What I've been reading in JuneFollow @dannas_
Here are the articles, videos, and tools that I’m excited about this June.
The format of this entry is stolen from the Interrupt MemFault blogs monthly roundup posts. You should read those. They’re way better than mine!
Articles & Learning
- Prefer Consumption Metrics to Time Metrics by Rico Mariani
“Elapsed time is data I can only cry about”! Rico says that even though elapsed time is what the customers are often interested in, you can’t tell from just looking at the time why things are slow. Rico prefers consumption metrics, things like disk (read ops, write ops, total bytes, queue length), CPU (instructions retired, branch predicts, even maybe CPU time), memory (L2, TLB, page faults), network (round trips, total bytes), GPU (texture sizes, vertices, shader cost), cardinality (# of draws, # of layouts, # of renders). There are many more. These data are more sensitive and more probative. And they’re often much more stable - and therefore more useful in lab settings.
- Minimum Times Tend to Mislead When Benchmarking by Laurie Tratt
I’ve been told that noise can only slow a program down. So I should run a program which I want to measure the execution time of several times and take the minimum time from those runs since it’s closest to the “true” performance. But Laurie argues that nondeterminism is always present, be it from the influence of caches; the branch predictor; or memory layout of the process. So we should always look at the distribution of execution times rather than just blindly accepting the minimum.
- Understanding the Glibc Heap Implementation by Maria Markstedter
Maria describes the Glibc heap implementation as a prerequisite for understanding heap-based exploits such as use-after-frees, double-frees, and heap-overflows. The text has very informative illustrations and there are links to the Glibc source code if you want to understand the allocation algorithms in more detail. Finally, she has references to five Phrack heap articles. You should read Phrack! It is (was?) great!
- You’re Doing It Wrong by Poul-Henning Kamp
Poul-Henning describes how the Varnish HTTP accelerator uses a binary-heap data structure that is optimized for page usage and achieves significantly better performance compared to a regular binary-heap. He also touches on the fact that Varnish uses virtual memory as the caching layer.
- What’s Wrong with 2006 Programming by Salvatore Sanfilippo
Salvatore argues that using virtual memory for caching is not a good solution for Redis. It’s bad since the OS paging causes blocking for long periods ; you have little control over the kernels decision on what to page out; and the 4K granularity for pages does not match Redis storage format. There’s a great discussion in the comments between Poul-Henning and Salvatore. Poul-Henning argues that Salvatore’s points about VM limitations can be resolved, though he admits that the POSIX API is far from ideal and he also says that threads would overcome the problems with blocking during paging. To that, Salvatore replies that Redis is meant to be run as a cluster, with one instance per core. So Redis will stay single-threaded.
- Ad Hoc Profiling by Nicholas Nethercote
A description of a small command-line program for tallying line frequencies within text files. Nicholas has done a lot of impressive performance optimizations for the Rust compiler in recent years and he says that counts have been used for every single investigation. The blog post contains many examples of what to measure and how to interpret the results.
- Performance Matters by Emery Berger
Just comparing performance before and after a code change is not enough. The process layout can bias measurements: Link order and environment variable size have a larger impact than switching to -O3. Emery’s tool stabilizer eliminates the effect of layout and thus enables sound performance evaluation. Stabilizer compares the distributions for figuring out if changes has improved the code. But that’s not all: Emery and his team have created the coz profiler which allows a user to figure out the impact of optimization. They optimized SQLite by 25%!
- Designing a Fast, Efficient, Cache-friendly Hash Table, Step by Step by Matt Kulukundis
A presentation of Google Abseils 2-level N-way associative hash table, using Robin Hood hashing. A new hash table is constructed step-by-step by loosening the constraints of the standard library containers and optimizing for data locality.
Neat Open Source Projects
Single file implementations in C of image loaders, image writers, font text rasterizers, sound decoders and much more. Mostly written by Sean Barrett.
Jon Olicks MPEG-1 Audio Layer1 decoder. 397 LoC. No memory allocation.
Jon Olicks MPEG writer. 256 lines of code. No memory allocations.
Jon Olicks JPEG writer. 336 LoC. No memory allocations.
Jon Olicks GIF writer. 398 LoC. No memory allocations.
The dynamic memory layout randomization from the linked Emery Berger video.
The casual profiler that measures optimization potential. It measures optimization potential for serial, parallel, and asynchronous programs. See Emery Berger’s video for more information.
Ad-hoc profiling tool that tallies line frequencies within text files. See the link under the Articles section.