ThrustRTC - Quick Start Guide

There could be a long time before ThrustRTC can be “properly” documented, with every API covered. That is not the aim of this document.

ThrustRTC is “multilingual”. There is a C++ library at its core, a wrapper layer for Python users, a wrapper layer for C# users, and a wrapper layers for Java users as well now. Therefore, documenting every detail will be a huge effort. However, thanks to Thrust, most of the core logics of the algorithms are already explained. Algorithm interfaces of ThrustRTC are following very similar logics as Thrust’s, so we can focus more on the differences here, which is mostly about the infrastructures.

Introduction

The project ThrustRTC is trying to provide a basic GPU algorithm library that can be used everywhere.

In GPU programming, templates are used to build very poweful libraries, like Thrust. However, these libraries can only be used in the language they are programmed in.

This project rewrites Thrust using a new paradigm: NVRTC + dynamic-instantiation, and tries to overcome this downside of Thrust.

ThrustRTC provides almost the same algorithms as Thrust, such as scan, sort and reduce, while making these algorithms available to non-C++ launguages.

Installation

Install from Source Code

Source code of ThrustRTC is available at:

https://github.com/fynv/ThrustRTC

The code does not actually contain any CUDA device code that need to be prebuilt, therefore CUDA SDK is not a requirement at building time.

At build time, you will only need:

• UnQLite source code, as submodule: thirdparty/unqlite
• CMake 3.x
• .Net development environment for building the C# library.

After cloning the repo from github and resolving the submodules, you can build it with CMake. Libraries for different lauguages are now built separately. The C++ library is used as a reference, not a dependency for other laungauges. In the case of C#, some pre-processing step is missing from the C# project, so building the C++ Library is neccessary.

To building C++ in windows using visual studio, you first create a folder “build_cpp”. Then you run cmake GUI to create a x64 solution using the CMakeLists.txt in the “cpp” folder. Build the solution in “release” mode then build the “INSTALL” project separately, you will get the library headers, binaries and examples in the “install” directory.

Now you can build the C# library using the project in the folder “CSharp”. Please select the “release - x64” configuration.

GitHub Release

Zip packages are available at:

https://github.com/fynv/ThrustRTC/releases

Runtime Dependencies

• CUDA driver (up-to-date)

• Shared library of NVRTC

  • Windows: nvrtc64*.dll, default location: %CUDA_PATH%/bin
  • Linux: libnvrtc.so, default location: /usr/local/cuda/lib64

  If the library is not at the default location, you need to call:

  • set_libnvrtc_path() from C++ or
  • ThrustRTC.set_libnvrtc_path() from Python
  • TRTC.set_libnvrtc_path() from C# or JAVA

  at run-time to specify the path of the library.

Context and General Kernel Launching

ThrustRTC is based on a special running context built upon NVRTC and NVIDIA graphics driver. There was actually a “Context” object. However, after a refactoring, it is now hidden behind the API as a global singleton.

There are still global functions to modify the one and the only one context. The context will evolve as the program runs. Headers and global constants can be added to the context, loaded kerenels will be cached. When user submits a kernel that is already loaded in the context, it will be used directly and will not be compiled and loaded again.

The following of this sections will talk about how to write and launch your own kernels using this context. If you are only interested in using the built-in algorithms, you can skip them an go ahead from Device Viewable Objects.

