Luajit And Common Lisp Benchmark

LuaJIT and SBCL Benchmark


This is a quick benchmark comparing LuaJit and SBCL using the Mandelbrot set program from Debian’s benchmark site.

Software Versions

For this comparison I built both compilers from source using the tip of their Git repos as of this past Tuesday night. For SBCL I’m using commit 92951c6f4 and for LuaJIT I’m using b025b01c5b.

I’m not familiar with the LuaJIT build process, so I used the default build procedure, and ran “make” and then “sudo make install”.

For SBCL I built with the following features turned on: (:sb-show-assem :immobile-space :compact-instance-header :sb-thread :sb-futex :sb-xref-for-internals). I compiled and installed with “ –dynamic-space-size=8192” and then “sudo sh”

$> sbcl --version
$> luajit -v
LuaJIT 2.0.5 -- Copyright (C) 2005-2017 Mike Pall.


For the LuaJIT program I’m using the fastest Lua code from the benchmark site with no changes. The code can be found here.

The fastest Lisp code uses SBCL’s assembly intrinsics, so I used the second fastest program. I modified it to take a thread count argument and also tweaked the build flags to create an executable instead of a core. The code can be found here.

Test Procedure

I created two shell scripts ( and to run the benchmark. Images of size 10000 and 20000 were generated using 4 and 8 threads, and each run was repeated 8 times.

# Runs Size Threads
8 10000 4
8 20000 4
8 10000 8
8 20000 8


Size 10000, 4 Threads

LuaJIT       SBCL      
User System % CPU Total User System % CPU Total
14.13s 0.16s 283% 5.034 13.32s 0.03s 278% 4.801
14.06s 0.12s 308% 4.591 13.34s 0.02s 276% 4.833
14.11s 0.13s 291% 4.883 13.30s 0.03s 275% 4.831
14.13s 0.14s 281% 5.073 13.47s 0.02s 275% 4.903
14.14s 0.12s 310% 4.596 13.31s 0.03s 276% 4.832
13.96s 0.12s 279% 5.031 13.32s 0.01s 275% 4.831
14.10s 0.13s 282% 5.043 13.31s 0.02s 276% 4.827
14.09s 0.12s 280% 5.065 13.32s 0.02s 275% 4.833

Size 10000, 8 Threads

LuaJIT       SBCL      
User System % CPU Total User System % CPU Total
14.83s 0.11s 378% 3.944 14.73s 0.03s 358% 4.123
14.62s 0.13s 357% 4.122 14.72s 0.04s 357% 4.133
14.67s 0.14s 361% 4.097 14.72s 0.03s 354% 4.158
14.60s 0.13s 355% 4.141 14.74s 0.04s 357% 4.129
14.57s 0.10s 357% 4.111 14.77s 0.03s 354% 4.170
14.70s 0.15s 365% 4.064 14.67s 0.02s 353% 4.163
14.71s 0.16s 366% 4.058 14.72s 0.03s 356% 4.136
14.82s 0.11s 376% 3.966 14.70s 0.05s 354% 4.163

Size 20000, 4 Threads

LuaJIT       SBCL      
User System % CPU Total User System % CPU Total
59.50s 0.45s 283% 21.166 53.14s 0.07s 276% 19.271
59.53s 0.43s 285% 20.992 54.11s 0.09s 274% 19.780
59.93s 0.43s 281% 21.478 53.10s 0.08s 276% 19.266
59.35s 0.42s 290% 20.543 53.11s 0.10s 275% 19.335
59.21s 0.41s 283% 20.997 53.00s 0.06s 274% 19.364
59.28s 0.44s 303% 19.695 53.11s 0.09s 275% 19.291
59.50s 0.45s 291% 20.545 54.57s 0.07s 272% 20.066
59.62s 0.42s 295% 20.329 53.13s 0.08s 275% 19.283

Size 20000, 8 Threads

LuaJIT       SBCL      
User System % CPU Total User System % CPU Total
61.61s 0.54s 376% 16.514 58.92s 0.07s 367% 16.040
61.83s 0.46s 376% 16.537 58.67s 0.08s 354% 16.557
61.82s 0.47s 375% 16.584 58.68s 0.07s 355% 16.513
61.52s 0.47s 377% 16.433 58.78s 0.07s 356% 16.506
61.76s 0.44s 373% 16.635 58.53s 0.08s 349% 16.765
61.66s 0.48s 371% 16.722 58.69s 0.09s 352% 16.668
61.52s 0.43s 370% 16.738 58.59s 0.08s 352% 16.636
61.86s 0.47s 371% 16.763 58.72s 0.09s 355% 16.530


As far as overall time spent, SBCL and LuaJIT performed the same on this benchmark.

