-- Turns out this was consuming 30% of the UG runtime, and causing problems elsewhere.
-- Removed addMissingSamples from VariantcontextUtils, and calls to it
-- Updated VCF / BCF writers to automatically write out a diploid no call for missing samples
-- Added unit tests for this behavior in VariantContextWritersUnitTest
1) SelectVariants could throw a ReviewedStingException (one of the nasty "Bug:") ones if the user requested a sample that wasn't present in the VCF. The walker now
checks for this in the initialize() phase, and throws a more informative error if the situation is detected. If the user simply wants to subset the VCF to
all the samples requested that are actually present in the VCF, the --ALLOW_NONOVERLAPPING_COMMAND_LINE_SAMPLES flag changes this UserException to a Warning,
and does the appropriate subsetting. Added integration tests for this.
2) GenotypeLikelihoods has an unsafe method getLog10GQ(GenotypeType), which is completely broken for multi-allelic sites. I marked that method
as deprecated, and added methods that use the context of the allele ordering (either directly specified or as a VC) to retrieve the appropriate GQ, and
added a unit test to cover this case. VariantsToBinaryPed needs to dynamically calculate the GQ field sometimes (because I have some VCFs with PLs but no GQ).
-- Now prints out a single combined NanoScheduler runtime profile report across all nano schedulers in use. So now if you run with -nt 4 you'll get one combined NanoScheduler profiler across all 4 instances of the NanoScheduler within TraverseXNano.
-- I've rewritten the entire NS framework to use a producer / consumer model for input -> map and from map -> reduce. This is allowing us to scale reasonably efficiently up to 4 threads (see figure). Future work on the nano scheduler will be itemized in a separate JIRA entry.
-- Restructured the NS code for clarity. Docs everywhere.
-- This is considered version 1.0
-Off by default; engine fork isolates new code paths from old code paths,
so no integration tests change yet
-Experimental implementation is currently BROKEN due to a serious issue
involving file spans. No one can/should use the experimental features
until I've patched this issue.
-There are temporarily two independent versions of LocusIteratorByState.
Anyone changing one version should port the change to the other (if possible),
and anyone adding unit tests for one version should add the same unit tests
for the other (again, if possible). This situation will hopefully be extremely
temporary, and last only until the experimental implementation is proven.
-- Separate updating cumulative traversal metrics from printing progress. There's now an updateCumulativeMetrics function and a printProgress() that only takes a current position
-- printProgress now soles relies on the time since the last progress to decide if it will print or not. No longer uses the number of cycles, since this isn't reliable in the case of nano scheduling
-- GenomeAnalysisEngine now maintains a pointer to the master cumulative metrics. getCumulativeMetrics never returns null, which was handled in some parts of the code but not others.
-- Update all of the traversals to use the new updateCumulativeMetrics, printProgress model
-- Added progress callback to nano scheduler. Every bufferSize elements this callback is invoked, allowing us to smoothly update the progress meter in the NanoScheduler
-- Rename MapFunction to NanoSchedulerMap and the same for reduce.
-- Yes, GenomeLoc.compareTo was broken. The compareTo function only considered the contig and start position, but not the stop, when comparing genome locs.
-- Updated GenomeLoc.compareTo function to account for stop. Updated GATK code where necessary to fix resulting problems that depended on this.
-- Added unit tests to ensure that hashcode, equals, and compareTo are all correct for GenomeLocs
-- Groups inputs for each thread so that we don't have one thread execution per map() call
-- Added shutdown function
-- Documentation everywhere
-- Code cleanup
-- Extensive unittests
-- At this point I'm ready to integrate it into the engine for CPU parallel read walkers
– 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
-- 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
* No reads with Hard/Soft clips in the middle of the cigar
* No reads starting with deletions (with or without preceding clips)
* No reads ending in deletions (with or without follow-up clips)
* No reads that are fully hard or soft clipped
* No reads that have consecutive indels in the cigar (II, DD, ID or DI)
Also added systematic test for good cigars and iterative test for bad cigars.
