Finish the first draft

Needs editing, but looking good so far.

_drafts/writing-fast-code.md

@@ -16,7 +16,7 @@ In roughly the time it takes other computers to access [main memory 8 times](htt
 
 Having now worked in the trading industry, I can confirm the developers are mortal; I've made some simple mistakes at the very least. But more to the point, what shows up from reading public discussions is that philosophy, not technique, separates high-performance systems from everything else. Performance-critical systems don't rely on C++ optimization tricks to make code fast (though they can be useful); there's a lot more to worry about than just the code written for the project. Rather, what shows up time and again is a focus on variance, and reducing the gap between the fastest and slowest runs of the same code.
 
-Don't get me wrong, I'm a much happier person when things are fast. Booting my computer in 10 seconds using SSD's, rather than 20 seconds with spinning plates? Awesome. But if every other day it takes a full 30 seconds to boot up because my computer is feeling temperamental? Not so great. When it comes to code, speeding up a function by an average 10 milliseconds doesn't mean much if there's a 1000ms difference between your fastest and slowest runs; you simply won't know until you call the function how long it takes to complete. **High-performance systems should first optimize for time variance**. Once you're consistent at the time scale you care about, then focus on improving overall time.
+Don't get me wrong, I'm a much happier person when things are fast. Computer goes from booting in 20 seconds down to 10 because I installed a solid-state drive? Awesome. But if every other day it takes a full 30 seconds to boot up because of corrupted sectors? Not so great. When it comes to code, speeding up a function by an average 10 milliseconds doesn't mean much if there's a 1000ms difference between your fastest and slowest runs; you simply won't know until you call the function how long it takes to complete. **High-performance systems should first optimize for time variance**. Once you're consistent at the time scale you care about, then focus on improving overall time.
 
 This focus on variance shows up all the time in public discussions (emphasis added in all quotes below):
 
@@ -32,9 +32,11 @@ This focus on variance shows up all the time in public discussions (emphasis add
 - [Solarflare](https://solarflare.com/), which makes highly-specialized network hardware, points out variance as a big concern for [electronic trading](https://solarflare.com/electronic-trading/):
     > The high stakes world of electronic trading, investment banks, market makers, hedge funds and exchanges demand the **lowest possible latency and jitter** while utilizing the highest bandwidth and return on their investment.
 
+One more important thing to note: we're not discussing *total runtime*, but variance of that total runtime. For example, trading firms use [wireless networks](https://sniperinmahwah.wordpress.com/2017/06/07/network-effects-part-i/) because the speed of light through air is faster than through fiber-optic cables. However, there's still at *absolute minimum* a [~33.76 millisecond](http://tinyurl.com/y2vd7tn8) delay required to send data between, say, [Chicago and Tokyo](https://www.theice.com/market-data/connectivity-and-feeds/wireless/tokyo-chicago) (unless [neutrinos](https://arxiv.org/pdf/1203.2847.pdf) become [a thing](https://news.ycombinator.com/item?id=10979340)). If a trading system in Chicago calls the function for "send order to Tokyo" and waits for a result, there's a fundamental limit to how long that will take. The focus should be to keep variance of any additional processing time to a minimum.
+
 So how exactly does one go about looking for and eliminating performance variance? To tell the truth, I don't think a systematic answer or flow-chart exists. There's no substitute for (A) building a deep understanding of the entire technology stack, and (B) actually measuring system performance (though (C) watching a lot of [CppCon](https://www.youtube.com/channel/UCMlGfpWw-RUdWX_JbLCukXg) videos never hurt). Even then, each project cares about performance to a different degree; you may need to build an entire [replica production system](https://www.youtube.com/watch?v=NH1Tta7purM&feature=youtu.be&t=3015) to accurately benchmark at nanosecond precision. Alternately, you may be content to simply [avoid garbage collection](https://www.youtube.com/watch?v=BD9cRbxWQx8&feature=youtu.be&t=1335) in your Java code.
 
