Benchmarks

The test file information are listed below:

Index	Filename	Number of Cells	Dimension	AMR	Public
1	1d_single.vlsv	20	1	No	Yes
2	bulk.2d.vlsv	6,300	1	No	Yes
3	2d_double.vlsv	51,200	2	No	Yes
4	2d_AFC.vlsv	4,612,500	2	No	Yes
5	3d_EGI.vlsv	3,966,580	3	Yes	No

Access to the public data can be found from vlsv_data.

Note

The numbers shown here are comparisons between Analysator v0.9 and Vlasiator.jl v0.9.32 running Python 3.6.9/3.9.7 and Julia 1.8.3 with the scripts perf.jl and perf.py. The timings are collected from a i5-10210U @ 1.6GHz CPU with 16 GB RAM if not specified.

Loading meta data^[1]

File Index	Julia [ms]	Python [ms]	Ratio
1	0.18	1.19	6.6
2	0.51	1.66	3.1
3	2.33	3.11	1.3
4	506	277	0.5
5	549	283	0.5

Reading DCCRG grid variables

File Index	Julia [ms]	Python [ms]	Ratio
1	0.004	0.07	17
2	0.02^[2]	0.08	4
3	0.17^[2]	0.21	1.2
4	20^[2]	23	1.1
5	11^[2]	11	1.0

Reading field solver grid variables^[3]

File Index	Size	Julia [s]	Julia, mmap [s]	Python [s]	Ratio
5	6.2 GiB Float32	18	8	56	7

Plotting 2D density contours on a uniform mesh (no GUI)

File Index	Julia [s]	Python [s]	Ratio
4	0.5	5.4	11

Plotting 2D density slices from a 3D AMR mesh (no GUI)

File Index	Julia [s]^[4]	Python [s]	Ratio
5	0.5	3.1	6.2

Static virtual satellite tracking from 3D AMR data (26G per frame, 32 MB Cell IDs) on a cluster^[5]

Frames	Julia, 1 thread [s]	Julia, 2 threads [s]	Python [s]
845	45	34	1220

1See the issue about sorting. The performance of EzXML is also not ideal: we may need to find a better XML parser in Julia.
2Vlasiator.jl can be faster if there is no conversion from Float64 to Float32. See Precision.
3The field solver grid is a regular Cartesian grid at the finest refinement level introduced after Vlasiator 5. Therefore fsgrid variables are quite large for 3D AMR runs: with limited memory (e.g. 16 GB RAM) you may encounter out-of-memory issues when reading fg_b more than once. In Vlasiator.jl, we provide the option usemmap=true for reading large arrays –- see Memory for more.
4The first time execution will be slower due to JIT compilation (which is excluded in the timing here). This is known as "Time-To-First-X" in the Julia community.
5University of Helsinki cluster Vorna with Intel Xeon E5-2697 @ 2.70GHz.