From d1ed518212a1f938ac1ea78bd7d06556716026a1 Mon Sep 17 00:00:00 2001 From: Adrian Pope <apope@anl.gov> Date: Mon, 9 Nov 2020 12:51:21 -0600 Subject: [PATCH] Update README.md --- README.md | 86 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 85 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 0fb17ac..a3146d1 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,87 @@ -GenericIO - For more information, please visit the wiki: https://xgitlab.cels.anl.gov/hacc/genericio/-/wikis/home +# GenericIO + +GenericIO is a write-optimized library for writing self-describing scientific data files on large-scale parallel file systems. + +## Reference + +Habib, et al., HACC: Simulating Future Sky Surveys on State-of-the-Art Supercomputing Architectures, New Astronomy, 2015 +(http://arxiv.org/abs/1410.2805). + +## Source Code + +A source archive is available here: [genericio-20190417.tar.gz](http://www.mcs.anl.gov/~turam/genericio/genericio-20190417.tar.gz), or from git: + +```bash + git clone https://xgitlab.cels.anl.gov/hacc/genericio.git +``` +## Output file partitions (subfiles) + +If you're running on an IBM BG/Q supercomputer, then the number of subfiles (partitions) chosen is based on the I/O nodes in an automatic way. Otherwise, by default, the GenericIO library picks the number of subfiles based on a fairly-naive hostname-based hashing scheme. This works reasonably-well on small clusters, but not on larger systems. On a larger system, you might want to set these environmental variables: + +```bash + GENERICIO_PARTITIONS_USE_NAME=0 + GENERICIO_RANK_PARTITIONS=256 +``` + +Where the number of partitions (256 above) determines the number of subfiles used. If you're using a Lustre file system, for example, an optimal number of files is: + +``` + # of files * stripe count ~ # OSTs +``` + +On Titan, for example, there are 1008 OSTs, and a default stripe count of 4, so we use approximately 256 files. + +## Benchmarks + +Once you build the library and associated programs (using make), you can run, for example: + +```bash + $ mpirun -np 8 ./mpi/GenericIOBenchmarkWrite /tmp/out.gio 123456 2 + Wrote 9 variables to /tmp/out (4691036 bytes) in 0.2361s: 18.9484 MB/s +``` + +```bash + $ mpirun -np 8 ./mpi/GenericIOBenchmarkRead /tmp/out.gio + Read 9 variables from /tmp/out (4688028 bytes) in 0.223067s: 20.0426 MB/s [excluding header read] +``` + +The read benchmark always reads all of the input data. The output benchmark takes two numerical parameters, one if the number of data rows to write, and the second is a random seed (which slightly perturbs the per-rank output sizes, but not by much). Each row is 36 bytes for these benchmarks. + +The write benchmark can be passed the -c parameter to enable output compression. Both benchmarks take an optional -a parameter to request that homogeneous aggregates (i.e. "float4") be used instead of using separate arrays for each position/velocity component. + +## Python module + +The repository includes a genericio Python module that can read genericio-formatted files and return numpy arrays. This is included in the standard build. To use it, once you've built genericio, you can read genericio data as follows: + +```bash +$ export PYTHONPATH=${GENERICIO_DIR}/python +$ python +>>> import genericio +>>> genericio.gio_inspect('m000-99.fofproperties') +Number of Elements: 1691 +[data type] Variable name +--------------------------------------------- +[i 32] fof_halo_count +[i 64] fof_halo_tag +[f 32] fof_halo_mass +[f 32] fof_halo_mean_x +[f 32] fof_halo_mean_y +[f 32] fof_halo_mean_z +[f 32] fof_halo_mean_vx +[f 32] fof_halo_mean_vy +[f 32] fof_halo_mean_vz +[f 32] fof_halo_vel_disp + +(i=integer,f=floating point, number bits size) +>>> genericio.gio_read('m000-99.fofproperties','fof_halo_mass') +array([[ 4.58575588e+13], + [ 5.00464689e+13], + [ 5.07078771e+12], + ..., + [ 1.35221006e+13], + [ 5.29125710e+12], + [ 7.12849857e+12]], dtype=float32) + +``` [Click here to go to the README for the alternative python interface](new_python/README.md) \ No newline at end of file -- GitLab