-Even though everyone has different needs, there are still common things to look for when trying to isolate variance. In no particular order, these are places to focus on when building high-performance/low-latency systems:
+Even though everyone has different needs, there are still common things to look for when trying to isolate variance. In no particular order, these are valuable places to focus on when thinking about variance in high-performance systems:
 
 ## Language-specific
 
@@ -46,15 +48,15 @@ Even though everyone has different needs, there are still common things to look
 
 **Data Layout**: How your data is arranged in memory matters; [data-oriented design](https://www.youtube.com/watch?v=yy8jQgmhbAU) and [cache locality](https://www.youtube.com/watch?v=2EWejmkKlxs&feature=youtu.be&t=1185) can have huge impacts on performance. The C family of languages (C, value types in C#, C++) and Rust all have guarantees about the shape every object takes in memory that others (like Java and Python) can't make. [Cachegrind](http://valgrind.org/docs/manual/cg-manual.html) and kernel [perf](https://perf.wiki.kernel.org/index.php/Main_Page) counters are both great for understanding how performance relates to memory layout.
 
-**Just-In-Time Compilation**: Languages that are compiled on the fly (LuaJIT, C#, Java, PyPy) are great because they optimize your program for how it's actually being used. However, there's a variance cost associated with this; the virtual machine may stop executing while it waits for translation from VM bytecode to native code. As a remedy, some languages now support ahead-of-time compilation ([CoreRT](https://github.com/dotnet/corert) in C# and [GraalVM](https://www.graalvm.org/) in Java). On the other hand, LLVM supports [Profile Guided Optimization](https://clang.llvm.org/docs/UsersManual.html#profile-guided-optimization), which should bring JIT-like benefits to non-JIT languages. Benchmarking is incredibly important here.
+**Just-In-Time Compilation**: Languages that are compiled on the fly (LuaJIT, C#, Java, PyPy) are great because they optimize your program for how it's actually being used. However, there's a variance cost associated with this because the virtual machine may stop executing while it waits for translation from VM bytecode to native code. As a remedy, some languages now support ahead-of-time compilation ([CoreRT](https://github.com/dotnet/corert) in C# and [GraalVM](https://www.graalvm.org/) in Java). On the other hand, LLVM supports [Profile Guided Optimization](https://clang.llvm.org/docs/UsersManual.html#profile-guided-optimization), which should bring JIT-like benefits to non-JIT languages. Benchmarking is incredibly important here.
 
 **Programming Tricks**: These won't make or break performance, but can be useful in specific circumstances. For example, C++ can use [templates instead of branches](https://www.youtube.com/watch?v=NH1Tta7purM&feature=youtu.be&t=1206).
 
 ## Kernel
 
-Code you wrote is almost certainly not the *only* code running on your system. There are many ways the operating system interacts with your program, from system calls to memory allocation, that are important to watch for.
+Code you wrote is almost certainly not the *only* code running on your system. There are many ways the operating system interacts with your program, from system calls to memory allocation, that are important to watch for. These are written from a Linux perspective, but Windows does typically have equivalent functionality.
 
-**Scheduling**: Set the [processor affinity](https://en.wikipedia.org/wiki/Processor_affinity) of your program, and make sure only your program can run on that processor. The kernel, by default, is free to schedule any process on any core, so it's important to reserve CPU cores exclusively for the important programs. Also, turning on [`NO_HZ`](https://github.com/torvalds/linux/blob/master/Documentation/timers/NO_HZ.txt) and turning off hyper-threading are probably good ideas.
+**Scheduling**: Set the [processor affinity](https://en.wikipedia.org/wiki/Processor_affinity) of your program (typically using [taskset](https://linux.die.net/man/1/taskset)), and make sure only your program can run on that processor. The kernel, by default, is free to schedule any process on any core, so it's important to reserve CPU cores exclusively for the important programs. Also, turning on [`NO_HZ`](https://github.com/torvalds/linux/blob/master/Documentation/timers/NO_HZ.txt) and turning off hyper-threading are probably good ideas.
 
 **System calls**: Reading from a UNIX socket? Writing to a file? In addition to not knowing how long the I/O operation takes, these all trigger expensive [system calls (syscalls)](https://en.wikipedia.org/wiki/System_call). To handle these, the CPU must [context switch](https://en.wikipedia.org/wiki/Context_switch) to the kernel, let the kernel operation complete, then context switch back to your program. We'd rather keep these to a minimum. [Strace](https://linux.die.net/man/1/strace) is your friend for understanding when and where syscalls happen.
 