Kernel Objects

A Kernel object can be created given the following:

• Names of parameters
• Body of the kernel function represented as a string

Then it can be launched given the following:

• Grid Dimensions
• Block Dimensions
• Device Viewable Objects as arguments

Example using Kernel objects:

// C++
#include <stdio.h>
#include "TRTCContext.h"
#include "DVVector.h"

int main()
{
	TRTC_Kernel ker(
	{ "arr_in", "arr_out", "k" },
	"    size_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n"
	"    if (idx >= arr_in.size()) return;\n"
	"    arr_out[idx] = arr_in[idx]*k;\n");

	float test[5] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0 };
	DVVector dvec_in("float", 5, test);
	DVVector dvec_out("float", 5);
	DVFloat k1(10.0);
	const DeviceViewable* args_f[] = { &dvec_in, &dvec_out, &k1 };
	ker.launch( { 1, 1, 1 }, { 128, 1, 1 }, args_f);
	dvec_out.to_host(test);
	printf("%f %f %f %f %f\n", test[0], test[1], test[2], test[3], test[4]);

	return 0;
}

// C#
using System;
using ThrustRTCSharp;

namespace test_trtc
{
    class test_trtc
    {
        static void Main(string[] args)
        {
            Kernel ker = new Kernel(new String[]{ "arr_in", "arr_out", "k" }, 
@"
    size_t idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= arr_in.size()) return;
    arr_out[idx] = arr_in[idx]*k;");

            DVVector dvec_in = new DVVector(new float[] { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f });
            DVVector dvec_out = new DVVector("float", 5);
            DVFloat dv_k = new DVFloat(10.0f);
            DeviceViewable[] args_f = new DeviceViewable[] { dvec_in, dvec_out, dv_k };
            ker.launch(1, 128, args_f);
            print_array((float[])dvec_out.to_host());
        }

        static void print_array<T>(T[] arr)
        {
            foreach (var item in arr)
            {
                Console.Write(item.ToString());
                Console.Write(" ");
            }
            Console.WriteLine("");
        }
	}
}

For-Loop Objects

For-Loop is a simplified version of Kernel, specialized for 1D-cases.

A For-Loop object can be created given the following:

• Names of parameters
• Name of the iterator variable
• Body of the for loop represented as a string

Then it can be launched given the following:

• An iteration range specified by a begin/end pair, or just n
• Device Viewable Objects as arguments

Example using For-Loop objects:

// C++
#include <stdio.h>
#include "TRTCContext.h"
#include "DVVector.h"

int main()
{
	TRTC_For f({ "arr_in", "arr_out", "k" }, "idx",
		"    arr_out[idx] = arr_in[idx]*k;\n");

	float test[5] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0 };
	DVVector dvec_in("float", 5, test);
	DVVector dvec_out("float", 5);
	DVDouble k1(10.0);
	const DeviceViewable* args_f[] = { &dvec_in, &dvec_out, &k1 };
	f.launch_n(5, args_f);
	dvec_out.to_host(test);
	printf("%f %f %f %f %f\n", test[0], test[1], test[2], test[3], test[4]);

	return 0;
}

// C#
using System;
using ThrustRTCSharp;

namespace test_for
{
    class test_for
    {
        static void Main(string[] args)
        {
            For ker = new For(new String[] { "arr_in", "arr_out", "k" }, "idx",
                "    arr_out[idx] = arr_in[idx]*k;\n");

            DVVector dvec_in = new DVVector(new float[] { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f });
            DVVector dvec_out = new DVVector("float", 5);
            DVFloat dv_k = new DVFloat(10.0f);
            DeviceViewable[] args_f = new DeviceViewable[] { dvec_in, dvec_out, dv_k };
            ker.launch_n(5, args_f);
            print_array((float[])dvec_out.to_host());

        }

        static void print_array<T>(T[] arr)
        {
            foreach (var item in arr)
            {
                Console.Write(item.ToString());
                Console.Write(" ");
            }
            Console.WriteLine("");
        }
    }
}

Device Viewable Objects

Device Viewable Objects are objects that can be used as kernel arguments. All Device Viewable objects are derived from the class DeviceViewable. Device Viewable Objects have their own type information maintained internally. Therefore, you see that the kernel and for-loop parameters do not have their types defined explicitly. You can consider that all parameters are “templated”, which means their types are decided by the recieved arguments.

Basic Types

The following types of Device Viewable Objects can be initalized using values of basic types.

