Pipeline Optimization¶

This wiki page is designed to help users make their pipelines cost and compute efficient for production level data.

Crunchstat Summary¶

Crunchstat-summary is an arvados tool to help choose optimal configurations for arvados jobs and pipeline instances. It helps you choose runtime_constraints specified in the pipeline template under each job, as well as graph general statistics for the job, for example, CPU usage, RAM, and Keep network traffic across the duration of a job.

How to install crunchstat-summary¶

$ git clone https://github.com/curoverse/arvados.git
$ cd arvados/tools/crunchstat-summary/
$ python setup.py build
$ python setup.py install --user

How to use crunchstat-summary¶

$ ./bin/crunchstat-summary --help
usage: crunchstat-summary [-h]
                          [--job UUID | --pipeline-instance UUID | --log-file LOG_FILE]
                          [--skip-child-jobs] [--format {html,text}]
                          [--verbose]

Summarize resource usage of an Arvados Crunch job

optional arguments:
  -h, --help            show this help message and exit
  --job UUID            Look up the specified job and read its log data from
                        Keep (or from the Arvados event log, if the job is
                        still running)
  --pipeline-instance UUID
                        Summarize each component of the given pipeline
                        instance
  --log-file LOG_FILE   Read log data from a regular file
  --skip-child-jobs     Do not include stats from child jobs
  --format {html,text}  Report format
  --verbose, -v         Log more information (once for progress, twice for
                        debug)

Case study 1: A job that does bwa-aln mapping and converts to bam using samtools.

category        metric  task_max        task_max_rate   job_total
blkio:202:0     read    310334464       -       913853440
blkio:202:0     write   2567127040      -       7693406208
blkio:202:16    read    8036201472      155118884.01    4538585088
blkio:202:16    write   55502038016     0       0
blkio:202:32    read    2756608 100760.59       6717440
blkio:202:32    write   53570560        0       99514368
cpu     cpus    8       -       -
cpu     sys     1592.34 1.17    805.32
cpu     user    11061.28        7.98    4620.17
cpu     user+sys        12653.62        8.00    5425.49
mem     cache   7454289920      -       -
mem     pgmajfault      1859    -       830
mem     rss     7965265920      -       -
mem     swap    5537792 -       -
net:docker0     rx      2023609029      -       2093089079
net:docker0     tx      21404100070     -       49909181906
net:docker0     tx+rx   23427709099     -       52002270985
net:eth0        rx      44750669842     67466325.07     14233805360
net:eth0        tx      2126085781      20171074.09     3670464917
net:eth0        tx+rx   46876755623     67673532.73     17904270277
time    elapsed 949     -       1899
# Number of tasks: 3
# Max CPU time spent by a single task: 12653.62s
# Max CPU usage in a single interval: 799.88%
# Overall CPU usage: 285.70%
# Max memory used by a single task: 7.97GB
# Max network traffic in a single task: 46.88GB
# Max network speed in a single interval: 67.67MB/s
# Keep cache miss rate 0.00%
# Keep cache utilization 0.00%
#!! qr1hi-8i9sb-bzn6hzttfu9cetv max CPU usage was 800% -- try runtime_constraints "min_cores_per_node":8
#!! qr1hi-8i9sb-bzn6hzttfu9cetv max RSS was 7597 MiB -- try runtime_constraints "min_ram_mb_per_node":7782

Here, you can see the distinct computation between the bwa-aln and the samtools step. There is a plateau on CPU, so it could be worth it to try upgrading to a bigger node. For example, a 16 core node to see if the plateau is actually at 8 cpus or if it can scale higher.

