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https://issues.apache.org/jira/browse/SYSTEMML-1451?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15985365#comment-15985365
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Nakul Jindal commented on SYSTEMML-1451:
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Hi [~KrishnaKalyan3], which test is this? and which size, 800MB, 8GB, ...?
Also, try staying within the constraints of your hardware (2 cores, 8GB RAM).
{code}
jvm_args: -Xmx20G -Xms20g -Xmn2g
java_command: org.apache.spark.deploy.SparkSubmit --master yarn-client --conf
spark.executor.memory="-Xms50g" --conf spark.driver.memory=20G --conf
spark.akka.frameSize=128 --conf spark.driver.maxResultSize=0 --conf
spark.memory.useLegacyMode=true --conf spark.rpc.askTimeout=6000s --conf
spark.network.timeout=6000s --conf spark.executor.extraJavaOptions="-Xmn5500m"
--conf spark.yarn.executor.memoryOverhead=8250 --conf
spark.files.useFetchCache=false --conf spark.driver.extraJavaOptions=-Xms20g
-Xmn2g --num-executors 5 --executor-memory 60G --executor-cores 24
./SystemML.jar -f extractTestData.dml -exec hybrid_spark -args
my_test_data/binomial/X10k_1k_sparse my_test_data/binomial/y10k_1k_sparse
my_test_data/binomial/X10k_1k_sparse_test
my_test_data/binomial/y10k_1k_sparse_test binary
{code}
The number of executor cores, number of executors, etc. Keep them small enough
to fit on your machine.
> Automate performance testing and reporting
> ------------------------------------------
>
> Key: SYSTEMML-1451
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1451
> Project: SystemML
> Issue Type: Improvement
> Components: Infrastructure, Test
> Reporter: Nakul Jindal
> Labels: gsoc2017, mentor, performance, reporting, testing
>
> As part of a release (and in general), performance tests are run for SystemML.
> Currently, running and reporting on these performance tests are a manual
> process. There are helper scripts, but largely the process is manual.
> The aim of this GSoC 2017 project is to automate performance testing and its
> reporting.
> These are the tasks that this entails
> 1. Automate running of the performance tests, including generation of test
> data
> 2. Detect errors and report if any
> 3. Record performance benchmarking information
> 4. Automatically compare this performance to previous version to check for
> performance regressions
> 5. Automatically compare to Spark MLLib, R?, Julia?
> 6. Prepare report with all the information about failed jobs, performance
> information, perf info against other comparable projects/algorithms
> (plotted/in plain text in CSV, PDF or other common format)
> 7. Create scripts to automatically run this process on a cloud provider that
> spins up machines, runs the test, saves the reports and spins down the
> machines.
> 8. Create a web application to do this interactively without dropping down
> into a shell.
> As part of this project, the student will need to know scripting (in Bash,
> Python, etc). It may also involve changing error reporting and performance
> reporting code in SystemML.
> Rating - Medium (for the amount of work)
> Mentor - [~nakul02] (Other co-mentors will join in)
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