User Guide for NVPTX Back-end

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.

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:

!0 = !{<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.

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, float* %ptr
  %ret = call float @my_fmad(float %val, float %val, float %val)
  store float %ret, float* %ptr
  ret void
}

!nvvm.annotations = !{!1}
!1 = !{void (float*)* @my_kernel, !"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.

@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.

Triples

The NVPTX target uses the module triple to select between 32/64-bit code generation and the driver-compiler interface to use. The triple architecture can be one of nvptx (32-bit PTX) or nvptx64 (64-bit PTX). The operating system should be one of cuda or nvcl, which determines the interface used by the generated code to communicate with the driver. Most users will want to use cuda as the operating system, which makes the generated PTX compatible with the CUDA Driver API.

Example: 32-bit PTX for CUDA Driver API: nvptx-nvidia-cuda

Example: 64-bit PTX for CUDA Driver API: nvptx64-nvidia-cuda

NVPTX Intrinsics

Address Space Conversion

llvm.nvvm.ptr.*.to.gen‘ Intrinsics

Syntax:

These are overloaded intrinsics. You can use these on any pointer types.

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.

declare i8 addrspace(1)* @llvm.nvvm.ptr.gen.to.global.p1i8.p0i8(i8*)
declare i8 addrspace(3)* @llvm.nvvm.ptr.gen.to.shared.p3i8.p0i8(i8*)
declare i8 addrspace(4)* @llvm.nvvm.ptr.gen.to.constant.p4i8.p0i8(i8*)
declare i8 addrspace(5)* @llvm.nvvm.ptr.gen.to.local.p5i8.p0i8(i8*)
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:
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:
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.

Linking with Libdevice

The CUDA Toolkit comes with an LLVM bitcode library called libdevice that implements many common mathematical functions. This library can be used as a high-performance math library for any compilers using the LLVM NVPTX target. The library can be found under nvvm/libdevice/ in the CUDA Toolkit and there is a separate version for each compute architecture.

For a list of all math functions implemented in libdevice, see libdevice Users Guide.

To accommodate various math-related compiler flags that can affect code generation of libdevice code, the library code depends on a special LLVM IR pass (NVVMReflect) to handle conditional compilation within LLVM IR. This pass looks for calls to the @__nvvm_reflect function and replaces them with constants based on the defined reflection parameters. Such conditional code often follows a pattern:

float my_function(float a) {
  if (__nvvm_reflect("FASTMATH"))
    return my_function_fast(a);
  else
    return my_function_precise(a);
}

The default value for all unspecified reflection parameters is zero.

The NVVMReflect pass should be executed early in the optimization pipeline, immediately after the link stage. The internalize pass is also recommended to remove unused math functions from the resulting PTX. For an input IR module module.bc, the following compilation flow is recommended:

  1. Save list of external functions in module.bc
  2. Link module.bc with libdevice.compute_XX.YY.bc
  3. Internalize all functions not in list from (1)
  4. Eliminate all unused internal functions
  5. Run NVVMReflect pass
  6. Run standard optimization pipeline

Note

linkonce and linkonce_odr linkage types are not suitable for the libdevice functions. It is possible to link two IR modules that have been linked against libdevice using different reflection variables.

Since the NVVMReflect pass replaces conditionals with constants, it will often leave behind dead code of the form:

entry:
  ..
  br i1 true, label %foo, label %bar
foo:
  ..
bar:
  ; Dead code
  ..

Therefore, it is recommended that NVVMReflect is executed early in the optimization pipeline before dead-code elimination.

Reflection Parameters

The libdevice library currently uses the following reflection parameters to control code generation:

Flag Description
__CUDA_FTZ=[0,1] Use optimized code paths that flush subnormals to zero

Invoking NVVMReflect

To ensure that all dead code caused by the reflection pass is eliminated, it is recommended that the reflection pass is executed early in the LLVM IR optimization pipeline. The pass takes an optional mapping of reflection parameter name to an integer value. This mapping can be specified as either a command-line option to opt or as an LLVM StringMap<int> object when programmatically creating a pass pipeline.

With opt:

# opt -nvvm-reflect -nvvm-reflect-list=<var>=<value>,<var>=<value> module.bc -o module.reflect.bc

With programmatic pass pipeline:

extern FunctionPass *llvm::createNVVMReflectPass(const StringMap<int>& Mapping);

StringMap<int> ReflectParams;
ReflectParams["__CUDA_FTZ"] = 1;
Passes.add(createNVVMReflectPass(ReflectParams));

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:

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:

CUmodule module;
CUfunction function;

// 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 distribution.