Case study 2: FastQC

category    metric    task_max    task_max_rate    job_total
blkio:0:0    read    174349211138    65352499.20    174349211138
blkio:0:0    write    0    0    0
cpu    cpus    8    -    -
cpu    sys    364.95    0.17    364.95
cpu    user    17589.59    6.59    17589.59
cpu    user+sys    17954.54    6.72    17954.54
fuseops    read    1330241    498.40    1330241
fuseops    write    0    0    0
keepcache    hit    2655806    1038.00    2655806
keepcache    miss    2633    1.60    2633
keepcalls    get    2658439    1039.00    2658439
keepcalls    put    0    0    0
mem    cache    19836608512    -    -
mem    pgmajfault    19    -    19
mem    rss    1481367552    -    -
net:eth0    rx    178321    17798.40    178321
net:eth0    tx    7156    685.00    7156
net:eth0    tx+rx    185477    18483.40    185477
net:keep0    rx    175959092914    107337311.20    175959092914
net:keep0    tx    0    0    0
net:keep0    tx+rx    175959092914    107337311.20    175959092914
time    elapsed    3301    -    3301
# Number of tasks: 1
# Max CPU time spent by a single task: 17954.54s
# Max CPU usage in a single interval: 672.01%
# Overall CPU usage: 543.91%
# Max memory used by a single task: 1.48GB
# Max network traffic in a single task: 175.96GB
# Max network speed in a single interval: 107.36MB/s
# Keep cache miss rate 0.10%
# Keep cache utilization 99.09%
#!! qr1hi-8i9sb-nxqqxravvapt10h max CPU usage was 673% -- try runtime_constraints "min_cores_per_node":7
#!! qr1hi-8i9sb-nxqqxravvapt10h max RSS was 1413 MiB -- try runtime_constraints "min_ram_mb_per_node":1945

Job Optimization¶

When to write straight to keep vs staging a file in a temporary directory and uploading after.¶

In general writing straight to keep will reap benefits. If you run crunchstat-summary --html and you see keep io stopping once in a while, then youre cpu bound. If you're seeing cpu level off and keep-read or keep-write taking too long, then you're io bound.

That being said, it's very safe for a job to write to a temporary directory then spending time to write the file to keep. On the other hand, writing straight to keep would save all the compute time of writing to keep. If you have time, it's worth trying both and seeing how much time you save by doing both. Most of the time, writing straight to keep using TaskOutputDir will be the right option, but using a tmpdir is always the safe alternative.

Choosing usually depends on how your tool works with an output directory. If its reading/writing from it a lot, then it might be worth using a temporary directory on SSD rather than going through the network. If it's just treating the output directory as a space for stdout then using TaskOutputDir should work just fine.

choosing the right number of jobs¶

Each job must output a collection, so if you don't want to output a file, then you should combine commands with each other. If you want a lot of 'checkpoints' you should have a job for each command. But the downside is more outputs. One upside to having more jobs is that you can choose nodetypes for each command. For example, BWA-mem can scale a lot better than fastqc or varscan, so having a 16 core node for something that doesn't have native multithreading would be wasteful.

choosing the right number of tasks¶

max_tasks_per_node allows you to choose how many tasks you would like to run on a machine. For example, if you have a lot of small tasks that use 1 core/1GB ram, you can put multiple of those on a bigger machine. For example, 8 tasks on an 8 core machine. If you want to utilize machines better for cost savings, you should use crunchstat-summary to find out the maximum memory/cpu usage for one task, and see if you can fit more than 1 of those on a machine. One warning, however is if you do run out of RAM (some compute nodes can't swap) your process will die with an extraneous error. Sometimes the error is obvious, sometimes its a red herring.

How to optimize the number of tasks when you don't have native multithreading¶

tools like gatk have native multithreading where you pass a -t. Here, you usually want to use that threading, and choose the min_cores_per_node. You can use any number of min_tasks_per_node making sure that your tool_threading*min_tasks_per_node is <= min_cores_per_node.

tools like varscan/freebayes don't have native multithreading so you need to find a workaround. Generally, these tools have a -L/--intervals to pass in certain loci to work on. If you have a bed file you can split reads on, then you can create a new task per interval. Then, have a job merge the outputs together.

piping between tools or writing to a tmpdir.¶

Creating pipes between tools has shown to sometimes be faster than writing/reading from disk. Feel free to pipe your tools together, for example using subprocess.PIPE in the python subprocess module. Sometimes piping is faster, sometimes it's not. You'll have to try for yourself.