Examples with TaskPartitioner org.apache.sysml.runtime.controlprogram.parfor.TaskPartitioner used on opensource projects

Example 1 with TaskPartitioner

use of org.apache.sysml.runtime.controlprogram.parfor.TaskPartitioner in project incubator-systemml by apache.

the class ParForProgramBlock method executeLocalParFor.

/**
	 * Executes the parfor locally, i.e., the parfor is realized with numThreads local threads that drive execution.
	 * This execution mode allows for arbitrary nested local parallelism and nested invocations of MR jobs. See
	 * below for details of the realization.
	 * 
	 * @param ec execution context
	 * @param itervar ?
	 * @param from ?
	 * @param to ?
	 * @param incr ?
	 * @throws DMLRuntimeException if DMLRuntimeException occurs
	 * @throws InterruptedException if InterruptedException occurs
	 */
private void executeLocalParFor(ExecutionContext ec, IntObject itervar, IntObject from, IntObject to, IntObject incr) throws DMLRuntimeException, InterruptedException {
    LOG.trace("Local Par For (multi-threaded) with degree of parallelism : " + _numThreads);
    /* Step 1) init parallel workers, task queue and threads
		 *         start threads (from now on waiting for tasks)
		 * Step 2) create tasks
		 *         put tasks into queue
		 *         mark end of task input stream
		 * Step 3) join all threads (wait for finished work)
		 * Step 4) collect results from each parallel worker
		 */
    Timing time = new Timing(true);
    int numExecutedTasks = 0;
    int numExecutedIterations = 0;
    //restrict recompilation to thread local memory
    setMemoryBudget();
    //enable runtime piggybacking if required
    if (_enableRuntimePiggybacking)
        //default piggybacking worker
        RuntimePiggybacking.start(_numThreads);
    try {
        // Step 1) init parallel workers, task queue and threads
        LocalTaskQueue<Task> queue = new LocalTaskQueue<Task>();
        Thread[] threads = new Thread[_numThreads];
        LocalParWorker[] workers = new LocalParWorker[_numThreads];
        for (int i = 0; i < _numThreads; i++) {
            //create parallel workers as (lazy) deep copies
            //including preparation of update-in-place variables
            workers[i] = createParallelWorker(_pwIDs[i], queue, ec);
            threads[i] = new Thread(workers[i]);
            threads[i].setPriority(Thread.MAX_PRIORITY);
        }
        // start threads (from now on waiting for tasks)
        for (Thread thread : threads) thread.start();
        //maintain statistics
        long tinit = (long) time.stop();
        if (DMLScript.STATISTICS)
            Statistics.incrementParForInitTime(tinit);
        if (_monitor)
            StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_PARWRK_T, tinit);
        // Step 2) create tasks 
        TaskPartitioner partitioner = createTaskPartitioner(from, to, incr);
        long numIterations = partitioner.getNumIterations();
        long numCreatedTasks = -1;
        if (USE_STREAMING_TASK_CREATION) {
            //put tasks into queue (parworker start work on first tasks while creating tasks) 
            numCreatedTasks = partitioner.createTasks(queue);
        } else {
            List<Task> tasks = partitioner.createTasks();
            numCreatedTasks = tasks.size();
            // put tasks into queue
            for (Task t : tasks) queue.enqueueTask(t);
            // mark end of task input stream
            queue.closeInput();
        }
        if (_monitor)
            StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_TASKS_T, time.stop());
        // Step 3) join all threads (wait for finished work)
        for (Thread thread : threads) thread.join();
        if (_monitor)
            StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_EXEC_T, time.stop());
        // Step 4) collecting results from each parallel worker
        //obtain results
        LocalVariableMap[] localVariables = new LocalVariableMap[_numThreads];
        for (int i = 0; i < _numThreads; i++) {
            localVariables[i] = workers[i].getVariables();
            numExecutedTasks += workers[i].getExecutedTasks();
            numExecutedIterations += workers[i].getExecutedIterations();
        }
        //consolidate results into global symbol table
        consolidateAndCheckResults(ec, numIterations, numCreatedTasks, numExecutedIterations, numExecutedTasks, localVariables);
        // Step 5) cleanup local parworkers (e.g., remove created functions)
        for (int i = 0; i < _numThreads; i++) {
            Collection<String> fnNames = workers[i].getFunctionNames();
            if (fnNames != null && !fnNames.isEmpty())
                for (String fn : fnNames) {
                    String[] parts = DMLProgram.splitFunctionKey(fn);
                    _prog.removeFunctionProgramBlock(parts[0], parts[1]);
                }
        }
        // the main thread to use the GPUContext
        if (DMLScript.USE_ACCELERATOR) {
            for (int i = 0; i < _numThreads; i++) {
                GPUContext gCtx = workers[i].getExecutionContext().getGPUContext();
                GPUContextPool.returnToPool(gCtx);
            }
            ec.setGPUContext(GPUContextPool.getFromPool());
            ec.getGPUContext().initializeThread();
        }
    } finally {
        //remove thread-local memory budget (reset to original budget)
        //(in finally to prevent error side effects for multiple scripts in one jvm)
        resetMemoryBudget();
        //disable runtime piggybacking
        if (_enableRuntimePiggybacking)
            RuntimePiggybacking.stop();
        if (_monitor) {
            StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_RESULTS_T, time.stop());
            StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMTASKS, numExecutedTasks);
            StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMITERS, numExecutedIterations);
        }
    }
}

