Examples with Broadcast org.apache.spark.broadcast.Broadcast used on opensource projects

Example 16 with Broadcast

use of org.apache.spark.broadcast.Broadcast in project pyramid by cheng-li.

the class SparkCBMOptimizer method updateBinaryClassifiers.

private void updateBinaryClassifiers() {
    if (logger.isDebugEnabled()) {
        logger.debug("start updateBinaryClassifiers");
    }
    Classifier.ProbabilityEstimator[][] localBinaryClassifiers = cbm.binaryClassifiers;
    double[][] localGammasT = gammasT;
    Broadcast<MultiLabelClfDataSet> localDataSetBroadcast = dataSetBroadCast;
    Broadcast<double[][][]> localTargetsBroadcast = targetDisBroadCast;
    double localVariance = priorVarianceBinary;
    List<BinaryTask> binaryTaskList = new ArrayList<>();
    for (int k = 0; k < cbm.numComponents; k++) {
        for (int l = 0; l < cbm.numLabels; l++) {
            LogisticRegression logisticRegression = (LogisticRegression) localBinaryClassifiers[k][l];
            double[] weights = localGammasT[k];
            binaryTaskList.add(new BinaryTask(k, l, logisticRegression, weights));
        }
    }
    JavaRDD<BinaryTask> binaryTaskRDD = sparkContext.parallelize(binaryTaskList, binaryTaskList.size());
    List<BinaryTaskResult> results = binaryTaskRDD.map(binaryTask -> {
        int labelIndex = binaryTask.classIndex;
        return updateBinaryLogisticRegression(binaryTask.componentIndex, binaryTask.classIndex, binaryTask.logisticRegression, localDataSetBroadcast.value(), binaryTask.weights, localTargetsBroadcast.value()[labelIndex], localVariance);
    }).collect();
    for (BinaryTaskResult result : results) {
        cbm.binaryClassifiers[result.componentIndex][result.classIndex] = result.binaryClassifier;
    }
    //        IntStream.range(0, cbm.numComponents).forEach(this::updateBinaryClassifiers);
    if (logger.isDebugEnabled()) {
        logger.debug("finish updateBinaryClassifiers");
    }
}

Example 17 with Broadcast

use of org.apache.spark.broadcast.Broadcast in project gatk by broadinstitute.

the class HaplotypeCallerSpark method createReadShards.

/**
     * Create an RDD of {@link Shard} from an RDD of {@link GATKRead}
     * @param shardBoundariesBroadcast  broadcast of an {@link OverlapDetector} loaded with the intervals that should be used for creating ReadShards
     * @param reads Rdd of {@link GATKRead}
     * @return a Rdd of reads grouped into potentially overlapping shards
     */
private static JavaRDD<Shard<GATKRead>> createReadShards(final Broadcast<OverlapDetector<ShardBoundary>> shardBoundariesBroadcast, final JavaRDD<GATKRead> reads) {
    final JavaPairRDD<ShardBoundary, GATKRead> paired = reads.flatMapToPair(read -> {
        final Collection<ShardBoundary> overlappingShards = shardBoundariesBroadcast.value().getOverlaps(read);
        return overlappingShards.stream().map(key -> new Tuple2<>(key, read)).iterator();
    });
    final JavaPairRDD<ShardBoundary, Iterable<GATKRead>> shardsWithReads = paired.groupByKey();
    return shardsWithReads.map(shard -> new SparkReadShard(shard._1(), shard._2()));
}

Example 18 with Broadcast

use of org.apache.spark.broadcast.Broadcast in project gatk by broadinstitute.

the class FindBreakpointEvidenceSpark method handleAssemblies.

/**
     * Transform all the reads for a supplied set of template names in each interval into FASTQ records
     * for each interval, and do something with the list of FASTQ records for each interval (like write it to a file).
     */
@VisibleForTesting
static List<AlignedAssemblyOrExcuse> handleAssemblies(final JavaSparkContext ctx, final HopscotchUniqueMultiMap<String, Integer, QNameAndInterval> qNamesMultiMap, final JavaRDD<GATKRead> reads, final int nIntervals, final boolean includeMappingLocation, final boolean dumpFASTQs, final LocalAssemblyHandler localAssemblyHandler) {
    final Broadcast<HopscotchUniqueMultiMap<String, Integer, QNameAndInterval>> broadcastQNamesMultiMap = ctx.broadcast(qNamesMultiMap);
    final List<AlignedAssemblyOrExcuse> intervalDispositions = reads.mapPartitionsToPair(readItr -> new ReadsForQNamesFinder(broadcastQNamesMultiMap.value(), nIntervals, includeMappingLocation, dumpFASTQs).call(readItr).iterator(), false).combineByKey(x -> x, FindBreakpointEvidenceSpark::combineLists, FindBreakpointEvidenceSpark::combineLists, new HashPartitioner(nIntervals), false, null).map(localAssemblyHandler::apply).collect();
    broadcastQNamesMultiMap.destroy();
    BwaMemIndexSingleton.closeAllDistributedInstances(ctx);
    return intervalDispositions;
}