Common Issues

ptxas complains of undefined function: __nvvm_reflect

When linking with libdevice, the NVVMReflect pass must be used. See Linking with Libdevice for more information.

Tutorial: A Simple Compute Kernel

To start, let us take a look at a simple compute kernel written directly in LLVM IR. The kernel implements vector addition, where each thread computes one element of the output vector C from the input vectors A and B. To make this easier, we also assume that only a single CTA (thread block) will be launched, and that it will be one dimensional.

The Kernel

target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"
target triple = "nvptx64-nvidia-cuda"

; Intrinsic to read X component of thread ID
declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind

define void @kernel(float addrspace(1)* %A,
                    float addrspace(1)* %B,
                    float addrspace(1)* %C) {
entry:
  ; What is my ID?
  %id = tail call i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind

  ; Compute pointers into A, B, and C
  %ptrA = getelementptr float, float addrspace(1)* %A, i32 %id
  %ptrB = getelementptr float, float addrspace(1)* %B, i32 %id
  %ptrC = getelementptr float, float addrspace(1)* %C, i32 %id

  ; Read A, B
  %valA = load float, float addrspace(1)* %ptrA, align 4
  %valB = load float, float addrspace(1)* %ptrB, align 4

  ; Compute C = A + B
  %valC = fadd float %valA, %valB

  ; Store back to C
  store float %valC, float addrspace(1)* %ptrC, align 4

  ret void
}

!nvvm.annotations = !{!0}
!0 = !{void (float addrspace(1)*,
             float addrspace(1)*,
             float addrspace(1)*)* @kernel, !"kernel", i32 1}

We can use the LLVM llc tool to directly run the NVPTX code generator:

# llc -mcpu=sm_20 kernel.ll -o kernel.ptx

Note

If you want to generate 32-bit code, change p:64:64:64 to p:32:32:32 in the module data layout string and use nvptx-nvidia-cuda as the target triple.

The output we get from llc (as of LLVM 3.4):

//
// Generated by LLVM NVPTX Back-End
//

.version 3.1
.target sm_20
.address_size 64

  // .globl kernel
                                        // @kernel
.visible .entry kernel(
  .param .u64 kernel_param_0,
  .param .u64 kernel_param_1,
  .param .u64 kernel_param_2
)
{
  .reg .f32   %f<4>;
  .reg .s32   %r<2>;
  .reg .s64   %rl<8>;

// BB#0:                                // %entry
  ld.param.u64    %rl1, [kernel_param_0];
  mov.u32         %r1, %tid.x;
  mul.wide.s32    %rl2, %r1, 4;
  add.s64         %rl3, %rl1, %rl2;
  ld.param.u64    %rl4, [kernel_param_1];
  add.s64         %rl5, %rl4, %rl2;
  ld.param.u64    %rl6, [kernel_param_2];
  add.s64         %rl7, %rl6, %rl2;
  ld.global.f32   %f1, [%rl3];
  ld.global.f32   %f2, [%rl5];
  add.f32         %f3, %f1, %f2;
  st.global.f32   [%rl7], %f3;
  ret;
}

Dissecting the Kernel

Now let us dissect the LLVM IR that makes up this kernel.

Data Layout

The data layout string determines the size in bits of common data types, their ABI alignment, and their storage size. For NVPTX, you should use one of the following:

32-bit PTX:

target datalayout = "e-p:32:32:32-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"

64-bit PTX:

target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"

Target Intrinsics

In this example, we use the @llvm.nvvm.read.ptx.sreg.tid.x intrinsic to read the X component of the current thread’s ID, which corresponds to a read of register %tid.x in PTX. The NVPTX back-end supports a large set of intrinsics. A short list is shown below; please see include/llvm/IR/IntrinsicsNVVM.td for the full list.

Intrinsic CUDA Equivalent
i32 @llvm.nvvm.read.ptx.sreg.tid.{x,y,z} threadIdx.{x,y,z}
i32 @llvm.nvvm.read.ptx.sreg.ctaid.{x,y,z} blockIdx.{x,y,z}
i32 @llvm.nvvm.read.ptx.sreg.ntid.{x,y,z} blockDim.{x,y,z}
i32 @llvm.nvvm.read.ptx.sreg.nctaid.{x,y,z} gridDim.{x,y,z}
void @llvm.cuda.syncthreads() __syncthreads()

Address Spaces

You may have noticed that all of the pointer types in the LLVM IR example had an explicit address space specifier. What is address space 1? NVIDIA GPU devices (generally) have four types of memory:

  • Global: Large, off-chip memory
  • Shared: Small, on-chip memory shared among all threads in a CTA
  • Local: Per-thread, private memory
  • Constant: Read-only memory shared across all threads

These different types of memory are represented in LLVM IR as address spaces. There is also a fifth address space used by the NVPTX code generator that corresponds to the “generic” address space. This address space can represent addresses in any other address space (with a few exceptions). This allows users to write IR functions that can load/store memory using the same instructions. Intrinsics are provided to convert pointers between the generic and non-generic address spaces.