Name of Class	C++ Type	Creation (C++)	Creation (C#)
DVInt8	int8_t	DVInt8 x(42);	DVInt8 x = new DVInt8(42);
DVUInt8	uint8_t	DVUInt8 x(42);	DVUInt8 x = new DVUInt8(42);
DVInt16	int16_t	DVInt16 x(42);	DVInt16 x = new DVInt16(42);
DVUInt16	uint16_t	DVUInt16 x(42);	DVUInt16 x = new DVUInt16(42);
DVInt32	int32_t	DVInt32 x(42);	DVInt32 x = new DVInt32(42);
DVUInt32	uint32_t	DVUInt32 x(42);	DVUInt32 x = new DVUInt32(42);
DVInt64	int64_t	DVInt64 x(42);	DVInt64 x = new DVInt64(42);
DVUInt64	uint64_t	DVUInt64 x(42);	DVInt64 x = new DVUInt64(42);
DVFloat	float	DVFloat x(42.0f);	DVInt64 x = new DVFloat(42.0);
DVDouble	double	DVDouble x(42.0);	DVInt64 x = new DVDouble(42.0);
DVBool	bool	DVBool x(true);	DVInt64 x = new DVBool(True);

Tuples

Tuples can be created by combining multiple Device Viewable Objects. Different from Thrust, in ThrustRTC, elements of a Tuple are accessed by their names, not by indices. The names of elements need to be specified at the creation of Tuples.

// C++
DVInt32 d_int(123);
DVFloat d_float(456.0f);
DVTuple d_tuple( { {"a", &d_int}, {"b",&d_float} });

// C#
DVInt32 d_int = new DVInt32(123);
DVFloat d_float = new DVFloat(456.0f);
DVTuple d_tuple = new DVTuple(new DeviceViewable[]{d_int, d_float}, new string[]{ "a", "b"});

Advanced Types

Besides the basic types and Tuples, Vectors and Functors are also Device Viewable Objects. These objects will be explained in separated sections.

An incomplete hierarchy of Device Viewable Objects:

Vectors

Just like in Thrust, Vector is used as the most important data-container in ThrustRTC.

A difference between ThrustRTC and Thrust is that there are no “iterators” in ThrustRTC. Vectors are Device Viewable Objects, and algorithms works directly on Vectors.

In Thrust, there are “Fancy Iteractors” like “constant_iterator”, “counting_iterator”. In ThrustRTC, the corresponding functionalities are provided through “Fake Vectors” – Device Viewable Objects that can be accessed by indices but does not necessarily have a storage.

DVVector

Creation

In C++ code, a DVVector object can be created given the following:

• Name of the type of elements: it can be anything that CUDA recognizes as a type.
• Number of elements
• A pointer to a host array as source (optional)

// C++
int hIn[8] = { 10, 20, 30, 40, 50, 60, 70, 80 };
DVVector dIn("int32_t", 8, hIn);
DVVector dOut("int32_t", 8);

In C#, there are 2 ways to create a DVVector object.

• Create from array of basic types

// C#
int[] harr = new int[] { 10, 20, 30, 40, 50, 60, 70, 80 };
DVVector darr = new DVVector(harr);

The supported C# type of array elements are:

C# type	C++ Type
sbyte	int8_t
byte	uint8_t
short	int16_t
ushort	uint16_t
int	int32_t
uint	uint32_t
long	int64_t
ulong	uint64_t
float	float
double	double
bool	bool

• Create with Specified Type and Size

// C#
DVVector dvec_out = new DVVector("float", 5);

In this case, the C++ type specified can be any type that CUDA recognizes.

Optionally, a raw C++ pointer (IntPtr) to an host array can be passed as the source to copy from.

to_host()

• The method to_host() can be used to copy the content of DVVector to host.

In C++, user needs a host buffer of enough size.