Example 19 with Broadcast

use of org.apache.spark.broadcast.Broadcast in project gatk-protected by broadinstitute.

the class CoverageModelWLinearOperatorSpark method operate.

@Override
public INDArray operate(@Nonnull final INDArray W_tl) throws DimensionMismatchException {
    if (W_tl.rank() != 2 || W_tl.shape()[0] != numTargets || W_tl.shape()[1] != numLatents)
        throw new DimensionMismatchException(W_tl.length(), numTargets * numLatents);
    /* Z F W */
    final long startTimeZFW = System.nanoTime();
    final INDArray Z_F_W_tl = Nd4j.create(numTargets, numLatents);
    IntStream.range(0, numLatents).parallel().forEach(li -> Z_F_W_tl.get(NDArrayIndex.all(), NDArrayIndex.point(li)).assign(F_tt.operate(W_tl.get(NDArrayIndex.all(), NDArrayIndex.point(li)))));
    Z_F_W_tl.assign(Nd4j.gemm(Z_F_W_tl, Z_ll, false, false));
    final long endTimeZFW = System.nanoTime();
    /* perform a broadcast hash join */
    final long startTimeQW = System.nanoTime();
    final Map<LinearlySpacedIndexBlock, INDArray> W_tl_map = CoverageModelSparkUtils.partitionINDArrayToMap(targetSpaceBlocks, W_tl);
    final Broadcast<Map<LinearlySpacedIndexBlock, INDArray>> W_tl_bc = ctx.broadcast(W_tl_map);
    final INDArray Q_W_tl = CoverageModelSparkUtils.assembleINDArrayBlocksFromRDD(computeRDD.mapValues(cb -> {
        final INDArray W_tl_chunk = W_tl_bc.value().get(cb.getTargetSpaceBlock());
        final INDArray Q_tll_chunk = cb.getINDArrayFromCache(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.Q_tll);
        final Collection<INDArray> W_Q_chunk = IntStream.range(0, cb.getTargetSpaceBlock().getNumElements()).parallel().mapToObj(ti -> Q_tll_chunk.get(NDArrayIndex.point(ti)).mmul(W_tl_chunk.get(NDArrayIndex.point(ti)).transpose())).collect(Collectors.toList());
        return Nd4j.vstack(W_Q_chunk);
    }), 0);
    W_tl_bc.destroy();
    //        final JavaPairRDD<LinearlySpacedIndexBlock, INDArray> W_tl_RDD = CoverageModelSparkUtils.rddFromINDArray(W_tl,
    //                targetSpaceBlocks, ctx, true);
    //        final INDArray Q_W_tl = CoverageModelSparkUtils.assembleINDArrayBlocks(
    //                computeRDD.join(W_tl_RDD).mapValues(p -> {
    //                    final CoverageModelEMComputeBlock cb = p._1;
    //                    final INDArray W_tl_chunk = p._2;
    //                    final INDArray Q_tll_chunk = cb.getINDArrayFromCache("Q_tll");
    //                    return Nd4j.vstack(IntStream.range(0, cb.getTargetSpaceBlock().getNumElements()).parallel()
    //                            .mapToObj(ti -> Q_tll_chunk.get(NDArrayIndex.point(ti)).mmul(W_tl_chunk.get(NDArrayIndex.point(ti)).transpose()))
    //                            .collect(Collectors.toList()));
    //                }), false);
    //        W_tl_RDD.unpersist();
    final long endTimeQW = System.nanoTime();
    logger.debug("Local [Z] [F] [W] timing: " + (endTimeZFW - startTimeZFW) / 1000000 + " ms");
    logger.debug("Spark [Q] [W] timing: " + (endTimeQW - startTimeQW) / 1000000 + " ms");
    return Q_W_tl.addi(Z_F_W_tl);
}

Example 20 with Broadcast

use of org.apache.spark.broadcast.Broadcast in project gatk-protected by broadinstitute.

the class CoverageModelWPreconditionerSpark method operate.