See Address Spaces and NVPTX Intrinsics for more information.

Kernel Metadata

In PTX, a function can be either a kernel function (callable from the host program), or a device function (callable only from GPU code). You can think of kernel functions as entry-points in the GPU program. To mark an LLVM IR function as a kernel function, we make use of special LLVM metadata. The NVPTX back-end will look for a named metadata node called nvvm.annotations. This named metadata must contain a list of metadata that describe the IR. For our purposes, we need to declare a metadata node that assigns the “kernel” attribute to the LLVM IR function that should be emitted as a PTX kernel function. These metadata nodes take the form:

!{<function ref>, metadata !"kernel", i32 1}

For the previous example, we have:

!nvvm.annotations = !{!0}
!0 = !{void (float addrspace(1)*,
             float addrspace(1)*,
             float addrspace(1)*)* @kernel, !"kernel", i32 1}

Here, we have a single metadata declaration in nvvm.annotations. This metadata annotates our @kernel function with the kernel attribute.

Running the Kernel

Generating PTX from LLVM IR is all well and good, but how do we execute it on a real GPU device? The CUDA Driver API provides a convenient mechanism for loading and JIT compiling PTX to a native GPU device, and launching a kernel. The API is similar to OpenCL. A simple example showing how to load and execute our vector addition code is shown below. Note that for brevity this code does not perform much error checking!

Note

You can also use the ptxas tool provided by the CUDA Toolkit to offline compile PTX to machine code (SASS) for a specific GPU architecture. Such binaries can be loaded by the CUDA Driver API in the same way as PTX. This can be useful for reducing startup time by precompiling the PTX kernels.

#include <iostream>
#include <fstream>
#include <cassert>
#include "cuda.h"


void checkCudaErrors(CUresult err) {
  assert(err == CUDA_SUCCESS);
}

/// main - Program entry point
int main(int argc, char **argv) {
  CUdevice    device;
  CUmodule    cudaModule;
  CUcontext   context;
  CUfunction  function;
  CUlinkState linker;
  int         devCount;

  // CUDA initialization
  checkCudaErrors(cuInit(0));
  checkCudaErrors(cuDeviceGetCount(&devCount));
  checkCudaErrors(cuDeviceGet(&device, 0));

  char name[128];
  checkCudaErrors(cuDeviceGetName(name, 128, device));
  std::cout << "Using CUDA Device [0]: " << name << "\n";

  int devMajor, devMinor;
  checkCudaErrors(cuDeviceComputeCapability(&devMajor, &devMinor, device));
  std::cout << "Device Compute Capability: "
            << devMajor << "." << devMinor << "\n";
  if (devMajor < 2) {
    std::cerr << "ERROR: Device 0 is not SM 2.0 or greater\n";
    return 1;
  }

  std::ifstream t("kernel.ptx");
  if (!t.is_open()) {
    std::cerr << "kernel.ptx not found\n";
    return 1;
  }
  std::string str((std::istreambuf_iterator<char>(t)),
                    std::istreambuf_iterator<char>());

  // Create driver context
  checkCudaErrors(cuCtxCreate(&context, 0, device));

  // Create module for object
  checkCudaErrors(cuModuleLoadDataEx(&cudaModule, str.c_str(), 0, 0, 0));

  // Get kernel function
  checkCudaErrors(cuModuleGetFunction(&function, cudaModule, "kernel"));

  // Device data
  CUdeviceptr devBufferA;
  CUdeviceptr devBufferB;
  CUdeviceptr devBufferC;

  checkCudaErrors(cuMemAlloc(&devBufferA, sizeof(float)*16));
  checkCudaErrors(cuMemAlloc(&devBufferB, sizeof(float)*16));
  checkCudaErrors(cuMemAlloc(&devBufferC, sizeof(float)*16));

  float* hostA = new float[16];
  float* hostB = new float[16];
  float* hostC = new float[16];

