C++ Language Extensions

HIP provides a C++ syntax that is suitable for compiling most code that commonly appears in compute kernels (classes, namespaces, operator overloading, and templates). HIP also defines other language features that are designed to target accelerators, such as:

A kernel-launch syntax that uses standard C++ (this resembles a function call and is portable to all HIP targets)
Short-vector headers that can serve on a host or device
Math functions that resemble those in math.h, which is included with standard C++ compilers
Built-in functions for accessing specific GPU hardware capabilities

Note

This chapter describes the built-in variables and functions that are accessible from the HIP kernel. It's intended for users who are familiar with CUDA kernel syntax and want to learn how HIP differs from CUDA.

Features are labeled with one of the following keywords:

Supported: HIP supports the feature with a CUDA-equivalent function
Not supported: HIP does not support the feature
Under development: The feature is under development and not yet available

Function-type qualifiers

`device`

Supported __device__ functions are:

Run on the device

Called from the device only

You can combine __device__ with the host keyword (host_attr).

`global`

Supported __global__ functions are:

Run on the device

Called (launched) from the host

HIP __global__ functions must have a void return type. The first parameter in a HIP __global__ function must have the type hipLaunchParm. Refer to kernel-launch-example to see usage.

HIP doesn't support dynamic-parallelism, which means that you can't call __global__ functions from the device.

`host`

Supported __host__ functions are:

Run on the host

Called from the host

You can combine __host__ with __device__; in this case, the function compiles for the host and the device. Note that these functions can't use the HIP grid coordinate functions (e.g., threadIdx.x). If you need to use HIP grid coordinate functions, you can pass the necessary coordinate information as an argument.

You can't combine __host__ with __global__.

HIP parses the __noinline__ and __forceinline__ keywords and converts them into the appropriate Clang attributes.

Calling `global` functions

__global__ functions are often referred to as kernels. When you call a global function, you're launching a kernel. When launching a kernel, you must specify an execution configuration that includes the grid and block dimensions. The execution configuration can also include other information for the launch, such as the amount of additional shared memory to allocate and the stream where you want to execute the kernel.

HIP introduces a standard C++ calling convention (hipLaunchKernelGGL) to pass the run configuration to the kernel. However, you can also use the CUDA <<< >>> syntax.

When using hipLaunchKernelGGL, your first five parameters must be:

symbol kernelName: The name of the kernel you want to launch. To support template kernels that contain ",", use the HIP_KERNEL_NAME macro (HIPIFY tools insert this automatically).

dim3 gridDim: 3D-grid dimensions that specify the number of blocks to launch.

dim3 blockDim: 3D-block dimensions that specify the number of threads in each block.

size_t dynamicShared: The amount of additional shared memory that you want to allocate when launching the kernel (see shared-variable-type).

hipStream_t: The stream where you want to run the kernel. A value of 0 corresponds to the NULL stream (see synchronization functions).

You can include your kernel arguments after these parameters.

// Example hipLaunchKernelGGL pseudocode:
__global__ MyKernel(hipLaunchParm lp, float *A, float *B, float *C, size_t N)
{
...
}

MyKernel<<<dim3(gridDim), dim3(groupDim), 0, 0>>> (a,b,c,n);

// Alternatively, you can launch the kernel using:
// hipLaunchKernelGGL(MyKernel, dim3(gridDim), dim3(groupDim), 0/*dynamicShared*/, 0/*stream), a, b, c, n);

You can use HIPIFY tools to convert CUDA launch syntax to hipLaunchKernelGGL. This includes the conversion of optional <<< >>> arguments into the five required hipLaunchKernelGGL parameters.

Note

HIP doesn't support dimension sizes of gridDim * blockDim ≥ 2³² when launching a kernel.

Kernel launch example

// Example showing device function, __device__ __host__
// <- compile for both device and host
float PlusOne(float x)
{
  return x + 1.0;
}

__global__
void
MyKernel (hipLaunchParm lp, /*lp parm for execution configuration */
          const float *a, const float *b, float *c, unsigned N)
{
  unsigned gid = threadIdx.x; // <- coordinate index function
  if (gid < N) {
    c[gid] = a[gid] + PlusOne(b[gid]);
  }
}
void callMyKernel()
{
  float *a, *b, *c; // initialization not shown...
  unsigned N = 1000000;
  const unsigned blockSize = 256;

  MyKernel<<<dim3(gridDim), dim3(groupDim), 0, 0>>> (a,b,c,n);
  // Alternatively, kernel can be launched by
  // hipLaunchKernelGGL(MyKernel, dim3(N/blockSize), dim3(blockSize), 0, 0,  a,b,c,N);
}

Variable type qualifiers

`constant`

The host writes constant memory before launching the kernel. This memory is read-only from the GPU while the kernel is running. The functions for accessing constant memory are:

hipGetSymbolAddress()
hipGetSymbolSize()
hipMemcpyToSymbol()
hipMemcpyToSymbolAsync()
hipMemcpyFromSymbol()
hipMemcpyFromSymbolAsync()

`shared`

To allow the host to dynamically allocate shared memory, you can specify extern __shared__ as a launch parameter.