@@ -74,13 +76,18 @@ Code you wrote is almost certainly not the *only* code running on your system. T
 
 ## Networks
 
-**Routing**: There's a reason financial firms are willing to pay [millions of euros](https://sniperinmahwah.wordpress.com/2019/03/26/4-les-moeres-english-version/) for rights to land and cell towers - having a straight-line connection from point A to point B means the path their data takes is incredibly simple. In contrast, there are currently 6 computers in between me and Google, but that may change at any moment that my ISP decides a more efficient route is available. Whether it's using [research-quality equipment](https://sniperinmahwah.wordpress.com/2018/05/07/shortwave-trading-part-i-the-west-chicago-tower-mystery/) or just making sure there's no data inadvertently going between data centers, routing matters.
+**Routing**: There's a reason financial firms are willing to pay [millions of euros](https://sniperinmahwah.wordpress.com/2019/03/26/4-les-moeres-english-version/) for rights to a small plot of land - having a straight-line connection from point A to point B means the path their data takes is incredibly simple. In contrast, there are currently 6 computers in between me and Google, but that may change at any moment that my ISP realizes a [more efficient route](https://en.wikipedia.org/wiki/Border_Gateway_Protocol) is available. Whether it's using [research-quality equipment](https://sniperinmahwah.wordpress.com/2018/05/07/shortwave-trading-part-i-the-west-chicago-tower-mystery/) for shortwave radio, or just making sure there's no data inadvertently going between data centers, routing matters.
 
 **Protocol**: TCP as a network protocol is awesome: guaranteed and in-order delivery, flow control, and congestion control all built in. But these attributes make the most sense when networking infrastructure is reasonably lossy. For systems that expect nearly all packets to be delivered correctly, the setup handshaking and packet acknowledgment are just overhead. Using UDP (unicast or multicast) may make sense in these contexts as it avoids the chatter needed to track connection state, and [gap-fill](https://iextrading.com/docs/IEX%20Transport%20Specification.pdf) [strategies](http://www.nasdaqtrader.com/content/technicalsupport/specifications/dataproducts/moldudp64.pdf) can handle the rest. 
 
 **Switching**: Many routers/switches handle packets by waiting for the whole packet, validating checksums, and then sending to the next device. This behavior is referred to as "store-and-forward." In variance terms, the time needed to move data between two nodes is proportional to the size of that data; the switch must "store" all data before it can calculate checksums and "forward" to the next node. With [cut-through](https://www.networkworld.com/article/2241573/latency-and-jitter--cut-through-design-pays-off-for-arista--blade.html) designs, switches will begin forwarding data as soon as they know where the destination is, checksums be damned. For communications within a datacenter, this can have a huge benefit.
 
-# Miscellaneous
+# Final Thoughts
 
-- Do you know where you care about latency? If any humans are involved, none of these tools make a difference, the humans are already too slow
-- If you benchmark, are you benchmarking in a way that's actually helpful? All the same variance rules from above apply to your benchmarks
+High-performance systems, whether military, finance, or autonomous cars, are not magical. They do require precision and attention to detail, but much of the tech is available to regular people. Interested in seeing how context switching affects performance of your benchmarks? `taskset` should be installed in all modern Linux distributions. Curious how often garbage collection triggers during a crucial operation? Your language of choice will typically expose details of its operations ([Python](https://docs.python.org/3/library/gc.html), [Java](https://www.oracle.com/technetwork/java/javase/tech/vmoptions-jsp-140102.html#DebuggingOptions)). Want to know how hard your program is stressing the TLB? Use `perf record` and look for `dtlb_load_misses.miss_causes_a_walk`.
+
+Two final caveats then. First, before attempting to apply some of the technology above to your own systems, can you first identify [where/when you care](http://wiki.c2.com/?PrematureOptimization) about "high-performance"? If there's ongoing human involvement with parts of the system, those likely don't need to worry about things like CPU pinning; humans are already far too slow to react in time.
+
+Second, if you're using benchmarks, are they being designed in a way that's actually helpful? Tools like [Criterion](http://www.serpentine.com/criterion/) (also in [Rust](https://github.com/bheisler/criterion.rs)) and Google's [Benchmark](https://github.com/google/benchmark) output not only average run time, but variance as well; your benchmarking environment is subject to the same concerns your production environment is.
+
+Finally then, I believe high-performance systems are a matter of philosophy, not necessarily technique. Rigorous focus on variance matters most, and there are plenty of ways to measure and mitigate it; once that's at an acceptable level, then optimize for speed.

_sass/components/_archives.scss

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