// C++
int hIn[8] = { 10, 20, 30, 40, 50, 60, 70, 80 };
DVVector dIn("int32_t", 8, hIn);
dIn.to_host(hIn);

In C#, there are 2 versions of to_host(). The first versions is similar to C++. A raw pointer (IntPtr) to the recieving buffer is required. The second version creates a C# array accoring to the data-type of the array and returns it as an object:

// C#
DVVector dvec = new DVVector(new float[]{1.0f, 2.0f, 3.0f, 4.0f, 5.0f });
float[] hvec = (float[]) dvec.to_host();

There are optional parameters begin and end which can be used to specify a range to copy.

DVRange

DVRange objects can be used to map to a range of an abitary Vector object.

In C++, user can create a DVRange object using an existing Vector object and a range specified by a begin/end pair.

// C++
float hvalues[8] = { 10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f };
DVVector dvalues("float", 8, hvalues);
DVRange drange(dvvalue, 2, 5);
// drange maps to { 30.0f, 40.0f, 50.0f }

In C#, a .range() method is available for every Vector object to simplify the creation of a DVRange:

// C#
DVVector dvalues = new DVVector(new float[] { 10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f });
DVRange drange = dvalues.range(2, 5); // the same as "DVRange drange = new DVRange(dvalues, 2, 5);"
// drange maps to { 30.0f, 40.0f, 50.0f }

The existence of DVRange compensates the lack of iterators in ThrustRTC.

DVConstant

DVConstant is corresponding to thrust::constant_iterator.

A DVConstant object can be created using a constant Device Viewable Object and accessed as a Vector of constant value.

// C++
#include "TRTCContext.h"
#include "DVVector.h"
#include "fake_vectors/DVConstant.h"
#include "copy.h"

int main()
{
	DVConstant src(DVInt32(123), 10)
	DVVector dst("int32_t", 10);
	TRTC_Copy(src, dst);
	...
}

// C#
DVConstant src = new DVConstant(new DVInt32(123), 10);
DVVector dst = new DVVector("int32_t", 10);
TRTC.Copy(src, dst);

DVCounter

DVCounter is corresponding to thrust::counting_iterator.

A DVCounter object can be created using a constant Device Viewable Object as initial value, and accessed as a Vector of sequentially changing values.

// C++
#include "TRTCContext.h"
#include "DVVector.h"
#include "fake_vectors/DVCounter.h"
#include "copy.h"

int main()
{
	DVCounter src(DVInt32(1), 10)
	DVVector dst("int32_t", 10);
	TRTC_Copy(src, dst);
	...
}

// C#
DVCounter src = new DVCounter(new DVInt32(1), 10);
DVVector dst = new DVVector("int32_t", 10);
TRTC.Copy(src, dst);

DVDiscard

DVDiscard is corresponding to thrust::discard_iterator.

A DVDiscard can be created given the element type. It will ignore all value written to it. Can be used as a place-holder for an unused output.

DVPermutation

DVPermutation is corresponding to thrust::permutation_iterator.

A DVPermutation object can be created using a Vector as source and another Vector as indices, and then accessed the source Vector in permuted order.

// C++
#include "TRTCContext.h"
#include "DVVector.h"
#include "fake_vectors/DVPermutation.h"
#include "copy.h"

int main()
{
	float hvalues[8] = { 10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f };
	DVVector dvalues("float", 8, hvalues);

	int hindices[4] = { 2,6,1,3 };
	DVVector dindices("int32_t", 4, hindices);

	DVPermutation src(dvalues, dindices);
	DVVector dst("float", 4);
	
	TRTC_Copy(src, dst);
	...
}

// C#
DVVector dvalues = new DVVector(new float[] { 10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f });
DVVector dindices = new DVVector(new int[] { 2, 6, 1, 3 });
DVPermutation src = new DVPermutation(dvalues, dindices);
DVVector dst = new DVVector("float", 4);
TRTC.Copy(src, dst);

DVReverse

DVReverse is corresponding to thrust::reverse_iterator.