Example 2 with TaskPartitioner

use of org.apache.sysml.runtime.controlprogram.parfor.TaskPartitioner in project incubator-systemml by apache.

the class ParForProgramBlock method executeRemoteSparkParFor.

private void executeRemoteSparkParFor(ExecutionContext ec, IntObject itervar, IntObject from, IntObject to, IntObject incr) throws DMLRuntimeException {
    Timing time = (_monitor ? new Timing(true) : null);
    // Step 0) check and compile to CP (if forced remote parfor)
    boolean flagForced = false;
    if (FORCE_CP_ON_REMOTE_MR && (_optMode == POptMode.NONE || (_optMode == POptMode.CONSTRAINED && _execMode == PExecMode.REMOTE_SPARK))) {
        //tid = 0  because replaced in remote parworker
        flagForced = checkMRAndRecompileToCP(0);
    }
    // Step 1) init parallel workers (serialize PBs)
    // NOTES: each mapper changes filenames with regard to his ID as we submit a single job,
    //        cannot reuse serialized string, since variables are serialized as well.
    ParForBody body = new ParForBody(_childBlocks, _resultVars, ec);
    HashMap<String, byte[]> clsMap = new HashMap<String, byte[]>();
    String program = ProgramConverter.serializeParForBody(body, clsMap);
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_PARWRK_T, time.stop());
    // Step 2) create tasks 
    TaskPartitioner partitioner = createTaskPartitioner(from, to, incr);
    long numIterations = partitioner.getNumIterations();
    //sequentially create tasks as input to parfor job
    List<Task> tasks = partitioner.createTasks();
    long numCreatedTasks = tasks.size();
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_TASKS_T, time.stop());
    //write matrices to HDFS 
    exportMatricesToHDFS(ec);
    // Step 3) submit Spark parfor job (no lazy evaluation, since collect on result)
    //MatrixObject colocatedDPMatrixObj = (_colocatedDPMatrix!=null)? (MatrixObject)ec.getVariable(_colocatedDPMatrix) : null;
    RemoteParForJobReturn ret = RemoteParForSpark.runJob(_ID, program, clsMap, tasks, ec, _enableCPCaching, _numThreads);
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_EXEC_T, time.stop());
    // Step 4) collecting results from each parallel worker
    int numExecutedTasks = ret.getNumExecutedTasks();
    int numExecutedIterations = ret.getNumExecutedIterations();
    //consolidate results into global symbol table
    consolidateAndCheckResults(ec, numIterations, numCreatedTasks, numExecutedIterations, numExecutedTasks, ret.getVariables());
    if (//see step 0
    flagForced)
        releaseForcedRecompile(0);
    if (_monitor) {
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_RESULTS_T, time.stop());
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMTASKS, numExecutedTasks);
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMITERS, numExecutedIterations);
    }
}

Example 3 with TaskPartitioner

use of org.apache.sysml.runtime.controlprogram.parfor.TaskPartitioner in project incubator-systemml by apache.

the class ParForProgramBlock method executeRemoteSparkParForDP.