The “User” time and “% CPU” columns are more interesting, though. The Lisp program spends less time executing user code, but also uses less of the CPU. My interpretation of this is that SBCL generates better single threaded code, but the Lisp program’s multi-threading is less efficient than the LuaJIT program.

Static Gists With Lisp

Here’s a Common Lisp function to generate static HTML and CSS from a GitHub Gist URL. The results are printed to standard out, and can be copy/pasted into an HTML document. This is really useful when working in Emacs with a Slime REPL open.

(defun make-static-gist (gist-url) 
  "Retrieve a Gist from GitHub and format it as static CSS and HTML."
  (let* ((nl-string (format nil "~c" #\newline))
         ;; Make sure the url has ".js" on the end
         (ending (subseq gist-url (- (length gist-url) 3)))
         (js-url (if (string= ending ".js")
                     (concatenate 'string gist-url ".js")))
         ;; Fetch the embedded gist from GitHub
         (gist-data (drakma:http-request js-url)))
    ;; Read the string data
    (with-input-from-string (strm gist-data)
      (let* (
             ;; Get the CSS link
             (css-html (read-line strm))
             ;; Get the document data
             (doc-part (read-line strm))
             ;; Parse out the CSS URL and fetch it
             ;; 45 is the length of "document.write...",
             ;; 4 is the length of "\");" on the end
             (css-url (subseq css-html 45 (- (length css-html) 4)))
             (style-sheet (drakma:http-request css-url))
             ;; Remove \n and \ from the html
             (escaped-html (subseq doc-part 16 (- (length doc-part) 4)))
             (html-nl (cl-ppcre:regex-replace-all "\\\\n" escaped-html nl-string))
             (raw-html (cl-ppcre:regex-replace-all "\\" html-nl "")))
        ;; Print everything out
        (format t "~a~%" style-sheet)
        (format t "~a~%" raw-html)))))

New Github Pages Site

I haven’t really done anything with my GitHub pages site, except setup Octopress and publish one blog post. Every time I’ve wanted to add a new entry I’ve got distracted fucking with Octopress and Ruby and Jekyll and a bunch of other stuff I don’t care to learn, and ended up losing my motivation.

This time it happened again, and I’ve decided to just get rid of Octopress and Jekyll entirely and go back to raw HTML. It won’t be the trendiest, prettiest website on the internet, but at least I know how to use it, and it won’t have random shit breaking requiring an hour of tinkering to figure out which of a million Ruby gems is broken or not installed.

Gpx Library

I recently wrote a small library, called gpxtools, for processing GPX track files in Common Lisp.

It doesn’t have many features yet, but right now it can read a GPX file and compute elevation gain, elevation loss, and the total distance of all the tracks in the file.

Here is a sample usage from the REPL:

* (ql:quickload 'gpxtools)
   To load "gpxtools":
   Load 1 ASDF system:
   ; Loading "gpxtools"
* (defparameter *gpx*
    (gpxtools:read-gpx "/Users/jeremiah/gpx_tracks/precarious_climb.gpx"))
Processing track: ACTIVE LOG
* (gpxtools:summarize *gpx*)
Total elevation gain: 3567.271946242661d0 feet
Total elevation loss: -3538.885209324304d0 feet
Total elevation loss: 6.437161570619994d0 miles

So far the most challenging part of the project was the calculation of distances given points in latitude and longitude. Doing so requires converting the coordinates to the Universal Transverse Mercator coordinate system which uses meters, and then calculating the distance.

I wasn’t able to find a library to do the conversion for me, so I wrote my own, and requested it be added to QuickLisp. It looks like everything went well, and it should be in the next release.

I’m planning to add more features to the gpxtools library, and to clean up the code in a few spots.

One thing I want to do is add generic traversal code and use it to simplify functions like distance, elevation-gain, and elevation-loss that iterate over the entire GPX data structure. Another thing I want to add is the ability to split tracks at a certain location and selectively write the results back to one or more files. I’ll often start my GPS at the beginning of a hike, but forget to turn off the GPS when I finish hiking and drive away. The result is a GPX track with 10 miles of hiking and 50 miles of driving. I may even try to do the cut automatically, ending the track when it gets back to (or reasonably close to) the origin, and splitting the drive out into a second track automatically.