A DVReverse object can be created using a Vector as source and access it in reversed order.

// C++
#include "TRTCContext.h"
#include "DVVector.h"
#include "fake_vectors/DVReverse.h"
#include "copy.h"

int main()
{
	int hvalues[4] = { 3, 7, 2, 5 };
	DVVector dvalues("int32_t", 4, hvalues);

	DVReverse src(dvalues);
	DVVector dst("int32_t", 4);
	
	TRTC_Copy(src, dst);
	...
}

// C#
DVVector dvalues = new DVVector(new int[]{3, 7, 2, 5});
DVReverse src = new DVReverse(dvalues);
DVVector dst = new DVVector("int32_t", 4);
TRTC.Copy(src, dst);

DVTransform

DVTransform is corresponding to thrust::transform_iterator.

A DVTransform object can be created using a Vector as source and a Functor as operator, then access the transformed values of the source Vector.

// C++
#include "TRTCContext.h"
#include "DVVector.h"
#include "fake_vectors/DVTransform.h"
#include "copy.h"

int main()
{
	float hvalues[8] = { 1.0f, 4.0f, 9.0f, 16.0f };
	DVVector dvalues("float", 4, hvalues);

	Functor square_root{ {}, { "x" }, "        return sqrtf(x);\n" };

	DVTransform src(dvalues, "float", square_root);
	DVVector dst("float", 4);
	
	TRTC_Copy(src, dst);
	...
}

// C#
DVVector dvalues = new DVVector(new float[]{1.0, 4.0, 9.0, 16.0});
Functor square_root = new Functor(new string[]{"x"}, "         return sqrtf(x);\n");
DVTransform src = new DVTransform(dvalues, 'float', square_root);
DVVector dst = new DVVector("float", 4);
TRTC.Copy(src, dst);

DVZipped

DVZipped is corresponding to thrust::zip_iterator.

A DVZipped object can be created using multiple Vectors as inputs and accessed as a Vector consisting elements combined from the elements from each of these Vectors.

// C++
#include "TRTCContext.h"
#include "DVVector.h"
#include "fake_vectors/DVTransform.h"
#include "copy.h"

int main()
{
	int h_int_in[5] = { 0, 1, 2, 3, 4};
	DVVector d_int_in("int32_t", 5, h_int_in);
	float h_float_in[5] = { 0.0f, 10.0f, 20.0f, 30.0f, 40.0f };
	DVVector d_float_in("float", 5, h_float_in);

	DVVector d_int_out("int32_t", 5);
	DVVector d_float_out("float", 5);

	DVZipped src({ &d_int_in, &d_float_in }, { "a", "b" });
	DVZipped dst({ &d_int_out, &d_float_out }, { "a", "b" });

	TRTC_Copy(src, dst);
	...
}

// C#
DVVector d_int_in = new DVVector(new int[] { 0, 1, 2, 3, 4 });
DVVector d_float_in = new DVVector(new float[] { 0.0f, 10.0f, 20.0f, 30.0f, 40.0f });

DVVector d_int_out = new DVVector("int32_t", 5);
DVVector d_float_out = new DVVector("float", 5);

DVZipped src = new DVZipped(new DVVectorLike[] { d_int_in, d_float_in }, new string[] { "a", "b" });
DVZipped dst = new DVZipped(new DVVectorLike[] { d_int_out, d_float_out }, new string[] { "a", "b" });

TRTC.Copy(src, dst);

DVCustomVector

DVCustomVector allows user to customize the behavior of a Fake Vector. It is corresponding to iterator_adaptor.

