User Guide for NVPTX Back-end — LLVM 20.1.8 documentation

# 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(ptr %ptr) {
      %val = load float, ptr %ptr
      %ret = call float @my_fmad(float %val, float %val, float %val)
      store float %ret, ptr %ptr
      ret void
    }

    !nvvm.annotations = !{!1}
    !1 = !{ptr @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

### 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.

### Electing a thread

#### ‘`llvm.nvvm.elect.sync`’

##### Syntax:

    declare {i32, i1} @llvm.nvvm.elect.sync(i32 %membermask)

##### Overview:

The ‘`@llvm.nvvm.elect.sync`’ intrinsic generates the `elect.sync` PTX instruction, which elects one predicated active leader thread from a set of threads specified by `membermask`. The behavior is undefined if the executing thread is not in `membermask`. The laneid of the elected thread is captured in the i32 return value. The i1 return value is set to `True` for the leader thread and `False` for all the other threads. Election of a leader thread happens deterministically, i.e. the same leader thread is elected for the same `membermask` every time. For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-elect-sync.

### Membar/Fences

#### ‘`llvm.nvvm.fence.proxy.tensormap_generic.*`’

##### Syntax:

    declare void @llvm.nvvm.fence.proxy.tensormap_generic.release.cta()
    declare void @llvm.nvvm.fence.proxy.tensormap_generic.release.cluster()
    declare void @llvm.nvvm.fence.proxy.tensormap_generic.release.gpu()
    declare void @llvm.nvvm.fence.proxy.tensormap_generic.release.sys()

    declare void @llvm.nvvm.fence.proxy.tensormap_generic.acquire.cta(ptr %addr, i32 %size)
    declare void @llvm.nvvm.fence.proxy.tensormap_generic.acquire.cluster(ptr %addr, i32 %size)
    declare void @llvm.nvvm.fence.proxy.tensormap_generic.acquire.gpu(ptr %addr, i32 %size)
    declare void @llvm.nvvm.fence.proxy.tensormap_generic.acquire.sys(ptr %addr, i32 %size)

##### Overview:

The `@llvm.nvvm.fence.proxy.tensormap_generic.*` is a uni-directional fence used to establish ordering between a prior memory access performed via the generic proxy<https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#proxies>_ and a subsequent memory access performed via the tensormap proxy. `nvvm.fence.proxy.tensormap_generic.release` can form a release sequence that synchronizes with an acquire sequence that contains the `nvvm.fence.proxy.tensormap_generic.acquire` proxy fence. The following table describes the mapping between LLVM Intrinsic and the PTX instruction:

> NVVM Intrinsic | PTX Instruction
> --- | ---
> `@llvm.nvvm.fence.proxy.tensormap_generic.release.*` | `fence.proxy.tensormap::generic.release.*`
> `@llvm.nvvm.fence.proxy.tensormap_generic.acquire.*` | `fence.proxy.tensormap::generic.acquire.* [addr], size`

The address operand `addr` and the operand `size` together specify the memory range `[addr, addr+size)` on which the ordering guarantees on the memory accesses across the proxies is to be provided. The only supported value for the `size` operand is `128` and must be an immediate. Generic Addressing is used unconditionally, and the address specified by the operand addr must fall within the `.global` state space. Otherwise, the behavior is undefined. For more information, see PTX ISA.

### Address Space Intrinsics

#### ‘`llvm.nvvm.isspacep.*`’ Intrinsics

##### Syntax:

    declare i1 @llvm.nvvm.isspacep.const(ptr %p)
    declare i1 @llvm.nvvm.isspacep.global(ptr %p)
    declare i1 @llvm.nvvm.isspacep.local(ptr %p)
    declare i1 @llvm.nvvm.isspacep.shared(ptr %p)
    declare i1 @llvm.nvvm.isspacep.shared.cluster(ptr %p)

##### Overview:

The ‘`llvm.nvvm.isspacep.*`’ intrinsics determine whether the provided generic pointer references memory which falls within a particular address space.

##### Semantics:

If the given pointer in the generic address space refers to memory which falls within the state space of the intrinsic (and therefore could be safely address space casted to this space), 1 is returned, otherwise 0 is returned.

