worth
|
|
it, and revert to more readable code when possible. I had one system that I
|
|
had optimized process startup time with a complex set of mmap, reflect, and
|
|
unsafe. Once we changed how the system was deployed, this code was no longer
|
|
required and I replaced it with much more readable regular file operations.
|
|
|
|
### Optimization workflow summary
|
|
|
|
All optimizations should follow these steps:
|
|
|
|
1. determine your performance goals and confirm you are not meeting them
|
|
1. profile to identify the areas to improve.
|
|
* This can be CPU, heap allocations, or goroutine blocking.
|
|
1. benchmark to determine the speed up your solution will provide using
|
|
the built-in benchmarking framework (<http://golang.org/pkg/testing/>)
|
|
* Make sure you're benchmarking the right thing on your target
|
|
operating system and architecture.
|
|
1. profile again afterwards to verify the issue is gone
|
|
1. use <https://godoc.org/golang.org/x/perf/benchstat> or
|
|
<https://github.com/codahale/tinystat> to verify that a set of timings
|
|
are 'sufficiently' different for an optimization to be worth the added
|
|
code complexity.
|
|
1. use <https://github.com/tsenart/vegeta> for load testing http services
|
|
(+ other fancy ones: k6, fortio, ...)
|
|
1. make sure your latency numbers make sense
|
|
|
|
The first step is important. It tells you when and where to start optimizing.
|
|
More importantly, it also tells you when to stop. Pretty much all optimizations
|
|
add code complexity in exchange for speed. And you can *always* make code
|
|
faster. It's a balancing act.
|
|
|
|
## Tooling
|
|
|
|
### Introductory Profiling
|
|
|
|
Techniques applicable to source code in general
|
|
|
|
1. Introduction to pprof
|
|
* go tool pprof (and <https://github.com/google/pprof>)
|
|
1. Writing and running (micro)benchmarks
|
|
* profile, extract hot code to benchmark, optimize benchmark, profile.
|
|
* -cpuprofile / -memprofile / -benchmem
|
|
* 0.5 ns/op means it was optimized away -> how to avoid
|
|
* tips for writing good microbenchmarks (remove unnecessary work, but add baselines)
|
|
1. How to read it pprof output
|
|
1. What are the different pieces of the runtime that show up
|
|
* malloc, gc workers
|
|
* runtime._ExternalCode
|
|
1. Macro-benchmarks (Profiling in production)
|
|
* net/http/pprof
|
|
1. Using -base to look at differences
|
|
1. Memory options: -inuse_space, -inuse_objects, -alloc_space, -alloc_objects
|
|
1. Profiling in production; localhost+ssh tunnels, auth headers, using curl.
|
|
|
|
### Tracer
|
|
|
|
### Look at some more interesting/advanced tooling
|
|
|
|
* other tooling in /x/perf
|
|
* perf (perf2pprof)
|
|
* intel vtune / amd codexl / apple instruments
|
|
* https://godoc.org/github.com/aclements/go-perf
|
|
|
|
## Garbage Collection
|
|
|
|
You pay for memory allocation more than once. The first is obviously when you
|
|
allocate it. But you also pay every time the garbage collection runs.
|
|
|
|
> Reduce/Reuse/Recycle.
|
|
> -- <cite>@bboreham</cite>
|
|
|
|
* Stack vs. heap allocations
|
|
* What causes heap allocations?
|
|
* Understanding escape analysis (and the current limitation)
|
|
* /debug/pprof/heap , and -base
|
|
* API design to limit allocations:
|
|
* allow passing in buffers so caller can reuse rather than forcing an allocation
|
|
* you can even modify a slice in place carefully while you scan over it
|
|
* reducing pointers to reduce gc scan times
|
|
* pointer-free map keys/values
|
|
* GOGC
|
|
* buffer reuse (sync.Pool vs or custom via go-slab, etc)
|
|
* slicing vs. offset: pointer writes while GC is running need writebarrier: https://github.com/golang/go/commit/b85433975aedc2be2971093b6bbb0a7dc264c8fd
|
|
* use error variables instead of errors.New() / fmt.Errorf() at call site (performance or style? interface requires pointer, so it escapes to heap anyway)
|
|
* use structured errors to reduce allocation (pass struct value), create string at error printing time
|
|
* size classes
|
|
|
|
## Runtime and compiler
|
|
|
|
* cost of calls via interfaces (indirect calls on the CPU level)
|
|
* runtime.convT2E / runtime.convT2I
|
|
* type assertions vs. type switches
|
|
* defer
|
|
* special-case map implementations for ints, strings
|
|
* map for byte/uint16 not optimized; use a slice instead.