Note

Prior to the HIP-Clang compiler, dynamic shared memory had to be declared using the HIP_DYNAMIC_SHARED macro in order to ensure accuracy. This is because using static shared memory in the same kernel could've resulted in overlapping memory ranges and data-races. The HIP-Clang compiler provides support for extern __shared_ declarations, so HIP_DYNAMIC_SHARED is no longer required.

`managed`

Managed memory, including the __managed__ keyword, is supported in HIP combined host/device compilation.

`restrict`

__restrict__ tells the compiler that the associated memory pointer not to alias with any other pointer in the kernel or function. This can help the compiler generate better code. In most use cases, every pointer argument should use this keyword in order to achieve the benefit.

Built-in variables

Coordinate built-ins

The kernel uses coordinate built-ins (thread*, block*, grid*) to determine the coordinate index and bounds for the active work item.

Built-ins are defined in amd_hip_runtime.h, rather than being implicitly defined by the compiler.

Coordinate variable definitions for built-ins are the same for HIP and CUDA. For example: threadIdx.x, blockIdx.y, and gridDim.y. The products gridDim.x * blockDim.x, gridDim.y * blockDim.y, and gridDim.z * blockDim.z are always less than 2^32.

Coordinate built-ins are implemented as structures for improved performance. When used with printf, they must be explicitly cast to integer types.

warpSize

The warpSize variable type is int. It contains the warp size (in threads) for the target device. warpSize should only be used in device functions that develop portable wave-aware code.

Note

NVIDIA devices return 32 for this variable; AMD devices return 64 for gfx9 and 32 for gfx10 and above.

Vector types

The following vector types are defined in hip_runtime.h. They are not automatically provided by the compiler.

Short vector types

Short vector types derive from basic integer and floating-point types. These structures are defined in hip_vector_types.h. The first, second, third, and fourth components of the vector are defined by the x, y, z, and w fields, respectively. All short vector types support a constructor function of the form make_<type_name>(). For example, float4 make_float4(float x, float y, float z, float w) creates a vector with type float4 and value (x,y,z,w).

HIP supports the following short vector formats:

Signed Integers:
- char1, char2, char3, char4
- short1, short2, short3, short4
- int1, int2, int3, int4
- long1, long2, long3, long4
- longlong1, longlong2, longlong3, longlong4
Unsigned Integers:
- uchar1, uchar2, uchar3, uchar4
- ushort1, ushort2, ushort3, ushort4
- uint1, uint2, uint3, uint4
- ulong1, ulong2, ulong3, ulong4
- ulonglong1, ulonglong2, ulonglong3, ulonglong4
Floating Points:
- float1, float2, float3, float4
- double1, double2, double3, double4

dim3

dim3 is a three-dimensional integer vector type that is commonly used to specify grid and group dimensions.

The dim3 constructor accepts between zero and three arguments. By default, it initializes unspecified dimensions to 1.

typedef struct dim3 {
  uint32_t x;
  uint32_t y;
  uint32_t z;

  dim3(uint32_t _x=1, uint32_t _y=1, uint32_t _z=1) : x(_x), y(_y), z(_z) {};
};

Memory fence instructions

HIP supports __threadfence() and __threadfence_block(). If you're using threadfence_system() in the HIP-Clang path, you can use the following workaround:

Build HIP with the HIP_COHERENT_HOST_ALLOC environment variable enabled.
Modify kernels that use __threadfence_system() as follows:

Ensure the kernel operates only on fine-grained system memory, which should be allocated with hipHostMalloc().

Remove memcpy for all allocated fine-grained system memory regions.

Synchronization functions

The __syncthreads() built-in function is supported in HIP. The __syncthreads_count(int), __syncthreads_and(int), and __syncthreads_or(int) functions are under development.

Math functions

HIP-Clang supports a set of math operations that are callable from the device. HIP supports most of the device functions supported by CUDA. These are described in the following sections.

Single precision mathematical functions

Following is the list of supported single precision mathematical functions.