### Arithmetic Intrinsics

#### ‘`llvm.nvvm.idp2a.[us].[us]`’ Intrinsics

##### Syntax:

    declare i32 @llvm.nvvm.idp2a.s.s(i32 %a, i32 %b, i1 immarg %is.hi, i32 %c)
    declare i32 @llvm.nvvm.idp2a.s.u(i32 %a, i32 %b, i1 immarg %is.hi, i32 %c)
    declare i32 @llvm.nvvm.idp2a.u.s(i32 %a, i32 %b, i1 immarg %is.hi, i32 %c)
    declare i32 @llvm.nvvm.idp2a.u.u(i32 %a, i32 %b, i1 immarg %is.hi, i32 %c)

##### Overview:

The ‘`llvm.nvvm.idp2a.[us].[us]`’ intrinsics performs a 2-element vector dot product followed by addition. They corresponds directly to the `dp2a` PTX instruction.

##### Semantics:

The 32-bit value in `%a` is broken into 2 16-bit values which are extended to 32 bits. For the ‘`llvm.nvvm.idp2a.u.[us]`’ variants zero-extension is used, while for the ‘`llvm.nvvm.idp2a.s.[us]`’ sign-extension is used. Two bytes are selected from `%b`, if `%is.hi` is true, the most significant bytes are selected, otherwise the least significant bytes are selected. These bytes are then extended to 32-bits. For the ‘`llvm.nvvm.idp2a.[us].u`’ variants zero-extension is used, while for the ‘`llvm.nvvm.idp2a.[us].s`’ sign-extension is used. The dot product of these 2-element vectors is added to `%c` to produce the return.

#### ‘`llvm.nvvm.idp4a.[us].[us]`’ Intrinsics

##### Syntax:

    declare i32 @llvm.nvvm.idp4a.s.s(i32 %a, i32 %b, i32 %c)
    declare i32 @llvm.nvvm.idp4a.s.u(i32 %a, i32 %b, i32 %c)
    declare i32 @llvm.nvvm.idp4a.u.s(i32 %a, i32 %b, i32 %c)
    declare i32 @llvm.nvvm.idp4a.u.u(i32 %a, i32 %b, i32 %c)

##### Overview:

The ‘`llvm.nvvm.idp4a.[us].[us]`’ intrinsics perform a 4-element vector dot product followed by addition. They corresponds directly to the `dp4a` PTX instruction.

##### Semantics:

Each of the 4 bytes in both `%a` and `%b` are extended to 32-bit integers forming 2 `<4 x i32>`. For `%a`, zero-extension is used in the ‘`llvm.nvvm.idp4a.u.[us]`’ variants, while sign-extension is used with ‘`llvm.nvvm.idp4a.s.[us]`’ variants. Similarly, for `%b`, zero-extension is used in the ‘`llvm.nvvm.idp4a.[us].u`’ variants, while sign-extension is used with ‘`llvm.nvvm.idp4a.[us].s`’ variants. The dot product of these 4-element vectors is added to `%c` to produce the return.

### Bit Manipulation Intrinsics

#### ‘`llvm.nvvm.fshl.clamp.*`’ Intrinsic

##### Syntax:

    declare i32 @llvm.nvvm.fshl.clamp.i32(i32 %hi, i32 %lo, i32 %n)

##### Overview:

The ‘`llvm.nvvm.fshl.clamp`’ family of intrinsics performs a clamped funnel shift left. These intrinsics are very similar to ‘`llvm.fshl`’, except the shift ammont is clamped at the integer width (instead of modulo it). Currently, only `i32` is supported.

##### Semantics:

The ‘`llvm.nvvm.fshl.clamp`’ family of intrinsic functions performs a clamped funnel shift left: the first two values are concatenated as { %hi : %lo } (%hi is the most significant bits of the wide value), the combined value is shifted left, and the most significant bits are extracted to produce a result that is the same size as the original arguments. The shift amount is the minimum of the value of %n and the bit width of the integer type.

