– Write general NanoScheduler framework in utils.threading. Test with reading via iterator from list of integers, map is int * 2, reduce is sum. Should be efficiency using resources to do sum of 2 * (sum(1 - X)).
Done!
CPU parallelism is nano threads. Pfor across read / map / reduce. Use work queue to implement.
Create general read map reduce framework in utils. Test parallelism independently before hooking up to Locus iterator
Represent explicitly the dependency graph. Scheduler should choose the work units that are ready for computation, that are marked as "completing a computation", and then finally that maximize the number of sequent available work units. May be worth measuring expected cost for read read / map / reduce unit and use it to balance the compute
As input is single threaded just need one thread to populate inputs, which runs as fast as possible on parallel pushing data to fixed size queue. Each push creates map job and links to upcoming reduce job.
Note that there's at most one thread for IO tasks, and all of the threads can contribute to CPU tasks
-- GATKRunReports contain itemized information about the numThreads used to execute the GATK, as well as the efficiency of the use of those threads to get real work done, including time spent running, waiting, blocking, and waiting for IO
-- See https://jira.broadinstitute.org/browse/GSA-506 for more details
-- Invert logic in GATKArgumentCollection to disable monitoring, not enable. That means monitoring is on by default
-- Fix testing error in unit tests
-- Rename variables in ThreadAllocation to be clearer
-- Old version StateMonitoringThreadFactory refactored into base class ThreadEfficiencyMonitor and subclass EfficiencyMonitoringThreadFactory.
-- Base class is used by LinearMicroScheduler to monitor performance of GATK in single threaded mode
-- MicroScheduler now handles management of the efficiency monitor. Includes master thread in monitor, meaning that reduce is now included for both schedulers
-- Allows us to ID (by proxy) time spent doing IO
-- Refactor StateMonitoryingThreadFactory to use it's own enum, not Thread.State
-- Reliable unit tests across mac and unix
-- See https://jira.broadinstitute.org/browse/GSA-502
-- New command line argument -mt enables thread monitoring
-- If enabled, HMS uses StateMonitoringThreadFactory to create monitored threads, and prints out an efficiency report when HMS exits, telling the user information like:
for BQSR – known to be inefficient locking
INFO 17:10:33,195 StateMonitoringThreadFactory - Number of activeThreads used: 8
INFO 17:10:33,196 StateMonitoringThreadFactory - Total runtime 90.3 m
INFO 17:10:33,196 StateMonitoringThreadFactory - Fraction of time spent blocked is 0.72 ( 64.8 m)
INFO 17:10:33,197 StateMonitoringThreadFactory - Fraction of time spent running is 0.26 ( 23.7 m)
INFO 17:10:33,197 StateMonitoringThreadFactory - Fraction of time spent waiting is 0.02 ( 112.8 s)
INFO 17:10:33,197 StateMonitoringThreadFactory - Efficiency of multi-threading: 26.19% of time spent doing productive work
for CountLoci
INFO 17:06:12,777 StateMonitoringThreadFactory - Number of activeThreads used: 8
INFO 17:06:12,777 StateMonitoringThreadFactory - Total runtime 43.5 m
INFO 17:06:12,778 StateMonitoringThreadFactory - Fraction of time spent blocked is 0.00 ( 4.2 s)
INFO 17:06:12,778 StateMonitoringThreadFactory - Fraction of time spent running is 1.00 ( 43.3 m)
INFO 17:06:12,779 StateMonitoringThreadFactory - Fraction of time spent waiting is 0.00 ( 6.0 s)
INFO 17:06:12,779 StateMonitoringThreadFactory - Efficiency of multi-threading: 99.61% of time spent doing productive work
-- For the high NT tests the total runtime may be too short to really assess nt efficiency vs. start up costs. Reworked underlying test data and intervals so that most tests run in 10-20 hrs for -nt 1.
-- For the high NT tests the total runtime may be too short to really assess nt efficiency vs. start up costs. Reworked underlying test data and intervals so that most tests run in 10-20 hrs for -nt 1.
-- Now uses new tagging capabilities so that 2.x runs will tag their logs as GATKPerformanceOverTime
-- Update bamboo runs, reverting back to gsa4 (it's slower but the results are less variable -- you were right david!).
-- Queue will incrementally now write out its jobReport.txt file whenever jobs finish running (FAIL or DONE)
-- This makes it far easier to track what's going on, or to analyze incrementally performance results coming out of Queue
-- Generally cleaned up the QJobsReporting code, creating a new clean class QJobsReporter that holds all of the information on what to do log and where to put into, which was previously scattered in QCommandLine and QJobReport
- Fix for M_Trieb's error report on the forum, and addition of integration tests to cover the walker.
- Addition of StructuralIndel as a class of variation within the VariantContext. These are for variants with a full alt allele that's >150bp in length.
- Adaptation of the MVLikelihoodRatio to work for a set of trios (takes the max over the trios of the MVLR)
- InsertSizeDistribution changed to use the new gatk report output (it was previously broken)
- RetrogeneDiscovery changed to be compatible with the new gatk report
- A maxIndelSize argument added to SelectVariants
- ByTranscriptEvaluator rewritten for cleanliness
- VariantRecalibrator modified to not exclude structural indels from recalibration if the mode is INDEL
- Documentation added to DepthOfCoverageIntegrationTest (no, don't yell at chartl ;_; )
Also sorry for the long commit history behind this that is the result of fixing merge conflicts. Because this *also* fixes a conflict (from git stash apply), for some reason I can't rebase all of them away. I'm pretty sure some of the commit notes say "this note isn't important because I'm going to rebase it anyway".
Mark DePristo
Christopher Hartl
Eric Banks
Guillermo del Angel
Menachem Fromer
Ryan Poplin