mirror of
https://github.com/RPCS3/llvm-mirror.git
synced 2024-11-25 20:23:11 +01:00
5cf995662b
Summary: These intrinsics should take a generic input address space and outputs a non-generic address space. Test Plan: no Reviewers: jholewinski, eliben Reviewed By: eliben Subscribers: eliben, jholewinski, llvm-commits Differential Revision: http://reviews.llvm.org/D10132 llvm-svn: 238620
981 lines
29 KiB
ReStructuredText
981 lines
29 KiB
ReStructuredText
=============================
|
|
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:
|
|
|
|
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.
|
|
|
|
|
|
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:
|
|
|
|
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 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:
|
|
"""""""
|
|
|
|
.. 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.
|
|
|
|
|
|
.. _libdevice:
|
|
|
|
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 <http://docs.nvidia.com/cuda/libdevice-users-guide/index.html>`_.
|
|
|
|
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:
|
|
|
|
.. code-block:: c++
|
|
|
|
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:
|
|
|
|
.. code-block:: llvm
|
|
|
|
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``:
|
|
|
|
.. code-block:: text
|
|
|
|
# opt -nvvm-reflect -nvvm-reflect-list=<var>=<value>,<var>=<value> module.bc -o module.reflect.bc
|
|
|
|
|
|
With programmatic pass pipeline:
|
|
|
|
.. code-block:: c++
|
|
|
|
extern ModulePass *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:
|
|
|
|
.. 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.
|
|
|
|
|
|
Common Issues
|
|
=============
|
|
|
|
ptxas complains of undefined function: __nvvm_reflect
|
|
-----------------------------------------------------
|
|
|
|
When linking with libdevice, the ``NVVMReflect`` pass must be used. See
|
|
:ref:`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
|
|
----------
|
|
|
|
.. code-block:: llvm
|
|
|
|
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 addrspace(1)* %A, i32 %id
|
|
%ptrB = getelementptr float addrspace(1)* %B, i32 %id
|
|
%ptrC = getelementptr float addrspace(1)* %C, i32 %id
|
|
|
|
; Read A, B
|
|
%valA = load float addrspace(1)* %ptrA, align 4
|
|
%valB = load 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 = metadata !{void (float addrspace(1)*,
|
|
float addrspace(1)*,
|
|
float addrspace(1)*)* @kernel, metadata !"kernel", i32 1}
|
|
|
|
|
|
We can use the LLVM ``llc`` tool to directly run the NVPTX code generator:
|
|
|
|
.. code-block:: text
|
|
|
|
# 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):
|
|
|
|
.. code-block:: text
|
|
|
|
//
|
|
// 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:
|
|
|
|
.. code-block:: llvm
|
|
|
|
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:
|
|
|
|
.. code-block:: llvm
|
|
|
|
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 :ref:`address_spaces` and :ref:`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:
|
|
|
|
.. code-block:: text
|
|
|
|
metadata !{<function ref>, metadata !"kernel", i32 1}
|
|
|
|
For the previous example, we have:
|
|
|
|
.. code-block:: llvm
|
|
|
|
!nvvm.annotations = !{!0}
|
|
!0 = metadata !{void (float addrspace(1)*,
|
|
float addrspace(1)*,
|
|
float addrspace(1)*)* @kernel, metadata !"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.
|
|
|
|
|
|
.. code-block:: c++
|
|
|
|
#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.
|
|
|
|
.. code-block:: text
|
|
|
|
# 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:
|
|
|
|
.. code-block:: text
|
|
|
|
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.
|
|
|
|
.. code-block:: llvm
|
|
|
|
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 addrspace(1)* %A, i32 %id
|
|
%ptrB = getelementptr float addrspace(1)* %B, i32 %id
|
|
%ptrC = getelementptr float addrspace(1)* %C, i32 %id
|
|
|
|
; Read A, B
|
|
%valA = load float addrspace(1)* %ptrA, align 4
|
|
%valB = load 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 = metadata !{void (float addrspace(1)*,
|
|
float addrspace(1)*,
|
|
float addrspace(1)*)* @kernel, metadata !"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
|
|
:ref:`libdevice`).
|
|
|
|
.. code-block:: text
|
|
|
|
# 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):
|
|
|
|
.. code-block:: text
|
|
|
|
//
|
|
// 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;
|
|
}
|
|
|