#### ‘`llvm.nvvm.fshr.clamp.*`’ Intrinsic

##### Syntax:

    declare i32 @llvm.nvvm.fshr.clamp.i32(i32 %hi, i32 %lo, i32 %n)

##### Overview:

The ‘`llvm.nvvm.fshr.clamp`’ family of intrinsics perform a clamped funnel shift right. These intrinsics are very similar to ‘`llvm.fshr`’, except the shift ammont is clamped at the integer width (instead of modulo it). Currently, only `i32` is supported.

##### Semantics:

The ‘`llvm.nvvm.fshr.clamp`’ family of intrinsic functions performs a clamped funnel shift right: the first two values are concatenated as { %hi : %lo } (%hi is the most significant bits of the wide value), the combined value is shifted right, and the least significant bits are extracted to produce a result that is the same size as the original arguments. The shift amount is the minimum of the value of %n and the bit width of the integer type.

#### ‘`llvm.nvvm.flo.u.*`’ Intrinsic

##### Syntax:

    declare i32 @llvm.nvvm.flo.u.i32(i32 %a, i1 %shiftamt)
    declare i32 @llvm.nvvm.flo.u.i64(i64 %a, i1 %shiftamt)

##### Overview:

The ‘`llvm.nvvm.flo.u`’ family of intrinsics identifies the bit position of the leading one, returning either it’s offset from the most or least significant bit.

##### Semantics:

The ‘`llvm.nvvm.flo.u`’ family of intrinsics returns the bit position of the most significant 1. If %shiftamt is true, The result is the shift amount needed to left-shift the found bit into the most-significant bit position, otherwise the result is the shift amount needed to right-shift the found bit into the least-significant bit position. 0xffffffff is returned if no 1 bit is found.

#### ‘`llvm.nvvm.flo.s.*`’ Intrinsic

##### Syntax:

    declare i32 @llvm.nvvm.flo.s.i32(i32 %a, i1 %shiftamt)
    declare i32 @llvm.nvvm.flo.s.i64(i64 %a, i1 %shiftamt)

##### Overview:

The ‘`llvm.nvvm.flo.s`’ family of intrinsics identifies the bit position of the leading non-sign bit, returning either it’s offset from the most or least significant bit.

##### Semantics:

The ‘`llvm.nvvm.flo.s`’ family of intrinsics returns the bit position of the most significant 0 for negative inputs and the most significant 1 for non-negative inputs. If %shiftamt is true, The result is the shift amount needed to left-shift the found bit into the most-significant bit position, otherwise the result is the shift amount needed to right-shift the found bit into the least-significant bit position. 0xffffffff is returned if no 1 bit is found.

### TMA family of Intrinsics

#### ‘`llvm.nvvm.cp.async.bulk.global.to.shared.cluster`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.global.to.shared.cluster(ptr addrspace(3) %dst, ptr addrspace(3) %mbar, ptr addrspace(1) %src, i32 %size, i16 %mc, i64 %ch, i1 %flag_mc, i1 %flag_ch)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.global.to.shared.cluster`’ intrinsic corresponds to the `cp.async.bulk.shared::cluster.global.*` family of PTX instructions. These instructions initiate an asynchronous copy of bulk data from global memory to shared::cluster memory. The 32-bit operand `%size` specifies the amount of memory to be copied and it must be a multiple of 16.

- The last two arguments to these intrinsics are boolean flags indicating support for cache_hint and/or multicast modifiers. These flag arguments must be compile-time constants. The backend looks through these flags and lowers the intrinsics appropriately.

- The Nth argument (denoted by `i1 %flag_ch`) when set, indicates a valid cache_hint (`i64 %ch`) and generates the `.L2::cache_hint` variant of the PTX instruction.

- The [N-1]th argument (denoted by `i1 %flag_mc`) when set, indicates the presence of a multicast mask (`i16 %mc`) and generates the PTX instruction with the `.multicast::cluster` modifier.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk.

#### ‘`llvm.nvvm.cp.async.bulk.shared.cta.to.global`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.shared.cta.to.global(ptr addrspace(1) %dst, ptr addrspace(3) %src, i32 %size, i64 %ch, i1 %flag_ch)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.shared.cta.to.global`’ intrinsic corresponds to the `cp.async.bulk.global.shared::cta.*` set of PTX instructions. These instructions initiate an asynchronous copy from shared::cta to global memory. The 32-bit operand `%size` specifies the amount of memory to be copied and it must be a multiple of 16.

