1️⃣🐝🏎️ The One Billion Row Challenge

Challenge blog post: https://www.morling.dev/blog/one-billion-row-challenge/
Challenge repository: https://github.com/gunnarmorling/1brc

The challenge: compute simple floating-point math over 1 billion rows. As fast as possible, without dependencies.

Implemented in standard C11 with POSIX threads (however, no SIMD). analyze.c contains the fastest implementation, while {1..7}.c contain slower versions of the same program.

I wrote up some implmentation details on my blog here: https://www.dannyvankooten.com/blog/2024/1brc/

Running the challenge

First, compile the two programs using any capable C-compiler.

make

To compile in debug mode:

DEBUG=1 make

By default, Make will attempt to find the number of threads to use from nproc or sysctl. To compile while specifying the number of threads to use explicitly:

NTHREADS=8 make

Create the measurements file with 1B rows

bin/create-sample 1000000000

This will create a 12 GB file with 1B rows named measurements.txt in your current working directory. The program to create this sample file will take a minute or two, but you only need to run it once.

Run the challenge:

time bin/analyze measurements.txt >/dev/null

real	0m1.392s
user	0m0.000s
sys	    0m0.010sys

Note: the performance difference between a warm and a hot pagecache is quite extreme. Run echo 3 > /proc/sys/vm/drop_caches to drop your pagecache, then run the program twice in a row. It's not uncommon for the second run to be well over twice as fast.

Benchmarks

Since I don't have access to a Hetzner CCX33 box, here are the reference times for the currently leading Java implementations from the official challenge when I run them on my machine.

#	Result (m:s.ms)	Implementation	Language	Submitter
?	00:01.590	link	C	Danny van Kooten
1.	00:06.131	link	21.0.1-graalce	Sam Pullara
2.	00:06.421	link	21.0.1-graalce	Roy van Rijn

Progressions

You can find the average runtime (across 5 consecutive runs) for the various states of the program below, from baseline to the final and fully optimized version. Because I have no patience, this was run on a measurements file with only 100M rows.

1.c runtime=[ 55.86 59.09 64.28 63.63 56.08 ] average=59.79s   linear-search by city name (baseline)
2.c runtime=[ 9.14 9.31 9.35 9.05 9.30 ] average=9.23s hashmap with linear probing
3.c runtime=[ 4.27 4.51 4.47 4.28 4.25 ] average=4.36s custom temperature float parser instead of strod
4.c runtime=[ 2.38 2.41 2.46 2.40 2.39 ] average=2.41s fread with 64MB chunks instead of line-by-line
5.c runtime=[ 2.13 1.99 1.99 2.00 2.05 ] average=2.03s unroll parsing of city name and generating hash
6.c runtime=[ 0.49 0.49 0.49 0.50 0.50 ] average=0.49s parallelize across 16 threads
7.c runtime=[ 0.30 0.25 0.23 0.24 0.24 ] average=0.25s mmap entire file instead of fread in chunks