|
|
* You can fake a float64-optimized with math.Float{32,64}{from,}bits, but beware float equality issues
|
|
* https://github.com/dgryski/go-gk/blob/master/exact.go says 100x faster; need benchmarks
|
|
* bounds check elimination
|
|
* []byte <-> string copies, map optimizations
|
|
* two-value range will copy an array, use the slice instead:
|
|
* <https://play.golang.org/p/4b181zkB1O>
|
|
* <https://github.com/mdempsky/rangerdanger>
|
|
* use string concatenation instead of fmt.Sprintf where possible; runtime has optimized routines for it
|
|
|
|
## Unsafe
|
|
|
|
* And all the dangers that go with it
|
|
* Common uses for unsafe
|
|
* mmap'ing data files
|
|
* struct padding
|
|
* but not always sufficiently faster to justify complexity/safety cost
|
|
* but "off-heap", so ignored by gc (but so would a pointerless slice)
|
|
* need to think about serialization format: how to deal with pointers, indexing (mph, index header)
|
|
* speedy de-serialization
|
|
* string <-> slice conversion, []byte <-> []uint32, ...
|
|
* int to bool unsafe hack (but cmov) (but != 0 is also branch-free)
|
|
* padding:
|
|
- https://dave.cheney.net/2015/10/09/padding-is-hard
|
|
- http://www.catb.org/esr/structure-packing/#_go_and_rust
|
|
- https://golang.org/ref/spec#Size_and_alignment_guarantees
|
|
- https://github.com/dominikh/go-tools structlayout, structlayout-optimize
|
|
- encode struct layout via Offsetof to notice breakage from unsafe or asm
|
|
|
|
## Common gotchas with the standard library
|
|
|
|
* time.After() leaks until it fires; use t := NewTimer(); t.Stop() / t.Reset()
|
|
* Reusing HTTP connections...; ensure the body is drained (issue #?)
|
|
* rand.Int() and friends are 1) mutex protected and 2) expensive to create
|
|
* consider alternate random number generation (go-pcgr, xorshift)
|
|
* binary.Read and binary.Write use reflection and are slow; do it by hand.
|
|
* use strconv instead of fmt if possible
|
|
* ...
|
|
|
|
## Alternate implementations
|
|
|
|
Popular replacements for standard library packages:
|
|
|
|
* encoding/json -> ffjson, easyjson, etc
|
|
* net/http -> fasthttp (but incompatible API, not RFC compliant in subtle ways)
|
|
* regexp -> ragel (or other regular expression package)
|
|
* serialization
|
|
* encoding/gob -> <https://github.com/alecthomas/go_serialization_benchmarks>
|
|
* protobuf -> <https://github.com/gogo/protobuf>
|
|
* all formats have trade-offs: choose one that matches what you need
|
|
encoded space, decoding speed, language/tooling compatibility, ...
|
|
* database/sql -> has tradeoffs that affect performance
|
|
* look for drivers that don't use it: jackx/pgx, crawshaw sqlite, ...
|
|
* gccgo (benchmark!), gollvm (WIP)
|
|
* container/list: use a slice instead (almost always)
|
|
|
|
## cgo
|
|
|
|
* Performance characteristics of cgo calls
|
|
* Tricks to reduce the costs: batching
|
|
* Rules on passing pointers between Go and C
|
|
* syso files (race detector, dev.boringssl)
|
|
|
|
## Advanced Techniques
|
|
|
|
Techniques specific to the architecture running the code
|
|
|
|
* introduction to CPU caches
|
|
* performance cliffs
|
|
* building intuition around cache-lines: sizes, padding, alignment
|
|
* OS tools to view cache-misses
|
|
* maps vs. slices
|
|
* SOA vs AOS layouts: row-major vs. column major; when you have an X, do you need another X or do you need a Y?