- The last argument to these intrinsics is a boolean flag indicating support for cache_hint. This flag argument must be a compile-time constant. When set, it indicates a valid cache_hint (`i64 %ch`) and generates the `.L2::cache_hint` variant of the PTX instruction.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk.

#### ‘`llvm.nvvm.cp.async.bulk.shared.cta.to.cluster`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.shared.cta.to.cluster(ptr addrspace(3) %dst, ptr addrspace(3) %mbar, ptr addrspace(3) %src, i32 %size)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.shared.cta.to.cluster`’ intrinsic corresponds to the `cp.async.bulk.shared::cluster.shared::cta.*` PTX instruction. This instruction initiates an asynchronous copy from shared::cta to shared::cluster memory. The destination has to be in the shared memory of a different CTA within the cluster. The 32-bit operand `%size` specifies the amount of memory to be copied and it must be a multiple of 16.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk.

#### ‘`llvm.nvvm.cp.async.bulk.prefetch.L2`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.prefetch.L2(ptr addrspace(1) %src, i32 %size, i64 %ch, i1 %flag_ch)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.prefetch.L2`’ intrinsic corresponds to the `cp.async.bulk.prefetch.L2.*` family of PTX instructions. These instructions initiate an asynchronous prefetch of bulk data from global memory to the L2 cache. The 32-bit operand `%size` specifies the amount of memory to be prefetched in terms of bytes and it must be a multiple of 16.

- The last argument to these intrinsics is boolean flag indicating support for cache_hint. These flag argument must be compile-time constant. When set, it indicates a valid cache_hint (`i64 %ch`) and generates the `.L2::cache_hint` variant of the PTX instruction.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/#data-movement-and-conversion-instructions-cp-async-bulk-prefetch.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.g2s.tile.[1-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.tile.1d(ptr addrspace(3) %dst, ptr addrspace(3) %bar, ptr %tensor_map, i32 %d0, i16 %mc, i64 %ch, i1 %flag_mc, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.tile.2d(..., i32 %d0, i32 %d1, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.tile.3d(..., i32 %d0, i32 %d1, i32 %d2, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.tile.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.tile.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.g2s.tile.[1-5]d`’ intrinsics correspond to the `cp.async.bulk.tensor.[1-5]d.*` set of PTX instructions. These instructions initiate an asynchronous copy of tensor data from global memory to shared::cluster memory (indicated by the `g2s` prefix) in `tile` mode. In tile mode, the multi-dimensional layout of the source tensor is preserved at the destination. The dimension of the tensor data ranges from 1d to 5d with the coordinates specified by the `i32 %d0 ... i32 %d4` arguments.

- The last two arguments to these intrinsics are boolean flags indicating support for cache_hint and/or multicast modifiers. These flag arguments must be compile-time constants. The backend looks through these flags and lowers the intrinsics appropriately.

- The Nth argument (denoted by `i1 flag_ch`) when set, indicates a valid cache_hint (`i64 %ch`) and generates the `.L2::cache_hint` variant of the PTX instruction.

