Class

ml.dmlc.mxnet

Rtc

Related Doc: package mxnet

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class Rtc extends AnyRef

This class allow you to write cuda kernel in Scala and call them with NDArray.

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Instance Constructors

  1. new Rtc(name: String, inputs: Array[(String, NDArray)], outputs: Array[(String, NDArray)], kernel: String)

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    name

    String, name of the kernel.

    inputs

    Array[(String, NDArray)], array of input names and ndarray.

    outputs

    Array[(String, NDArray)], array of output names and ndarray.

    kernel

    String, the actual kernel code. Note that this is only the body of the kernel, i.e. after { and before }. Rtc will decorate the kernel. For example, if name = "mykernel" and inputs = Array(("x", NDArray.zeros(10))) outputs = Array(("y", NDArray.zeros(10))) kernel = "y[threadIdx.x] = x[threadIdx.x];", the kernel that is compile will be: extern "C" global mykernel(float *x, float *y) { const int x_ndim = 1; const int x_dims[] = { 10 }; const int y_ndim = 1; const int y_dims[] = { 10 }; y[threadIdx.x] = x[threadIdx.x]; }

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. def dispose(): Unit

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    Free the rtc handle.

    Free the rtc handle. The object shall never be used after it is disposed.

  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def push(ins: Array[NDArray], outs: Array[NDArray], gridDims: (Int, Int, Int), blockDims: (Int, Int, Int)): Unit

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    run the kernel.

  17. final def synchronized[T0](arg0: ⇒ T0): T0

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  18. def toString(): String

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  19. final def wait(): Unit

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  20. final def wait(arg0: Long, arg1: Int): Unit

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  21. final def wait(arg0: Long): Unit

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