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+=============================
+User Guide for NVPTX Back-end
+=============================
+
+.. contents::
+   :local:
+   :depth: 3
+
+
+Introduction
+============
+
+To support GPU programming, the NVPTX back-end supports a subset of LLVM IR
+along with a defined set of conventions used to represent GPU programming
+concepts. This document provides an overview of the general usage of the back-
+end, including a description of the conventions used and the set of accepted
+LLVM IR.
+
+.. note:: 
+   
+   This document assumes a basic familiarity with CUDA and the PTX
+   assembly language. Information about the CUDA Driver API and the PTX assembly
+   language can be found in the `CUDA documentation
+   <http://docs.nvidia.com/cuda/index.html>`_.
+
+
+
+Conventions
+===========
+
+Marking Functions as Kernels
+----------------------------
+
+In PTX, there are two types of functions: *device functions*, which are only
+callable by device code, and *kernel functions*, which are callable by host
+code. By default, the back-end will emit device functions. Metadata is used to
+declare a function as a kernel function. This metadata is attached to the
+``nvvm.annotations`` named metadata object, and has the following format:
+
+.. code-block:: llvm
+
+   !0 = metadata !{<function-ref>, metadata !"kernel", i32 1}
+
+The first parameter is a reference to the kernel function. The following
+example shows a kernel function calling a device function in LLVM IR. The
+function ``@my_kernel`` is callable from host code, but ``@my_fmad`` is not.
+
+.. code-block:: llvm
+
+    define float @my_fmad(float %x, float %y, float %z) {
+      %mul = fmul float %x, %y
+      %add = fadd float %mul, %z
+      ret float %add
+    }
+
+    define void @my_kernel(float* %ptr) {
+      %val = load float* %ptr
+      %ret = call float @my_fmad(float %val, float %val, float %val)
+      store float %ret, float* %ptr
+      ret void
+    }
+
+    !nvvm.annotations = !{!1}
+    !1 = metadata !{void (float*)* @my_kernel, metadata !"kernel", i32 1}
+
+When compiled, the PTX kernel functions are callable by host-side code.
+
+
+Address Spaces
+--------------
+
+The NVPTX back-end uses the following address space mapping:
+
+   ============= ======================
+   Address Space Memory Space
+   ============= ======================
+   0             Generic
+   1             Global
+   2             Internal Use
+   3             Shared
+   4             Constant
+   5             Local
+   ============= ======================
+
+Every global variable and pointer type is assigned to one of these address
+spaces, with 0 being the default address space. Intrinsics are provided which
+can be used to convert pointers between the generic and non-generic address
+spaces.
+
+As an example, the following IR will define an array ``@g`` that resides in
+global device memory.
+
+.. code-block:: llvm
+
+    @g = internal addrspace(1) global [4 x i32] [ i32 0, i32 1, i32 2, i32 3 ]
+
+LLVM IR functions can read and write to this array, and host-side code can
+copy data to it by name with the CUDA Driver API.
+
+Note that since address space 0 is the generic space, it is illegal to have
+global variables in address space 0.  Address space 0 is the default address
+space in LLVM, so the ``addrspace(N)`` annotation is *required* for global
+variables.
+
+
+NVPTX Intrinsics
+================
+
+Address Space Conversion
+------------------------
+
+'``llvm.nvvm.ptr.*.to.gen``' Intrinsics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Syntax:
+"""""""
+
+These are overloaded intrinsics.  You can use these on any pointer types.
+
+.. code-block:: llvm
+
+    declare i8* @llvm.nvvm.ptr.global.to.gen.p0i8.p1i8(i8 addrspace(1)*)
+    declare i8* @llvm.nvvm.ptr.shared.to.gen.p0i8.p3i8(i8 addrspace(3)*)
+    declare i8* @llvm.nvvm.ptr.constant.to.gen.p0i8.p4i8(i8 addrspace(4)*)
+    declare i8* @llvm.nvvm.ptr.local.to.gen.p0i8.p5i8(i8 addrspace(5)*)
+
+Overview:
+"""""""""
+
+The '``llvm.nvvm.ptr.*.to.gen``' intrinsics convert a pointer in a non-generic
+address space to a generic address space pointer.
+
+Semantics:
+""""""""""
+
+These intrinsics modify the pointer value to be a valid generic address space
+pointer.
+
+
+'``llvm.nvvm.ptr.gen.to.*``' Intrinsics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Syntax:
+"""""""
+
+These are overloaded intrinsics.  You can use these on any pointer types.