- The [N-1]th argument (denoted by `i1 flag_mc`) when set, indicates the presence of a multicast mask (`i16 %mc`) and generates the PTX instruction with the `.multicast::cluster` modifier.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk-tensor.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.g2s.im2col.[3-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.im2col.3d(ptr addrspace(3) %dst, ptr addrspace(3) %bar, ptr %tensor_map, i32 %d0, i32 %d1, i32 %d2, i16 %im2col0, i16 %mc, i64 %ch, i1 %flag_mc, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.im2col.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i16 %im2col0, i16 %im2col1, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.g2s.im2col.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, i16 %im2col0, i16 %im2col1, i16 %im2col2, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.g2s.im2col.[3-5]d`’ intrinsics correspond to the `cp.async.bulk.tensor.[1-5]d.*` set of PTX instructions. These instructions initiate an asynchronous copy of tensor data from global memory to shared::cluster memory (indicated by the `g2s` prefix) in `im2col` mode. In im2col mode, some dimensions of the source tensor are unrolled into a single dimensional column at the destination. In this mode, the tensor has to be at least three-dimensional. Along with the tensor coordinates, im2col offsets are also specified (denoted by `i16 im2col0...i16 %im2col2`). The number of im2col offsets is two less than the number of dimensions of the tensor operation. The last two arguments to these intrinsics are boolean flags, with the same functionality as described in the `tile` mode intrinsics above.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk-tensor.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.s2g.tile.[1-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.tile.2d(..., i32 %d0, i32 %d1, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.tile.3d(..., i32 %d0, i32 %d1, i32 %d2, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.tile.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.tile.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.s2g.tile.[1-5]d`’ intrinsics correspond to the `cp.async.bulk.tensor.[1-5]d.*` set of PTX instructions. These instructions initiate an asynchronous copy of tensor data from shared::cta to global memory (indicated by the `s2g` prefix) in `tile` mode. The dimension of the tensor data ranges from 1d to 5d with the coordinates specified by the `i32 %d0 ... i32 %d4` arguments.

- The last argument to these intrinsics is a boolean flag indicating support for cache_hint. This flag argument must be a compile-time constant. When set, it indicates a valid cache_hint (`i64 %ch`) and generates the `.L2::cache_hint` variant of the PTX instruction.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk-tensor.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.s2g.im2col.[3-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.im2col.3d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i32 %d1, i32 %d2, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.im2col.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.s2g.im2col.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.s2g.im2col.[1-5]d`’ intrinsics correspond to the `cp.async.bulk.tensor.[1-5]d.*` set of PTX instructions. These instructions initiate an asynchronous copy of tensor data from shared::cta to global memory (indicated by the `s2g` prefix) in `im2col` mode. In this mode, the tensor has to be at least three-dimensional. Unlike the `g2s` variants, there are no im2col_offsets for these intrinsics. The last argument to these intrinsics is a boolean flag, with the same functionality as described in the `s2g.tile` mode intrinsics above.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk-tensor.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.prefetch.tile.[1-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.tile.1d(ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.tile.2d(..., i32 %d0, i32 %d1, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.tile.3d(..., i32 %d0, i32 %d1, i32 %d2, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.tile.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.tile.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.prefetch.tile.[1-5]d`’ intrinsics correspond to the `cp.async.bulk.prefetch.tensor.[1-5]d.L2.global*` set of PTX instructions. These instructions initiate an asynchronous prefetch of tensor data from global memory to the L2 cache. In tile mode, the multi-dimensional layout of the source tensor is preserved at the destination. The dimension of the tensor data ranges from 1d to 5d with the coordinates specified by the `i32 %d0 ... i32 %d4` arguments.

- The last argument to these intrinsics is a boolean flag indicating support for cache_hint. This flag argument must be a compile-time constant. When set, it indicates a valid cache_hint (`i64 %ch`) and generates the `.L2::cache_hint` variant of the PTX instruction.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/#data-movement-and-conversion-instructions-cp-async-bulk-prefetch-tensor.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.prefetch.im2col.[3-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.im2col.3d(ptr %tensor_map, i32 %d0, i32 %d1, i32 %d2, i16 %im2col0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.im2col.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i16 %im2col0, i16 %im2col1, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.prefetch.im2col.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, i16 %im2col0, i16 %im2col1, i16 %im2col2, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.prefetch.im2col.[3-5]d`’ intrinsics correspond to the `cp.async.bulk.prefetch.tensor.[1-5]d.L2.global*` set of PTX instructions. These instructions initiate an asynchronous prefetch of tensor data from global memory to the L2 cache. In im2col mode, some dimensions of the source tensor are unrolled into a single dimensional column at the destination. In this mode, the tensor has to be at least three-dimensional. Along with the tensor coordinates, im2col offsets are also specified (denoted by `i16 im2col0...i16 %im2col2`). The number of im2col offsets is two less than the number of dimensions of the tensor operation. The last argument to these intrinsics is a boolean flag, with the same functionality as described in the `tile` mode intrinsics above.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/#data-movement-and-conversion-instructions-cp-async-bulk-prefetch-tensor.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.reduce.[red_op].tile.[1-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.add.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.min.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.max.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.inc.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.dec.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.and.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.or.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.xor.tile.1d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i64 %ch, i1 %flag_ch)

