Arvados

h1. Pipeline Optimization

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

h2. 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":http://doc.arvados.org/api/schema/Job.html specified in the pipeline template under each job by graphing job statistics. For example: CPU usage, RAM, and Keep network traffic over time.

h3. How to install crunchstat-summary

<pre>

$ git clone https://github.com/curoverse/arvados.git

$ cd arvados/tools/crunchstat-summary/

$ python setup.py build

$ python setup.py install --user

</pre>

h3. How to use crunchstat-summary

<pre>

$ ./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)

</pre>

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.

<pre>

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

</pre>

!86538baca4ecef099d9fad76ad9c7180.png!

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

<pre>

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

</pre>

!62222dc72a51c18c15836796e91f3bc7.png!

One thing to point out here is "keep_cache utilization":http://doc.arvados.org/api/schema/Job.html, 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.

h2. Job Optimization

h3. 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.

h3. Choosing the right number of jobs

The right number of jobs depends on how versatile you want your pipeline to be. Do you want to output all your intermediate files to keep? How many checkpoints do you want your pipeline to have?

One thing to note is that 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.

h3. 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.

h3. 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.

h3. Piping between tools or writing to a temporary directory.

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":https://docs.python.org/2/library/subprocess.html. Sometimes piping is faster, sometimes it's not. You'll have to try for yourself.