A DVCustomVector object can be created given:

• A map of Device Viewable Object to be captured, usually used as source of data
• Name of the index variable, which is a size_t variable used by operator[]
• The code-body of operator[] represented as a string.
• ELement type of the vector as a string
• Size of the vector
• Optional switch telling if the elements are read-only

// C++
#include "TRTCContext.h"
#include "DVVector.h"
#include "fake_vectors/DVCustomVector.h"
#include "copy.h"

int main()
{
	int h_in[5] = { 0, 1, 2, 3, 4};
	DVVector d_in("int32_t", 5, h_in);

	DVCustomVector src({ {"src", &d_in} }, "idx",
	"        return src[idx % src.size()];\n", "int32_t", d_in.size() * 5);

	DVVector dst("int32_t", 25);
	
	TRTC_Copy(src, dst);
}

// C#
DVVector d_in = new DVVector(new int[] { 0, 1, 2, 3, 4 });
DVCustomVector src = new DVCustomVector(new DeviceViewable[] { d_in }, new string[] { "src" }, "idx",
    "        return src[idx % src.size()];\n", "int32_t", d_in.size() * 5);
DVVector dst= new DVVector("int32_t", 25);
TRTC.Copy(src, dst);

Functors

Functors, in generally sense, are objects with an operator(). In ThrustRTC, Functors are Device Viewable Objects with a device operator(). There are 2 kinds of Functors that can be used: user defined Functors and built-in Functors.

User Defined Functors

An user defined functor can be created given:

• A map of Device Viewable Object to be captured, can be empty.
• A list of names of Functor parameters, that are the parameters of operator().
• The code-body of operator() represented as a string.

// C++
Functor is_even = { {},{ "x" }, "        return x % 2 == 0;\n" };

There is a simplified version of the constructor in C# that omits the capturing list:

// C#
Functor is_even = new Functor( new string[]{"x"}, "         return x % 2 == 0;\n");

A temporary structure will be created internally, which looks like:

// CUDA C++, internal code 
struct
{
	template<typename T>
	__device__ inline auto operator()(const T& x)
	{
		return x % 2 == 0;
	}
};

The indentation is there just to make the code formated nicely so it will be easy to debug when there is a compilation error.

Built-In Functors

In both C++ code and C# code, the class Functor has a constructor that takes in a single string parameter, which can be used to create built-in Functors.

The following built-in Functors are available:

Name of Functor	Creation (C++)	Creation (C#)
Identity	Functor f(“Identity”);	Functor f = new Functor(“Identity”);
Maximum	Functor f(“Maximum”);	Functor f = new Functor(“Maximum”);
Minimum	Functor f(“Minimum”);	Functor f = new Functor(“Minimum”);
EqualTo	Functor f(“EqualTo”);	Functor f = new Functor(“EqualTo”);
NotEqualTo	Functor f(“NotEqualTo”);	Functor f = new Functor(“NotEqualTo”);
Greater	Functor f(“Greater”);	Functor f = new Functor(“Greater”);
Less	Functor f(“Less”);	Functor f = new Functor(“Less”);
GreaterEqual	Functor f(“GreaterEqual”);	Functor f = new Functor(“GreaterEqual”);
LessEqual	Functor f(“LessEqual”);	Functor f = new Functor(“LessEqual”);
Plus	Functor f(“Plus”);	Functor f = new Functor(“Plus”);
Minus	Functor f(“Minus”);	Functor f = new Functor(“Minus”);
Multiplies	Functor f(“Multiplies”);	Functor f = new Functor(“Multiplies”);
Divides	Functor f(“Divides”);	Functor f = new Functor(“Divides”);
Modulus	Functor f(“Modulus”);	Functor f = new Functor(“Modulus”);
Negate	Functor f(“Negate”);	Functor f = new Functor(“Negate”);

Algorithms

Each algorithm of ThrustRTC is corresponding to one in Thrust. A significant difference between ThrustRTC functions and Thrust functions is that Thrust functions takes in iterators as parameters, while ThrustRTC takes in Vectors directly. In ThrustRTC, DVRange can be used to create a working range of a Vector object.

Transformations

Transformation algorithms are one-pass algorithms applied to each element of one or more Vectors.