    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.tile.2d(..., i32 %d0, i32 %d1, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.tile.3d(..., i32 %d0, i32 %d1, i32 %d2, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.tile.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.tile.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.tile.[1-5]d`’ intrinsics correspond to the `cp.reduce.async.bulk.tensor.[1-5]d.*` set of PTX instructions. These instructions initiate an asynchronous reduction operation of tensor data in global memory with the tensor data in shared{::cta} memory, using `tile` mode. The dimension of the tensor data ranges from 1d to 5d with the coordinates specified by the `i32 %d0 ... i32 %d4` arguments. The supported reduction operations are {add, min, max, inc, dec, and, or, xor} as described in the `tile.1d` intrinsics.

- The last argument to these intrinsics is a boolean flag indicating support for cache_hint. This flag argument must be a compile-time constant. When set, it indicates a valid cache_hint (`i64 %ch`) and generates the `.L2::cache_hint` variant of the PTX instruction.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-reduce-async-bulk-tensor.

#### ‘`llvm.nvvm.cp.async.bulk.tensor.reduce.[red_op].im2col.[3-5]d`’

##### Syntax:

    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.im2col.3d(ptr addrspace(3) %src, ptr %tensor_map, i32 %d0, i32 %d1, i32 %d2, i64 %ch, i1 %flag_ch)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.im2col.4d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, ...)
    declare void @llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.im2col.5d(..., i32 %d0, i32 %d1, i32 %d2, i32 %d3, i32 %d4, ...)

##### Overview:

The ‘`@llvm.nvvm.cp.async.bulk.tensor.reduce.<red_op>.im2col.[3-5]d`’ intrinsics correspond to the `cp.reduce.async.bulk.tensor.[3-5]d.*` set of PTX instructions. These instructions initiate an asynchronous reduction operation of tensor data in global memory with the tensor data in shared{::cta} memory, using `im2col` mode. In this mode, the tensor has to be at least three-dimensional. The supported reduction operations supported are the same as the ones in the tile mode. The last argument to these intrinsics is a boolean flag, with the same functionality as described in the `tile` mode intrinsics above.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-reduce-async-bulk-tensor.

### Warp Group Intrinsics

#### ‘`llvm.nvvm.wgmma.fence.sync.aligned`’

##### Syntax:

    declare void @llvm.nvvm.wgmma.fence.sync.aligned()

##### Overview:

The ‘`@llvm.nvvm.wgmma.fence.sync.aligned`’ intrinsic generates the `wgmma.fence.sync.aligned` PTX instruction, which establishes an ordering between prior accesses to any warpgroup registers and subsequent accesses to the same registers by a `wgmma.mma_async` instruction.

The `wgmma.fence` instruction must be issued by all warps of the warpgroup in the following locations:

- Before the first `wgmma.mma_async` operation in a warpgroup.

- Between a register access by a thread in the warpgroup and any `wgmma.mma_async` instruction that accesses the same registers, except when these are accumulator register accesses across multiple `wgmma.mma_async` instructions of the same shape in which case an ordering guarantee is provided by default.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/#asynchronous-warpgroup-level-matrix-instructions-wgmma-fence.

#### ‘`llvm.nvvm.wgmma.commit_group.sync.aligned`’

##### Syntax:

    declare void @llvm.nvvm.wgmma.commit_group.sync.aligned()

##### Overview:

The ‘`@llvm.nvvm.wgmma.commit_group.sync.aligned`’ intrinsic generates the `wgmma.commit_group.sync.aligned` PTX instruction, which creates a new wgmma-group per warpgroup and batches all prior `wgmma.mma_async` instructions initiated by the executing warp but not committed to any wgmma-group into the new wgmma-group. If there are no uncommitted `wgmma mma_async` instructions then, `wgmma.commit_group` results in an empty wgmma-group.