private void executeRemoteSparkParForDP(ExecutionContext ec, IntObject itervar, IntObject from, IntObject to, IntObject incr) throws DMLRuntimeException, IOException {
    Timing time = (_monitor ? new Timing(true) : null);
    // Step 0) check and compile to CP (if forced remote parfor)
    boolean flagForced = checkMRAndRecompileToCP(0);
    // Step 1) prepare partitioned input matrix (needs to happen before serializing the progam)
    ParForStatementBlock sb = (ParForStatementBlock) getStatementBlock();
    MatrixObject inputMatrix = ec.getMatrixObject(_colocatedDPMatrix);
    PartitionFormat inputDPF = sb.determineDataPartitionFormat(_colocatedDPMatrix);
    //mark matrix var as partitioned  
    inputMatrix.setPartitioned(inputDPF._dpf, inputDPF._N);
    // Step 2) init parallel workers (serialize PBs)
    // NOTES: each mapper changes filenames with regard to his ID as we submit a single job,
    //        cannot reuse serialized string, since variables are serialized as well.
    ParForBody body = new ParForBody(_childBlocks, _resultVars, ec);
    HashMap<String, byte[]> clsMap = new HashMap<String, byte[]>();
    String program = ProgramConverter.serializeParForBody(body, clsMap);
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_PARWRK_T, time.stop());
    // Step 3) create tasks 
    TaskPartitioner partitioner = createTaskPartitioner(from, to, incr);
    String resultFile = constructResultFileName();
    long numIterations = partitioner.getNumIterations();
    //partitioner.createTasks().size();
    long numCreatedTasks = numIterations;
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_TASKS_T, time.stop());
    //write matrices to HDFS, except DP matrix which is the input to the RemoteDPParForSpark job
    exportMatricesToHDFS(ec, _colocatedDPMatrix);
    // Step 4) submit MR job (wait for finished work)
    //TODO runtime support for binary cell partitioning 
    //OutputInfo inputOI = ((inputMatrix.getSparsity()<0.1 && inputDPF==PDataPartitionFormat.COLUMN_WISE)||
    //		              (inputMatrix.getSparsity()<0.001 && inputDPF==PDataPartitionFormat.ROW_WISE))? 
    //		             OutputInfo.BinaryCellOutputInfo : OutputInfo.BinaryBlockOutputInfo;
    OutputInfo inputOI = OutputInfo.BinaryBlockOutputInfo;
    RemoteParForJobReturn ret = RemoteDPParForSpark.runJob(_ID, itervar.getName(), _colocatedDPMatrix, program, clsMap, resultFile, inputMatrix, ec, inputDPF, inputOI, _tSparseCol, _enableCPCaching, _numThreads);
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_EXEC_T, time.stop());
    // Step 5) collecting results from each parallel worker
    int numExecutedTasks = ret.getNumExecutedTasks();
    int numExecutedIterations = ret.getNumExecutedIterations();
    //consolidate results into global symbol table
    consolidateAndCheckResults(ec, numIterations, numCreatedTasks, numExecutedIterations, numExecutedTasks, ret.getVariables());
    if (//see step 0
    flagForced)
        releaseForcedRecompile(0);
    inputMatrix.unsetPartitioned();
    if (_monitor) {
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_RESULTS_T, time.stop());
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMTASKS, numExecutedTasks);
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMITERS, numExecutedIterations);
    }
}

Example 4 with TaskPartitioner

use of org.apache.sysml.runtime.controlprogram.parfor.TaskPartitioner in project incubator-systemml by apache.

the class ParForProgramBlock method executeRemoteMRParFor.

private void executeRemoteMRParFor(ExecutionContext ec, IntObject itervar, IntObject from, IntObject to, IntObject incr) throws DMLRuntimeException, IOException {
    /* Step 0) check and recompile MR inst
		 * Step 1) serialize child PB and inst
		 * Step 2) create tasks
		 *         serialize tasks
		 * Step 3) submit MR Jobs and wait for results                        
		 * Step 4) collect results from each parallel worker
		 */
    Timing time = (_monitor ? new Timing(true) : null);
    // Step 0) check and compile to CP (if forced remote parfor)
    boolean flagForced = false;
    if (FORCE_CP_ON_REMOTE_MR && (_optMode == POptMode.NONE || (_optMode == POptMode.CONSTRAINED && _execMode == PExecMode.REMOTE_MR))) {
        //tid = 0  because replaced in remote parworker
        flagForced = checkMRAndRecompileToCP(0);
    }
    // Step 1) init parallel workers (serialize PBs)
    // NOTES: each mapper changes filenames with regard to his ID as we submit a single job,
    //        cannot reuse serialized string, since variables are serialized as well.
    ParForBody body = new ParForBody(_childBlocks, _resultVars, ec);
    String program = ProgramConverter.serializeParForBody(body);
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_PARWRK_T, time.stop());
    // Step 2) create tasks 
    TaskPartitioner partitioner = createTaskPartitioner(from, to, incr);
    String taskFile = constructTaskFileName();
    String resultFile = constructResultFileName();
    long numIterations = partitioner.getNumIterations();
    int maxDigits = (int) Math.log10(to.getLongValue()) + 1;
    long numCreatedTasks = -1;
    if (USE_STREAMING_TASK_CREATION) {
        LocalTaskQueue<Task> queue = new LocalTaskQueue<Task>();
        //put tasks into queue and start writing to taskFile
        numCreatedTasks = partitioner.createTasks(queue);
        taskFile = writeTasksToFile(taskFile, queue, maxDigits);
    } else {
        //sequentially create tasks and write to disk
        List<Task> tasks = partitioner.createTasks();
        numCreatedTasks = tasks.size();
        taskFile = writeTasksToFile(taskFile, tasks, maxDigits);
    }
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_TASKS_T, time.stop());
    //write matrices to HDFS 
    exportMatricesToHDFS(ec);
    // Step 3) submit MR job (wait for finished work)
    MatrixObject colocatedDPMatrixObj = (_colocatedDPMatrix != null) ? ec.getMatrixObject(_colocatedDPMatrix) : null;
    RemoteParForJobReturn ret = RemoteParForMR.runJob(_ID, program, taskFile, resultFile, colocatedDPMatrixObj, _enableCPCaching, _numThreads, WRITE_REPLICATION_FACTOR, MAX_RETRYS_ON_ERROR, getMinMemory(ec), (ALLOW_REUSE_MR_JVMS & _jvmReuse));
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_EXEC_T, time.stop());
    // Step 4) collecting results from each parallel worker
    int numExecutedTasks = ret.getNumExecutedTasks();
    int numExecutedIterations = ret.getNumExecutedIterations();
    //consolidate results into global symbol table
    consolidateAndCheckResults(ec, numIterations, numCreatedTasks, numExecutedIterations, numExecutedTasks, ret.getVariables());
    if (//see step 0
    flagForced)
        releaseForcedRecompile(0);
    if (_monitor) {
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_RESULTS_T, time.stop());
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMTASKS, numExecutedTasks);
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMITERS, numExecutedIterations);
    }
}