  // Populate input
  for (unsigned i = 0; i != 16; ++i) {
    hostA[i] = (float)i;
    hostB[i] = (float)(2*i);
    hostC[i] = 0.0f;
  }

  checkCudaErrors(cuMemcpyHtoD(devBufferA, &hostA[0], sizeof(float)*16));
  checkCudaErrors(cuMemcpyHtoD(devBufferB, &hostB[0], sizeof(float)*16));


  unsigned blockSizeX = 16;
  unsigned blockSizeY = 1;
  unsigned blockSizeZ = 1;
  unsigned gridSizeX  = 1;
  unsigned gridSizeY  = 1;
  unsigned gridSizeZ  = 1;

  // Kernel parameters
  void *KernelParams[] = { &devBufferA, &devBufferB, &devBufferC };

  std::cout << "Launching kernel\n";

  // Kernel launch
  checkCudaErrors(cuLaunchKernel(function, gridSizeX, gridSizeY, gridSizeZ,
                                 blockSizeX, blockSizeY, blockSizeZ,
                                 0, NULL, KernelParams, NULL));

  // Retrieve device data
  checkCudaErrors(cuMemcpyDtoH(&hostC[0], devBufferC, sizeof(float)*16));


  std::cout << "Results:\n";
  for (unsigned i = 0; i != 16; ++i) {
    std::cout << hostA[i] << " + " << hostB[i] << " = " << hostC[i] << "\n";
  }


  // Clean up after ourselves
  delete [] hostA;
  delete [] hostB;
  delete [] hostC;

  // Clean-up
  checkCudaErrors(cuMemFree(devBufferA));
  checkCudaErrors(cuMemFree(devBufferB));
  checkCudaErrors(cuMemFree(devBufferC));
  checkCudaErrors(cuModuleUnload(cudaModule));
  checkCudaErrors(cuCtxDestroy(context));

  return 0;
}

You will need to link with the CUDA driver and specify the path to cuda.h.

# clang++ sample.cpp -o sample -O2 -g -I/usr/local/cuda-5.5/include -lcuda

We don’t need to specify a path to libcuda.so since this is installed in a system location by the driver, not the CUDA toolkit.

If everything goes as planned, you should see the following output when running the compiled program:

Using CUDA Device [0]: GeForce GTX 680
Device Compute Capability: 3.0
Launching kernel
Results:
0 + 0 = 0
1 + 2 = 3
2 + 4 = 6
3 + 6 = 9
4 + 8 = 12
5 + 10 = 15
6 + 12 = 18
7 + 14 = 21
8 + 16 = 24
9 + 18 = 27
10 + 20 = 30
11 + 22 = 33
12 + 24 = 36
13 + 26 = 39
14 + 28 = 42
15 + 30 = 45

Note

You will likely see a different device identifier based on your hardware

Tutorial: Linking with Libdevice

In this tutorial, we show a simple example of linking LLVM IR with the libdevice library. We will use the same kernel as the previous tutorial, except that we will compute C = pow(A, B) instead of C = A + B. Libdevice provides an __nv_powf function that we will use.

target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"
target triple = "nvptx64-nvidia-cuda"

; Intrinsic to read X component of thread ID
declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind
; libdevice function
declare float @__nv_powf(float, float)

define void @kernel(float addrspace(1)* %A,
                    float addrspace(1)* %B,
                    float addrspace(1)* %C) {
entry:
  ; What is my ID?
  %id = tail call i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind

  ; Compute pointers into A, B, and C
  %ptrA = getelementptr float, float addrspace(1)* %A, i32 %id
  %ptrB = getelementptr float, float addrspace(1)* %B, i32 %id
  %ptrC = getelementptr float, float addrspace(1)* %C, i32 %id

  ; Read A, B
  %valA = load float, float addrspace(1)* %ptrA, align 4
  %valB = load float, float addrspace(1)* %ptrB, align 4

  ; Compute C = pow(A, B)
  %valC = call float @__nv_powf(float %valA, float %valB)

  ; Store back to C
  store float %valC, float addrspace(1)* %ptrC, align 4

  ret void
}

!nvvm.annotations = !{!0}
!0 = !{void (float addrspace(1)*,
             float addrspace(1)*,
             float addrspace(1)*)* @kernel, !"kernel", i32 1}

To compile this kernel, we perform the following steps:

  1. Link with libdevice
  2. Internalize all but the public kernel function
  3. Run NVVMReflect and set __CUDA_FTZ to 0
  4. Optimize the linked module
  5. Codegen the module

These steps can be performed by the LLVM llvm-link, opt, and llc tools. In a complete compiler, these steps can also be performed entirely programmatically by setting up an appropriate pass configuration (see Linking with Libdevice).