+
+.. code-block:: llvm
+
+    declare i8* @llvm.nvvm.ptr.gen.to.global.p1i8.p0i8(i8 addrspace(1)*)
+    declare i8* @llvm.nvvm.ptr.gen.to.shared.p3i8.p0i8(i8 addrspace(3)*)
+    declare i8* @llvm.nvvm.ptr.gen.to.constant.p4i8.p0i8(i8 addrspace(4)*)
+    declare i8* @llvm.nvvm.ptr.gen.to.local.p5i8.p0i8(i8 addrspace(5)*)
+
+Overview:
+"""""""""
+
+The '``llvm.nvvm.ptr.gen.to.*``' intrinsics convert a pointer in the generic
+address space to a pointer in the target address space.  Note that these
+intrinsics are only useful if the address space of the target address space of
+the pointer is known.  It is not legal to use address space conversion
+intrinsics to convert a pointer from one non-generic address space to another
+non-generic address space.
+
+Semantics:
+""""""""""
+
+These intrinsics modify the pointer value to be a valid pointer in the target
+non-generic address space.
+
+
+Reading PTX Special Registers
+-----------------------------
+
+'``llvm.nvvm.read.ptx.sreg.*``'
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Syntax:
+"""""""
+
+.. code-block:: llvm
+
+    declare i32 @llvm.nvvm.read.ptx.sreg.tid.x()
+    declare i32 @llvm.nvvm.read.ptx.sreg.tid.y()
+    declare i32 @llvm.nvvm.read.ptx.sreg.tid.z()
+    declare i32 @llvm.nvvm.read.ptx.sreg.ntid.x()
+    declare i32 @llvm.nvvm.read.ptx.sreg.ntid.y()
+    declare i32 @llvm.nvvm.read.ptx.sreg.ntid.z()
+    declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.x()
+    declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.y()
+    declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.z()
+    declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.x()
+    declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.y()
+    declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.z()
+    declare i32 @llvm.nvvm.read.ptx.sreg.warpsize()
+
+Overview:
+"""""""""
+
+The '``@llvm.nvvm.read.ptx.sreg.*``' intrinsics provide access to the PTX
+special registers, in particular the kernel launch bounds.  These registers
+map in the following way to CUDA builtins:
+
+   ============ =====================================
+   CUDA Builtin PTX Special Register Intrinsic
+   ============ =====================================
+   ``threadId`` ``@llvm.nvvm.read.ptx.sreg.tid.*``
+   ``blockIdx`` ``@llvm.nvvm.read.ptx.sreg.ctaid.*``
+   ``blockDim`` ``@llvm.nvvm.read.ptx.sreg.ntid.*``
+   ``gridDim``  ``@llvm.nvvm.read.ptx.sreg.nctaid.*``
+   ============ =====================================
+
+
+Barriers
+--------
+
+'``llvm.nvvm.barrier0``'
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Syntax:
+"""""""
+
+.. code-block:: llvm
+
+  declare void @llvm.nvvm.barrier0()
+
+Overview:
+"""""""""
+
+The '``@llvm.nvvm.barrier0()``' intrinsic emits a PTX ``bar.sync 0``
+instruction, equivalent to the ``__syncthreads()`` call in CUDA.
+
+
+Other Intrinsics
+----------------
+
+For the full set of NVPTX intrinsics, please see the
+``include/llvm/IR/IntrinsicsNVVM.td`` file in the LLVM source tree.
+
+
+Executing PTX
+=============
+
+The most common way to execute PTX assembly on a GPU device is to use the CUDA
+Driver API. This API is a low-level interface to the GPU driver and allows for
+JIT compilation of PTX code to native GPU machine code.
+
+Initializing the Driver API:
+
+.. code-block:: c++
+
+    CUdevice device;
+    CUcontext context;
+
+    // Initialize the driver API
+    cuInit(0);
+    // Get a handle to the first compute device
+    cuDeviceGet(&device, 0);
+    // Create a compute device context
+    cuCtxCreate(&context, 0, device);
+
+JIT compiling a PTX string to a device binary:
+
+.. code-block:: c++
+
+    CUmodule module;
+    CUfunction funcion;
+
+    // JIT compile a null-terminated PTX string
+    cuModuleLoadData(&module, (void*)PTXString);
+
+    // Get a handle to the "myfunction" kernel function
+    cuModuleGetFunction(&function, module, "myfunction");
+
+For full examples of executing PTX assembly, please see the `CUDA Samples
+<https://developer.nvidia.com/cuda-downloads>`_ distribution.