|
|
* temporal and spacial locality: use what you have and what's nearby as much as possible
|
|
* reducing pointer chasing
|
|
* explicit memory prefetching; frequently ineffective; lack of intrinsics means function call overhead (removed from runtime)
|
|
* make the first 64-bytes of your struct count
|
|
* branch prediction
|
|
* remove branches from inner loops:
|
|
if a { for { } } else { for { } }
|
|
instead of
|
|
for { if a { } else { } }
|
|
benchmark due to branch prediction
|
|
structure to avoid branch
|
|
|
|
if i % 2 == 0 {
|
|
evens++
|
|
} else {
|
|
odds++
|
|
}
|
|
|
|
counts[i & 1] ++
|
|
"branch-free code", benchmark; not always faster, but frequently harder to read
|
|
TODO: ASCII class counts example, with benchmarks
|
|
|
|
* sorting data can help improve performance via both cache locality and branch prediction, even taking into account the time it takes to sort
|
|
* function call overhead
|
|
* reduce data copies
|
|
|
|
* Comment about Jeff Dean's 2002 numbers (plus updates)
|
|
* cpus have gotten faster, but memory hasn't kept up
|
|
|
|
## Concurrency
|
|
|
|
* Figure out which pieces can be done in parallel and which must be sequential
|
|
* goroutines are cheap, but not free.
|
|
* Optimizing multi-threaded code
|
|
* false-sharing -> pad to cache-line size
|
|
* true sharing -> sharding
|
|
* Overlap with previous section on caches and false/true sharing
|
|
* Lazy synchronization; it's expensive, so duplicating work may be cheaper
|
|
* things you can control: number of workers, batch size
|
|
|
|
You need a mutex to protect shared mutable state. If you have lots of mutex
|
|
contention, you need to either reduce the shared, or reduce the mutable. Two
|
|
ways to reduce the shared are 1) shard the locks or 2) process independently
|
|
and combine afterwards. To reduce mutable: well, make your data structure
|
|
read-only. You can also reduce the time the data needs be shared by reducing
|
|
the critical section -- hold the lock as little as needed. Sometimes a RWMutex
|
|
will be sufficient, although note that they're slower but they allow multiple
|
|
readers in.
|
|
|
|
If you're sharding the locks, be careful of shared cache-lines. You'll need to pad
|
|
to avoid cache-line ownership bouncing between processors.
|
|
|
|
var stripe [8]struct{ sync.Mutex; _ [7]uint64 } // mutex is 64-bits; padding fills the rest of the cacheline
|
|
|
|
## Assembly
|
|
|
|
* Stuff about writing assembly code for Go
|
|
* compilers improve; the bar is high
|
|
* replace as little as possible to make an impact; maintenance cost is high
|
|
* good reasons: SIMD instructions or other things outside of what Go and the compiler can provide
|
|
* very important to benchmark: improvements can be huge (10x for go-highway)
|
|
zero (go-speck), or even slower (no inlining)
|
|
* rebenchmark with new versions to see if you can delete your code yet
|
|
* TODO: link to 1.11 patches removing asm code
|
|
* always have pure-Go version (noasm build tag): testing, arm, gccgo
|
|
* brief intro to syntax
|
|
* how to type the middle dot
|
|
* calling convention
|
|
* using opcodes unsupported by the asm (asm2plan9, but this is getting rarer)
|
|
* notes about why inline assembly is hard
|
|
* all the tooling to make this easier: asmfmt, peachpy, c2goasm, ...
|
|
|
|
## Optimizing an entire service
|
|
|
|
Most of the time you won't be presented with a single CPU-bound routine.
|
|
That's the easy case. If you have a service to optimize, you need to look
|
|
at the entire system. Monitoring. Metrics. Log lots of things over time
|
|
so you can see them getting worse and so you can see the impact your
|
|
changes have in production.