An executing thread can wait for the completion of all `wgmma.mma_async` operations in a wgmma-group by using `wgmma.wait_group`.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/#asynchronous-warpgroup-level-matrix-instructions-wgmma-commit-group.

#### ‘`llvm.nvvm.wgmma.wait_group.sync.aligned`’

##### Syntax:

    declare void @llvm.nvvm.wgmma.wait_group.sync.aligned(i64 immarg N)

##### Overview:

The ‘`@llvm.nvvm.wgmma.wait_group.sync.aligned`’ intrinsic generates the `wgmma.commit_group.sync.aligned N` PTX instruction, which will cause the executing thread to wait until only `N` or fewer of the most recent wgmma-groups are pending and all the prior wgmma-groups committed by the executing threads are complete. For example, when `N` is 0, the executing thread waits on all the prior wgmma-groups to complete. Operand `N` is an integer constant.

Accessing the accumulator register or the input register containing the fragments of matrix A of a `wgmma.mma_async` instruction without first performing a `wgmma.wait_group` instruction that waits on a wgmma-group including that `wgmma.mma_async` instruction is undefined behavior.

For more information, refer PTX ISA https://docs.nvidia.com/cuda/parallel-thread-execution/#asynchronous-warpgroup-level-matrix-instructions-wgmma-wait-group.

#### ‘`llvm.nvvm.griddepcontrol.*`’

##### Syntax:

    declare void @llvm.nvvm.griddepcontrol.launch_dependents()
    declare void @llvm.nvvm.griddepcontrol.wait()

##### Overview:

The `griddepcontrol` intrinsics allows the dependent grids and prerequisite grids as defined by the runtime, to control execution in the following way:

`griddepcontrol.launch_dependents` intrinsic signals that the dependents can be scheduled, before the current grid completes. The intrinsic can be invoked by multiple threads in the current CTA and repeated invocations of the intrinsic will have no additional side effects past that of the first invocation.

`griddepcontrol.wait` intrinsic causes the executing thread to wait until all prerequisite grids in flight have completed and all the memory operations from the prerequisite grids are performed and made visible to the current grid.

For more information, refer PTX ISA.

### 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:

The `NVVMReflect` pass will attempt to remove dead code even without optimizations. This allows potentially incompatible instructions to be avoided at all optimizations levels by using the `__CUDA_ARCH` argument.

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.

The NVPTX TargetMachine knows how to schedule `NVVMReflect` at the beginning of your pass manager; just use the following code when setting up your pass manager and the PassBuilder will use `registerPassBuilderCallbacks` to let NVPTXTargetMachine::registerPassBuilderCallbacks add the pass to the pass manager:

    std::unique_ptr<TargetMachine> TM = ...;
    PassBuilder PB(TM);
    ModulePassManager MPM;
    PB.parsePassPipeline(MPM, ...);

### 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

The value of this flag is determined by the “nvvm-reflect-ftz” module flag. The following sets the ftz flag to 1.

    !llvm.module.flags = !{!0}
    !0 = !{i32 4, !"nvvm-reflect-ftz", i32 1}

(`i32 4` indicates that the value set here overrides the value in another module we link with. See the LangRef <LangRef.html#module-flags-metadata> for details.)

## 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(ptr addrspace(1) %A,
                        ptr addrspace(1) %B,
                        ptr 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, ptr addrspace(1) %A, i32 %id
      %ptrB = getelementptr float, ptr addrspace(1) %B, i32 %id
      %ptrC = getelementptr float, ptr addrspace(1) %C, i32 %id

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

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

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

      ret void
    }

    !nvvm.annotations = !{!0}
    !0 = !{ptr @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.nvvm.barrier0()` | __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 = !{ptr @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(ptr addrspace(1) %A,
                        ptr addrspace(1) %B,
                        ptr 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, ptr addrspace(1) %A, i32 %id
      %ptrB = getelementptr float, ptr addrspace(1) %B, i32 %id
      %ptrC = getelementptr float, ptr addrspace(1) %C, i32 %id

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

      ret void
    }

    !nvvm.annotations = !{!0}
    !0 = !{ptr @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;
    }