object GpuBatchUtils
Utility class with methods for calculating various metrics about GPU memory usage prior to allocation, along with some operations with batches.
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val
OFFSET_BYTES: Int
Number of bytes per offset (32 bit)
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val
VALIDITY_BUFFER_BOUNDARY_BYTES: Int
Validity buffers are 64 byte aligned
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val
VALIDITY_BUFFER_BOUNDARY_ROWS: Int
Validity buffers are 64 byte aligned and each byte represents 8 rows
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- def calculateOffsetBufferSize(rows: Long): Long
- def calculateValidityBufferSize(rows: Long): Long
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def
concatSpillBatchesAndClose(spillBatches: Seq[SpillableColumnarBatch]): Option[SpillableColumnarBatch]
Concatenate the input batches into a single one.
Concatenate the input batches into a single one. The caller is responsible for closing the returned batch.
- spillBatches
the batches to be concatenated, will be closed after the call returns.
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the concatenated SpillableColumnarBatch or None if the input is empty.
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- def estimateGpuMemory(dataType: DataType, nullable: Boolean, rowCount: Long): Long
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def
estimateGpuMemory(schema: StructType, columnIndex: Int, rowCount: Long): Long
Estimate the amount of GPU memory a batch of rows will occupy once converted
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def
estimateGpuMemory(schema: StructType, rowCount: Long): Long
Estimate the amount of GPU memory a batch of rows will occupy once converted
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def
estimateRowCount(desiredBatchSizeBytes: Long, currentBatchSize: Long, currentBatchRowCount: Long): Int
Estimate the number of rows required to meet a batch size limit
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finalize(): Unit
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def
generateSplitIndices(rows: Long, numSplits: Int): Array[Int]
Generate indices which evenly splitting input batch
Generate indices which evenly splitting input batch
- rows
number of rows of input batch
- numSplits
desired number of splits
- returns
splitting indices
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def
minGpuMemory(dataType: DataType, nullable: Boolean, rowCount: Long): Long
Get the minimum size a column could be that matches these conditions.
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