-- Removed half-a*ssed attempt to automatically repair VCF files with bad headers, which allowed users to provide a replacement header overwriting the file's actually header on the fly. Not a good idea, really. Eric has promised to create a utility that walks through a VCF file and creates a meaningful header field based on the file's contents (if this ever becomes a priority)
-- BCF2 now determines whether it can safely write out raw genotype blocks, which is true in the case where the VCF header of the input is a complete, ordered subset of the output header. Added utilities to determine this and extensive unit tests (headerLinesAreOrderedConsistently)
-- Cleanup collapseStringList and exploreStringList for new unit tests of BCF2Utils. Fixed bug in edge case that never occurred in practice
-- VCFContigHeaderLine now provides its own key (VCFHeader.CONTIG_KEY) directly instead of requiring the user to provide it (and hoping its right)
-- More ways to access the data in VCFHeader
-- BCF2Writer uses a cache to avoid recomputing unnecessarily whether raw genotype blocks can be emitted directly into the output
-- Optimization of fullyDecodeAttributes -- attributes.size() is expensive and unnecessary. We just guess that on average we need ~10 elements for the attribute map
-- CombineVariants optimization -- filters are online HashSet but are sorted at the end by creating a TreeSet
-- makeCombinations is now makePermutations, and you can request to create the permutations with or without replacement
-- Expanded unit tests
-- Support for clean logging of results to logger
-- Refactored MyTime into AutoFormattingTime in Utils, out of TraversalEngine, for cleanliness and reuse
-- Added docs and contracts to StateMonitoringThreadFactory
-- All low-level reads throw IOException instead of catching it directly. This allows us to not try/catch in readByte, improving performance by 5% or so
-- Optimize encodeTypeDescriptor with final variables. Avoid using Math.min instead do inline comparison
-- Inlined willOverflow directly in its single use
-- VisualizeContextTree now can write out an equivalent BQSR table determined after adaptive context merging of all RG x QUAL x CONTEXT trees
-- Docs, algorithm descriptions, etc so that it makes sense what's going on
-- VisualizeContextTree should really be simplified when into a single tool that just visualize the trees when / if we decide to make adaptive contexts standard part of BQSR
-- Misc. cleaning, organization of the code (recalibation tests were in private but corresponding actual files were public)
-- We are no likely to fail with an error when reading old BCF files, rather than just giving bad results
-- Added new class BCFVersion that consolidates all of the version management of BCF
-- Previous version would count all alt alleles as present in a sample, even if only 1 were present, because of the way VariantEval subsetted VCs
-- Updated code for subsetting VCs by sample to be clearer about how it handles rederiving alleles
-- Update a few pieces of code to get previous correct behavior
-- Updated a few MD5s as now ref calls at sites in dbSNP are counted as having a comp sites, and therefore show up in known sites when Novelty strat is on (which I think is correct)
-- Walkers that used old subsetting function with true are now using clearer version that does rederive alleles by default
-- Moved most of BQSR classes (which are used throughout the codebase) to utils.recalibration. It's better in my opinion to keep commonly used code in utils, and only specialized code in walkers. As code becomes embedded throughout GATK its should be refactored to live in utils
-- Removed unncessary imports of BQSR in VQSR v3
-- Now ready to refactor QualQuantizer and unit test into a subclass of RecalDatum, refactor unit tests into RecalDatum unit tests, and generalize into hierarchical recal datum that can be used in QualQuantizer and the analysis of adaptive context covariate
-- Update PluginManager to sort the plugins and interfaces. This allows us to have a deterministic order in which the plugin classes come back, which caused BQSR integration tests to temporarily change because I moved my classes around a bit.
Christopher Hartl
Mark DePristo
David Roazen
Menachem Fromer
Ryan Poplin
Eric Banks
Mauricio Carneiro