Pipeline Optimization¶

Overview¶

This wiki page is designed to help users make their Arvados pipelines cost and compute efficient for production level data. This page will go over Arvados best practices for making your pipeline cost and compute efficient.

Pipeline design¶

Choosing the right number of jobs¶

The right number of jobs depends on how versatile you want your pipeline to be. Specifically, how many steps do you want your pipeline to have?
Questions you mask ask yourself are:

Do I want to output all your intermediate files to keep?
How many checkpoints do I want my pipeline to have?
Do I want to do alignment and variant calling in one step? Should I separate them?

Each job must output a collection of files. If you don't want to output files from a command, you should combine multiple commands in a job. You always choose what to upload to keep, so if you don't need files later on, its best to leave it on the compute node.

If you want a lot of checkpoints you should have a job for each command. You'll be able to resume/restart work easily if any unexpected interruption happens. Also, you can choose different node types 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 multi-threading would be wasteful.

If you choose to do multiple computations in a job, you should try piping them together. 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.

An alternative option is using arvados.current_task.tmpdir to store all your intermediate files, and then only upload what you need to keep.

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 multi-threading where if you pass a -t, it will use the correct number of cores on the node. You usually want take advantage of this, and choose the min_cores_per_node that equals your threading parameter. 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. Also making sure that your node has enough RAM to allocate to all the tasks.

Tools like varscan/freebayes don't have native multi-threading 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.

Writing to keep¶

There are two ways to write your output collection to keep. Writing straight to keep ( arvados.crunch.TaskOutputDir() ) and staging a file in a temporary directory and then uploading to keep.

In general, writing straight to keep will reap more benefits. TaskOutputDir acts like a pipe, so you never have to spend node time on uploading data.
One problem though is if your job is dependent on using your output directory as a temporary space for files. If your job uses its output directory for computation, then your job will be trying to compute over a network and could become very slow. That being said, it's very safe for a job to write to a temporary directory then spending compute time uploading 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.

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 by graphing job statistics. For example: CPU usage, RAM, and Keep network traffic over time.

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)

There are two ways of using crunchstat-summary: a text view for an overall view of a job or an html page, which graphs usage over time.

Case 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 steps between the bwa-aln and the samtools step. Since there is a noticeable plateau on CPU usage for both computations, it would be worth trying to run the job on a bigger node. For example, a 16 core node to see if the computation can scale higher than 8 cores.

Another thing to note is you can also see the runtime_constraints recommendations. These recommendations are for you to set to ensure the job will be able to call the right node type and run reliably when reproduced.

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

One thing to point out here is keep_cache utilization, which can be changed using 'keep_cache_mb_per_task'. You can see keep cache utilization at 99.09%, which means its at a good point. You can try increasing this since it is almost at 100%, but it may not yield significant gains.

Another thing to note is to look at the CPU usage and keep transfer rate graphs. You should look to see if they ever mirror each other, which is a sign of a cpu bound job, or an i/o bound job. For example, if keep transfer is low but CPU usage is high, then your job is highly dependent on CPU, which means you should upgrade to a higher core node. If CPU usage is low and keep transfer is high, then you may want to increase the keep_cache_mb_per_task in order to be able to compute on more data.