The simpliest transformation algorithm is Fill, which sets all elements of a Vector to a specified value.

// C++
DVVector darr("int32_t", 5);
TRTC_Fill(darr, DVInt32(123));

// C#
DVVector darr = new DVVector("int32_t", 5);
TRTC.Fill(darr, new DVInt32(123));

The above code sets all elements of the Vector to 123. Other transformations include Sequence, Replace, and of course Transform.

The following source code demonstrates several of the transformation algorithms.

// C++
#include <stdio.h>
#include "TRTCContext.h"
#include "DVVector.h"
#include "transform.h"
#include "sequence.h"
#include "fill.h"
#include "replace.h"

int main()
{
	DVVector X("int32_t", 10);
	DVVector Y("int32_t", 10);
	DVVector Z("int32_t", 10);

	// initialize X to 0,1,2,3, ....
	TRTC_Sequence(X);

	// compute Y = -X
	TRTC_Transform(X, Y, Functor("Negate"));

	// fill Z with twos
	TRTC_Fill(Z, DVInt32(2));

	// compute Y = X mod 2
	TRTC_Transform_Binary(X, Z, Y, Functor("Modulus"));

	// replace all the ones in Y with tens
	TRTC_Replace(Y, DVInt32(1), DVInt32(10));

	// print Y
	int results[10];
	Y.to_host(results);

	for (int i = 0; i < 10; i++)
		printf("%d ", results[i]);
	puts("");

	return 0;
}

// C#
DVVector X = new DVVector("int32_t", 10);
DVVector Y = new DVVector("int32_t", 10);
DVVector Z = new DVVector("int32_t", 10);

// initialize X to 0,1,2,3, ....
TRTC.Sequence(X);

// compute Y = -X
TRTC.Transform(X, Y, new Functor("Negate"));

// fill Z with twos
TRTC.Fill(Z, new DVInt32(2));

// compute Y = X mod 2
TRTC.Transform_Binary(X, Z, Y, new Functor("Modulus"));

// replace all the ones in Y with tens
TRTC.Replace(Y, new DVInt32(1), new DVInt32(10));

// print Y.to_host()...

Reductions

A reduction algorithm uses a binary operation to reduce an input Vector to a single value. For example, the sum of a Vector of numbers is obtained by reducing the array with a Plus operation. Similarly, the maximum of a Vector is obtained by reducing with a Maximum operation. The sum of a Vector D can be done by the following code:

// C++
ViewBuf ret;
TRTC_Reduce(D, DVInt32(0), Functor("Plus"), ret);
int sum = *((int*)ret.data());

// C#
int sum = (int)TRTC.Reduce(D, new DVInt32(0), new Functor("Plus"));

The first argument is the input Vector. The second and third arguments provide the initial value and reduction operator respectively. Actually, this kind of reduction is so common that it is the default choice when no initial value or operator is provided. The following three lines are therefore equivalent:

// C#
int sum = (int)TRTC.Reduce(D, new DVInt32(0), new Functor("Plus"));
int sum = (int)TRTC.Reduce(D, new DVInt32(0));
int sum = (int)TRTC.Reduce(D);

Although Reduce() is sufficient to implement a wide variety of reductions, ThrustRTC provides a few additional functions for convenience. For example, Count() returns the number of instances of a specific value in a given Vector.

// C#
DVVector vec = new DVVector(new int[]{ 0, 1, 0, 1, 1});
long result = TRTC.Count(vec, new DVInt32(1));

Other reduction operations include Count_If(), Min_Element(), Max_Element(), Is_Sorted(), Inner_Product(), and several others.