@Override
public INDArray operate(@Nonnull final INDArray W_tl) throws DimensionMismatchException {
    if (W_tl.rank() != 2 || W_tl.shape()[0] != numTargets || W_tl.shape()[1] != numLatents) {
        throw new DimensionMismatchException(W_tl.length(), numTargets * numLatents);
    }
    long startTimeRFFT = System.nanoTime();
    /* forward rfft */
    final INDArray W_kl = Nd4j.create(fftSize, numLatents);
    IntStream.range(0, numLatents).parallel().forEach(li -> W_kl.get(NDArrayIndex.all(), NDArrayIndex.point(li)).assign(Nd4j.create(F_tt.getForwardFFT(W_tl.get(NDArrayIndex.all(), NDArrayIndex.point(li))), new int[] { fftSize, 1 })));
    long endTimeRFFT = System.nanoTime();
    /* apply the preconditioner in the Fourier space */
    long startTimePrecond = System.nanoTime();
    final Map<LinearlySpacedIndexBlock, INDArray> W_kl_map = CoverageModelSparkUtils.partitionINDArrayToMap(fourierSpaceBlocks, W_kl);
    final Broadcast<Map<LinearlySpacedIndexBlock, INDArray>> W_kl_bc = ctx.broadcast(W_kl_map);
    final JavaPairRDD<LinearlySpacedIndexBlock, INDArray> preconditionedWRDD = linOpPairRDD.mapToPair(p -> {
        final INDArray W_kl_chuck = W_kl_bc.value().get(p._1);
        final INDArray linOp_chunk = p._2;
        final int blockSize = linOp_chunk.shape()[0];
        final List<INDArray> linOpWList = IntStream.range(0, blockSize).parallel().mapToObj(k -> CoverageModelEMWorkspaceMathUtils.linsolve(linOp_chunk.get(NDArrayIndex.point(k)), W_kl_chuck.get(NDArrayIndex.point(k)))).collect(Collectors.toList());
        return new Tuple2<>(p._1, Nd4j.vstack(linOpWList));
    });
    W_kl.assign(CoverageModelSparkUtils.assembleINDArrayBlocksFromRDD(preconditionedWRDD, 0));
    W_kl_bc.destroy();
    //        final JavaPairRDD<LinearlySpacedIndexBlock, INDArray> W_kl_RDD = CoverageModelSparkUtils.rddFromINDArray(W_kl,
    //                fourierSpaceBlocks, ctx, true);
    //        W_kl.assign(CoverageModelSparkUtils.assembleINDArrayBlocks(linOpPairRDD.join((W_kl_RDD))
    //                .mapValues(p -> {
    //                    final INDArray linOp = p._1;
    //                    final INDArray W = p._2;
    //                    final int blockSize = linOp.shape()[0];
    //                    final List<INDArray> linOpWList = IntStream.range(0, blockSize).parallel().mapToObj(k ->
    //                            CoverageModelEMWorkspaceMathUtils.linsolve(linOp.get(NDArrayIndex.point(k)),
    //                                    W.get(NDArrayIndex.point(k))))
    //                            .collect(Collectors.toList());
    //                    return Nd4j.vstack(linOpWList);
    //                }), false));
    //        W_kl_RDD.unpersist();
    long endTimePrecond = System.nanoTime();
    /* irfft */
    long startTimeIRFFT = System.nanoTime();
    final INDArray res = Nd4j.create(numTargets, numLatents);
    IntStream.range(0, numLatents).parallel().forEach(li -> res.get(NDArrayIndex.all(), NDArrayIndex.point(li)).assign(F_tt.getInverseFFT(W_kl.get(NDArrayIndex.all(), NDArrayIndex.point(li)))));
    long endTimeIRFFT = System.nanoTime();
    logger.debug("Local FFT timing: " + (endTimeRFFT - startTimeRFFT + endTimeIRFFT - startTimeIRFFT) / 1000000 + " ms");
    logger.debug("Spark preconditioner application timing: " + (endTimePrecond - startTimePrecond) / 1000000 + " ms");
    return res;
}