|
|
|
|
tip.golang.org/doc/diagnostics.html
|
|
|
|
* references for system design: SRE Book, practical distributed system design
|
|
* extra tooling: more logging + analysis
|
|
* The two basic rules: either speed up the slow things or do them less frequently.
|
|
* distributed tracing to track bottlenecks at a higher level
|
|
* query patterns for querying a single server instead of in bulk
|
|
* your performance issues may not be your code, but you'll have to work around them anyway
|
|
|
|
## Appendix: Implementing Research Papers
|
|
|
|
Tips for implementing papers: (For `algorithm` read also `data structure`)
|
|
|
|
* Don't. Start with the obvious solution and reasonable data structures.
|
|
|
|
"Modern" algorithms tend to have lower theoretical complexities but high constant
|
|
factors and lots of implementation complexity. One of the classic examples of
|
|
this is Fibonacci heaps. They're notoriously difficult to get right and have
|
|
a huge constant factor. There has been a number of papers published comparing
|
|
different heap implementations on different workloads, and in general the 4-
|
|
or 8-ary implicit heaps consistently come out on top. And even in the cases
|
|
where Fibonacci heap should be faster (due to O(1) "decrease-key"),
|
|
experiments with Dijkstra's depth-first search algorithm show it's faster
|
|
when they use the straight heap removal and addition.
|
|
|
|
Similarly, treaps or skiplists vs. the more complex red-black or AVL trees.
|
|
On modern hardware, the "slower" algorithm may be fast enough, or even
|
|
faster.
|
|
|
|
> The fastest algorithm can frequently be replaced by one that is almost as fast and much easier to understand.
|
|
>
|
|
> Douglas W. Jones, University of Iowa
|
|
|
|
The added complexity has to be enough that the payoff is actually worth it.
|
|
Another example is cache eviction algorithms. Different algorithms can have
|
|
much higher complexity for only a small improvement in hit ratio. Of course,
|
|
you may not be able to test this until you have a working implementation and
|
|
have integrated it into your program.
|
|
|
|
Sometimes the paper will have graphs, but much like the trend towards
|
|
publishing only positive results, these will tend to be skewed in favour of
|
|
showing how good the new algorithm is.
|
|
|
|
* Choose the right paper.
|
|
* Look for the paper their algorithm claims to beat and implement that.
|
|
|
|
Frequently, earlier papers will be easier to understand and necessarily have
|
|
simpler algorithms.
|
|
|
|
Not all papers are good.
|
|
|
|
Look at the context the paper was written in. Determine assumptions about
|
|
the hardware: disk space, memory usage, etc. Some older papers make
|
|
different tradeoffs that were reasonable in the 70s or 80s but don't
|
|
necessarily apply to your use case. For example, what they determine to be
|
|
"reasonable" memory vs. disk usage tradeoffs. Memory sizes are now orders of
|
|
magnitude larger, and SSDs have altered the latency penalty for using disk.
|
|
Simiarly, some streaming algorithms are designed for router hardware, which
|
|
can make it a pain to translate into software.
|
|
|
|
Make sure the assumptions the algorithm makes about your data hold.
|
|
|
|
This will take some digging. You probably don't want to implement the
|
|
first paper you find.
|
|
|
|
* Make sure you understand the algorithm. This sounds obvious, but it will be
|
|
impossible to debug otherwise.
|
|
|
|
<https://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/paper-reading.pdf>
|
|
|
|
A good understanding may allow you to extract the key idea from the paper
|
|
and possibly apply just that to your problem, which may be simpler than
|
|
reimplementing the entire thing.
|
|
|
|
* The original paper for a data structure or algorithm isn't always the best. Later papers may have better explanations.
|
|
|
|
* Some papers release reference source code which you can compare against, but
|
|
1) academic code is almost universally terrible
|
|
2) beware licensing restrictions ("research purposes only")
|
|
3) beware bugs; edge cases, error checking, performance etc.
|
|
|
|
Also look out for other implementations on GitHub: they may have the same (or different!) bugs as yours.
|
|
|
|
Other resources on this topic:
|
|
* <https://www.youtube.com/watch?v=8eRx5Wo3xYA>
|
|
* <http://codecapsule.com/2012/01/18/how-to-implement-a-paper/>
|