Prefix-Sums

Parallel prefix-sums, or scan operations, are important building blocks in many parallel algorithms such as stream compaction and radix sort. Consider the following source code which illustrates an inclusive scan operation using the default plus operator:

// C++
int data[6] = { 1, 0, 2, 2, 1, 3 };
DVVector d_data("int32_t", 6, data);
TRTC_Inclusive_Scan(d_data, d_data); // in-place scan
d_data.to_host(data);
// data is now {1, 1, 3, 5, 6, 9}

// C#
DVVector d_data = new DVVector(new int[]{1, 0, 2, 2, 1, 3});
TRTC.Inclusive_Scan(d_data, d_data);// in-place scan
//  d_data is now {1, 1, 3, 5, 6, 9}

In an inclusive scan each element of the output is the corresponding partial sum of the input Vector. For example, data[2] = data[0] + data[1] + data[2]. An exclusive scan is similar, but shifted by one place to the right:

// C#
DVVector d_data = new DVVector(new int[]{1, 0, 2, 2, 1, 3});
TRTC.Exclusive_Scan(d_data, d_data); // in-place scan
// d_data is now {0, 1, 1, 3, 5, 6}

So now data[2] = data[0] + data[1]. As these examples show, Inclusive_Scan() and Exclusive_Scan() are permitted to be performed in-place. ThrustRTC also provides the functions Transform_Inclusive_Scan() and Transform_Exclusive_Scan() which apply a unary function to the input Vector before performing the scan.

Reordering

ThrustRTC provides support for partitioning and stream compaction through the following algorithms:

Copy_If(): copy elements that pass a predicate test Partition(): reorder elements according to a predicate (True values precede False values) Remove() and Remove_If(): remove elements that pass a predicate test Unique(): remove consecutive duplicates within a sequence

Sorting

ThrustRTC offers several functions to sort data or rearrange data according to a given criterion. All sorting algorithms of ThrustRTC are stable.

// C++
int hvalues[6]= { 1, 4, 2, 8, 5, 7 };
DVVector dvalues("int32_t", 6, hvalues);

TRTC_Sort(dvalues);
dvalues.to_host(hvalues);
// hvalues is now {1, 2, 4, 5, 7, 8}

// C#
DVVector dvalues = new DVVector(new int[]{ 1, 4, 2, 8, 5, 7 });
TRTC.Sort(dvalues)
// dvalues is now {1, 2, 4, 5, 7, 8}

In addition, ThrustRTC provides Sort_By_Key(), which sort key-value pairs stored in separate places.

// C++
int hkeys[6] = { 1, 4, 2, 8, 5, 7 };
DVVector dkeys("int32_t", 6, hkeys);
char hvalues[6] = { 'a', 'b', 'c', 'd', 'e', 'f' };
DVVector dvalues("int8_t", 6, hvalues);
TRTC_Sort_By_Key(dkeys, dvalues);
dkeys.to_host(hkeys);
dvalues.to_host(hvalues);
// hkeys is now { 1, 2, 4, 5, 7, 8}
// hvalues is now {'a', 'c', 'b', 'e', 'f', 'd'}

// C#
DVVector dkeys = new DVVector(new int[]{ 1, 4, 2, 8, 5, 7 });
DVVector dvalues = new DVVector(new int[]{ 1, 2, 3, 4, 5, 6});
TRTC.Sort_By_Key(dkeys, dvalues)
// dkeys is now { 1, 2, 4, 5, 7, 8}
// dvalues is now {'1', '3', '2', '5', '6', '4'}

Just like in Thrust, sorting functions in ThrustRTC also accept functors as alternative comparison operators:

// C++
int hvalues[6] = { 1, 4, 2, 8, 5, 7 };
DVVector dvalues("int32_t", 6, hvalues);
TRTC_Sort(dvalues, Functor("Greater"));
dvalues.to_host(hvalues);
// hvalues is now {8, 7, 5, 4, 2, 1}

// C#
DVVector dvalues = new DVVector(new int[]{ 1, 4, 2, 8, 5, 7 });
TRTC.Sort(dvalues, new Functor("Greater"));
// dvalues is now {8, 7, 5, 4, 2, 1}