# llvm-link t2.bc libdevice.compute_20.10.bc -o t2.linked.bc
# opt -internalize -internalize-public-api-list=kernel -nvvm-reflect-list=__CUDA_FTZ=0 -nvvm-reflect -O3 t2.linked.bc -o t2.opt.bc
# llc -mcpu=sm_20 t2.opt.bc -o t2.ptx

Note

The -nvvm-reflect-list=_CUDA_FTZ=0 is not strictly required, as any undefined variables will default to zero. It is shown here for evaluation purposes.

This gives us the following PTX (excerpt):

//
// Generated by LLVM NVPTX Back-End
//

.version 3.1
.target sm_20
.address_size 64

  // .globl kernel
                                        // @kernel
.visible .entry kernel(
  .param .u64 kernel_param_0,
  .param .u64 kernel_param_1,
  .param .u64 kernel_param_2
)
{
  .reg .pred  %p<30>;
  .reg .f32   %f<111>;
  .reg .s32   %r<21>;
  .reg .s64   %rl<8>;

// BB#0:                                // %entry
  ld.param.u64  %rl2, [kernel_param_0];
  mov.u32   %r3, %tid.x;
  ld.param.u64  %rl3, [kernel_param_1];
  mul.wide.s32  %rl4, %r3, 4;
  add.s64   %rl5, %rl2, %rl4;
  ld.param.u64  %rl6, [kernel_param_2];
  add.s64   %rl7, %rl3, %rl4;
  add.s64   %rl1, %rl6, %rl4;
  ld.global.f32   %f1, [%rl5];
  ld.global.f32   %f2, [%rl7];
  setp.eq.f32 %p1, %f1, 0f3F800000;
  setp.eq.f32 %p2, %f2, 0f00000000;
  or.pred   %p3, %p1, %p2;
  @%p3 bra  BB0_1;
  bra.uni   BB0_2;
BB0_1:
  mov.f32   %f110, 0f3F800000;
  st.global.f32   [%rl1], %f110;
  ret;
BB0_2:                                  // %__nv_isnanf.exit.i
  abs.f32   %f4, %f1;
  setp.gtu.f32  %p4, %f4, 0f7F800000;
  @%p4 bra  BB0_4;
// BB#3:                                // %__nv_isnanf.exit5.i
  abs.f32   %f5, %f2;
  setp.le.f32 %p5, %f5, 0f7F800000;
  @%p5 bra  BB0_5;
BB0_4:                                  // %.critedge1.i
  add.f32   %f110, %f1, %f2;
  st.global.f32   [%rl1], %f110;
  ret;
BB0_5:                                  // %__nv_isinff.exit.i

  ...

BB0_26:                                 // %__nv_truncf.exit.i.i.i.i.i
  mul.f32   %f90, %f107, 0f3FB8AA3B;
  cvt.rzi.f32.f32 %f91, %f90;
  mov.f32   %f92, 0fBF317200;
  fma.rn.f32  %f93, %f91, %f92, %f107;
  mov.f32   %f94, 0fB5BFBE8E;
  fma.rn.f32  %f95, %f91, %f94, %f93;
  mul.f32   %f89, %f95, 0f3FB8AA3B;
  // inline asm
  ex2.approx.ftz.f32 %f88,%f89;
  // inline asm
  add.f32   %f96, %f91, 0f00000000;
  ex2.approx.f32  %f97, %f96;
  mul.f32   %f98, %f88, %f97;
  setp.lt.f32 %p15, %f107, 0fC2D20000;
  selp.f32  %f99, 0f00000000, %f98, %p15;
  setp.gt.f32 %p16, %f107, 0f42D20000;
  selp.f32  %f110, 0f7F800000, %f99, %p16;
  setp.eq.f32 %p17, %f110, 0f7F800000;
  @%p17 bra   BB0_28;
// BB#27:
  fma.rn.f32  %f110, %f110, %f108, %f110;
BB0_28:                                 // %__internal_accurate_powf.exit.i
  setp.lt.f32 %p18, %f1, 0f00000000;
  setp.eq.f32 %p19, %f3, 0f3F800000;
  and.pred    %p20, %p18, %p19;
  @!%p20 bra  BB0_30;
  bra.uni   BB0_29;
BB0_29:
  mov.b32    %r9, %f110;
  xor.b32   %r10, %r9, -2147483648;
  mov.b32    %f110, %r10;
BB0_30:                                 // %__nv_powf.exit
  st.global.f32   [%rl1], %f110;
  ret;
}