Variance First

Don't get me wrong, I'm a much happier person when things are fast. Computer now boots up in 9 seconds after switching from spinning plates to solid-state? Awesome. But if the computer takes a full 60 seconds to boot up tomorrow? Not so great. When it comes to code, speeding up a function by 10 milliseconds doesn't mean much if the variance of that function is ±1000ms; 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 you can focus on improving overall time.

But you don't have to take my word for it (emphasis added in all quotes below):

In marketing materials for NASDAQ's matching engine, they specifically call out variance:

Able to consistently sustain an order rate of over 100,000 orders per second at sub-40 microsecond average latency
The Aeron message bus has this to say about performance:

Performance is the key focus. Aeron is designed to be the highest throughput with the lowest and most predictable latency possible of any messaging system
The company PolySync, which is working on autonomous vehicles, mentions why they picked their specific messaging format:

In general, high performance is almost always desirable for serialization. But in the world of autonomous vehicles, steady timing performance is even more important than peak throughput. This is because safe operation is sensitive to timing outliers. Nobody wants the system that decides when to slam on the brakes to occasionally take 100 times longer than usual to encode its commands.
Solarflare, which makes highly-specialized network hardware, points out variance as a big concern for 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.

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 performance using benchmarks (or (C) watching a lot of CppCon videos). Even then, each project cares about performance to a different degree; you may need to build an entire replica production system to accurately benchmark at nanosecond precision. Alternately, you may be content to simply avoid garbage collection 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:

Language-specific

Garbage Collection: How often does garbage collection happen? What are the impacts?

In Python, individual objects are collected if the reference count reaches 0, and each generation is collected if num_alloc - num_dealloc > gc_threshold whenever an allocation happens. The GIL is acquired for the duration of generational collection.
Java has many different collection algorithms to choose from, each with different characteristics. The default algorithms (Parallel GC in Java 8, G1 in Java 9) freeze the JVM while collecting, while more recent algorithms (ZGC and Shenandoah) are designed to keep "stop the world" to a minimum by doing collection work in parallel.

Allocation: Every language has a different way of interacting with "heap" memory, but the principle is the same; figuring out what chunks of memory are available to give to a program is complex. Allocation libraries have a great deal of sophisticated strategies to deal with this, but it's unknown how long it may take to find available space. Understanding when your language interacts with the allocator is crucial (and I wrote a guide for Rust).

Data Layout: How your data is arranged in memory matters; data-oriented design and cache locality 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 and kernel perf 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 in C# and GraalVM in Java). On the other hand, LLVM supports Profile Guided Optimization, which should bring JIT-like benefits to non-JIT languages. Benchmarking is incredibly important here.

Kernel

Code you wrote is likely 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 be aware of.

Scheduling: Set the 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 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). To handle these, the CPU must 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 is your friend for understanding when and where syscalls happen.

Signal Handling: Far less likely to be an issue, but signals do trigger a context switch if your code has a handler registered. This will be highly dependent on the application, but you can block signals if it's an issue.

Interrupts: System interrupts are how devices connected to your computer notify the CPU that something has happened. It's then up to the CPU to pause whatever program is running so the operating system can handle the interrupt. We don't want our program to be the one paused, so make sure that SMP affinity is set and the interrupts are handled on a CPU core not running the program we care about.

NUMA: While NUMA is good at making multi-cell systems transparent, there are variance implications; if the kernel moves a process across nodes, future memory accesses must wait for the controller on the original node. Use numactl to handle memory/cpu pinning.

Hardware

CPU Pipelining/Speculation: Speculative execution in modern processors gave us vulnerabilities like Spectre, but it also gave us performance improvements like branch prediction. However, there's variance involved because the CPU might mis-predict. And while the compiler knows a lot about how your CPU pipelines instructions, code can be structured to help the branch predictor.

Paging: For most systems, virtual memory is incredible. Applications live in their own worlds, and the CPU/MMU figures out the details afterward. However, there's a variance penalty associated with memory paging and caching; if you access more memory pages than the TLB can store, you'll have to wait for the page walk. Kernel perf tools are necessary to figure out if this is an issue, but techniques like huge pages can reduce TLB burdens. Alternately, running applications in a hypervisor like Jailhouse allows one to skip virtual memory entirely, but this is potentially more work than the benefits are worth.

Network Interfaces: When more than one computer is involved, variance can go up dramatically. Tuning kernel network parameters may be helpful, but modern systems more frequently opt to skip the kernel altogether with a technique called kernel bypass. This typically requires specialized hardware and custom drivers, but even industries like telecom are finding the benefits.

Networks
- Internet routing - no idea what the network path looks like, so financial firms pay big money to make sure they have straight-line connections
- Latency within the switch - cut-through vs. store-and-forward routing - https://www.networkworld.com/article/2241573/latency-and-jitter--cut-through-design-pays-off-for-arista--blade.html

Don't do unnecessary work

Don't recompute results - see the C++ template trick to go down buy/sell-specific code paths
Stack frames are not free; jumping around isn't helpful if you can inline and help out the instruction cache
Copies are not free

Miscellaneous

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

13 KiB Raw Blame History