| CachedReuseVariables |
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| DataPartitioner |
This is the base class for all data partitioner.
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| DataPartitionerLocal |
Partitions a given matrix into row or column partitions with a two pass-approach.
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| DataPartitionerRemoteSpark |
MR job class for submitting parfor remote partitioning MR jobs.
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| DataPartitionerRemoteSparkMapper |
NOTE: for the moment we only support binary block here
TODO extend impl for binarycell and textcell
Interface of Writable output in order to support both PairWritableBlock and PairWritableCell.
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| DataPartitionerRemoteSparkReducer |
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| LocalParWorker |
Instances of this class can be used to execute tasks in parallel.
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| LocalTaskQueue<T> |
This class provides a way of dynamic task distribution to multiple workers
in local multi-threaded environments.
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| ParForBody |
Wrapper for exchanging parfor body data structures.
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| ParWorker |
Super class for master/worker pattern implementations.
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| RemoteDPParForSpark |
TODO heavy hitter maintenance
TODO data partitioning with binarycell
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| RemoteDPParForSparkWorker |
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| RemoteParForJobReturn |
Wrapper for job return of ParFor REMOTE for transferring statistics and result symbol table.
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| RemoteParForSpark |
This class serves two purposes: (1) isolating Spark imports to enable running in
environments where no Spark libraries are available, and (2) to follow the same
structure as the parfor remote_mr job submission.
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| RemoteParForSparkWorker |
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| RemoteParForUtils |
Common functionalities for parfor workers in MR jobs.
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| ResultMerge<T extends CacheableData<?>> |
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| ResultMergeFrameLocalMemory |
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| ResultMergeLocalAutomatic |
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| ResultMergeLocalFile |
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| ResultMergeLocalMemory |
Local in-memory realization of result merge.
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| ResultMergeMatrix |
Due to independence of all iterations, any result has the following properties:
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| ResultMergeRemoteSpark |
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| ResultMergeRemoteSparkWCompare |
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| Task |
A task is a logical group of one or multiple iterations (each iteration is assigned to exactly one task).
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| TaskPartitioner |
This is the base class for all task partitioner.
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| TaskPartitionerFactoring |
This factoring task partitioner virtually iterates over the given FOR loop (from, to, incr),
creates iterations and group them to tasks.
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| TaskPartitionerFactoringCmax |
Factoring with maximum constraint (e.g., if LIX matrix out-of-core and we need
to bound the maximum number of iterations per map task -> memory bounds)
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| TaskPartitionerFactoringCmin |
Factoring with minimum constraint (e.g., if communication is expensive)
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| TaskPartitionerFactory |
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| TaskPartitionerFixedsize |
This naive task partitioner virtually iterates over the given FOR loop (from, to, incr),
creates iterations and group them to tasks according to the given task size.
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| TaskPartitionerNaive |
This static task partitioner virtually iterates over the given FOR loop (from, to, incr),
creates iterations and group them to tasks according to a task size of numIterations/numWorkers.
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| TaskPartitionerStatic |
This static task partitioner virtually iterates over the given FOR loop (from, to, incr),
creates iterations and group them to tasks according to a task size of numIterations/numWorkers.
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