Example 5 with TaskPartitioner

use of org.apache.sysml.runtime.controlprogram.parfor.TaskPartitioner in project incubator-systemml by apache.

the class ParForProgramBlock method executeRemoteMRParForDP.

private void executeRemoteMRParForDP(ExecutionContext ec, IntObject itervar, IntObject from, IntObject to, IntObject incr) throws DMLRuntimeException, IOException {
    /* Step 0) check and recompile MR inst
		 * Step 1) serialize child PB and inst
		 * Step 2) create tasks
		 *         serialize tasks
		 * Step 3) submit MR Jobs and wait for results                        
		 * Step 4) collect results from each parallel worker
		 */
    Timing time = (_monitor ? new Timing(true) : null);
    // Step 0) check and compile to CP (if forced remote parfor)
    boolean flagForced = checkMRAndRecompileToCP(0);
    // Step 1) prepare partitioned input matrix (needs to happen before serializing the program)
    ParForStatementBlock sb = (ParForStatementBlock) getStatementBlock();
    MatrixObject inputMatrix = ec.getMatrixObject(_colocatedDPMatrix);
    PartitionFormat inputDPF = sb.determineDataPartitionFormat(_colocatedDPMatrix);
    //mark matrix var as partitioned  
    inputMatrix.setPartitioned(inputDPF._dpf, inputDPF._N);
    // Step 2) init parallel workers (serialize PBs)
    // NOTES: each mapper changes filenames with regard to his ID as we submit a single job,
    //        cannot reuse serialized string, since variables are serialized as well.
    ParForBody body = new ParForBody(_childBlocks, _resultVars, ec);
    String program = ProgramConverter.serializeParForBody(body);
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_PARWRK_T, time.stop());
    // Step 3) create tasks 
    TaskPartitioner partitioner = createTaskPartitioner(from, to, incr);
    String resultFile = constructResultFileName();
    long numIterations = partitioner.getNumIterations();
    //partitioner.createTasks().size();
    long numCreatedTasks = numIterations;
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_INIT_TASKS_T, time.stop());
    //write matrices to HDFS 
    exportMatricesToHDFS(ec);
    // Step 4) submit MR job (wait for finished work)
    OutputInfo inputOI = ((inputMatrix.getSparsity() < 0.1 && inputDPF == PartitionFormat.COLUMN_WISE) || (inputMatrix.getSparsity() < 0.001 && inputDPF == PartitionFormat.ROW_WISE)) ? OutputInfo.BinaryCellOutputInfo : OutputInfo.BinaryBlockOutputInfo;
    RemoteParForJobReturn ret = RemoteDPParForMR.runJob(_ID, itervar.getName(), _colocatedDPMatrix, program, resultFile, inputMatrix, inputDPF, inputOI, _tSparseCol, _enableCPCaching, _numThreads, _replicationDP);
    if (_monitor)
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_EXEC_T, time.stop());
    // Step 5) collecting results from each parallel worker
    int numExecutedTasks = ret.getNumExecutedTasks();
    int numExecutedIterations = ret.getNumExecutedIterations();
    //consolidate results into global symbol table
    consolidateAndCheckResults(ec, numIterations, numCreatedTasks, numExecutedIterations, numExecutedTasks, ret.getVariables());
    if (//see step 0
    flagForced)
        releaseForcedRecompile(0);
    inputMatrix.unsetPartitioned();
    if (_monitor) {
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_WAIT_RESULTS_T, time.stop());
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMTASKS, numExecutedTasks);
        StatisticMonitor.putPFStat(_ID, Stat.PARFOR_NUMITERS, numExecutedIterations);
    }
}