Single precision mathematical functions

Function	Supported on Host	Supported on Device
float acosf ( float x ) Calculate the arc cosine of the input argument.	✓	✓
float acoshf ( float x ) Calculate the nonnegative arc hyperbolic cosine of the input argument.	✓	✓
float asinf ( float x ) Calculate the arc sine of the input argument	✓	✓
float asinhf ( float x ) Calculate the arc hyperbolic sine of the input argument.	✓	✓
float atan2f ( float y, float x ) Calculate the arc tangent of the ratio of first and second input arguments.	✓	✓
float atanf ( float x ) Calculate the arc tangent of the input argument.	✓	✓
float atanhf ( float x ) Calculate the arc hyperbolic tangent of the input argument.	✓	✓
float cbrtf ( float x ) Calculate the cube root of the input argument.	✓	✓
float ceilf ( float x ) Calculate ceiling of the input argument.	✓	✓
float copysignf ( float x, float y ) Create value with given magnitude, copying sign of second value.	✓	✓
float copysignf ( float x, float y ) Create value with given magnitude, copying sign of second value.	✓	✓
float cosf ( float x ) Calculate the cosine of the input argument.	✓	✓
float coshf ( float x ) Calculate the hyperbolic cosine of the input argument.	✓	✓
float erfcf ( float x ) Calculate the complementary error function of the input argument.	✓	✓
float erff ( float x ) Calculate the error function of the input argument.	✓	✓
float exp10f ( float x ) Calculate the base 10 exponential of the input argument.	✓	✓
float exp2f ( float x ) Calculate the base 2 exponential of the input argument.	✓	✓
float expf ( float x ) Calculate the base e exponential of the input argument.	✓	✓
float expm1f ( float x ) Calculate the base e exponential of the input argument, minus 1.	✓	✓
float fabsf ( float x ) Calculate the absolute value of its argument.	✓	✓
float fdimf ( float x, float y ) Compute the positive difference between x and y.	✓	✓
float floorf ( float x ) Calculate the largest integer less than or equal to x.	✓	✓
float fmaf ( float x, float y, float z ) Compute x × y + z as a single operation.	✓	✓
float fmaxf ( float x, float y ) Determine the maximum numeric value of the arguments.	✓	✓
float fminf ( float x, float y ) Determine the minimum numeric value of the arguments.	✓	✓
float fmodf ( float x, float y ) Calculate the floating-point remainder of x / y.	✓	✓
float frexpf ( float x, int* nptr ) Extract mantissa and exponent of a floating-point value.	✓	✗
float hypotf ( float x, float y ) Calculate the square root of the sum of squares of two arguments.	✓	✓
int ilogbf ( float x ) Compute the unbiased integer exponent of the argument.	✓	✓
__RETURN_TYPE isfinite ( float a ) Determine whether argument is finite.	✓	✓
__RETURN_TYPE isinf ( float a ) Determine whether argument is infinite.	✓	✓
__RETURN_TYPE isnan ( float a ) Determine whether argument is a NaN.	✓	✓
float ldexpf ( float x, int exp ) Calculate the value of x ⋅ 2 of the exponent of the input argument.	✓	✓
loat log10f ( float x ) Calculate the base 10 logarithm of the input argument.	✓	✓
float log1pf ( float x ) Calculate the value of the exponent of the input argument	✓	✓
float logbf ( float x ) Calculate the floating point representation of the exponent of the input argument.	✓	✓
float log2f ( float x ) Calculate the base 2 logarithm of the input argument.	✓	✓
float logf ( float x ) Calculate the natural logarithm of the input argument.	✓	✓
float modff ( float x, float* iptr ) Break down the input argument into fractional and integral parts.	✓	✗
float nanf ( const char* tagp ) Returns "Not a Number" value.	✗	✓
float nearbyintf ( float x ) Round the input argument to the nearest integer.	✓	✓
float powf ( float x, float y ) Calculate the value of first argument to the power of second argument.	✓	✓
float remainderf ( float x, float y ) Compute single-precision floating-point remainder.	✓	✓
float remquof ( float x, float y, int* quo ) Compute single-precision floating-point remainder and part of quotient.	✓	✗
float roundf ( float x ) Round to nearest integer value in floating-point.	✓	✓
float scalbnf ( float x, int n ) Scale floating-point input by integer power of two.	✓	✓
__RETURN_TYPE signbit ( float a ) Return the sign bit of the input.	✓	✓
void sincosf ( float x, float* sptr, float* cptr ) Calculate the sine and cosine of the first input argument.	✓	✗
float sinf ( float x ) Calculate the sine of the input argument.	✓	✓
float sinhf ( float x ) Calculate the hyperbolic sine of the input argument.	✓	✓
float sqrtf ( float x ) Calculate the square root of the input argument.	✓	✓
float tanf ( float x ) Calculate the tangent of the input argument.	✓	✓
float tanhf ( float x ) Calculate the hyperbolic tangent of the input argument.	✓	✓
float truncf ( float x ) Truncate input argument to the integral part.	✓	✓
float tgammaf ( float x ) Calculate the gamma function of the input argument.	✓	✓
float erfcinvf ( float y ) Calculate the inverse complementary function of the input argument.	✓	✓
float erfcxf ( float x ) Calculate the scaled complementary error function of the input argument.	✓	✓
float erfinvf ( float y ) Calculate the inverse error function of the input argument.	✓	✓
float fdividef ( float x, float y ) Divide two floating point values.	✓	✓
float frexpf ( float x, int *nptr ) Extract mantissa and exponent of a floating-point value.	✓	✓
float j0f ( float x ) Calculate the value of the Bessel function of the first kind of order 0 for the input argument.	✓	✓
float j1f ( float x ) Calculate the value of the Bessel function of the first kind of order 1 for the input argument.	✓	✓
float jnf ( int n, float x ) Calculate the value of the Bessel function of the first kind of order n for the input argument.	✓	✓
float lgammaf ( float x ) Calculate the natural logarithm of the absolute value of the gamma function of the input argument.	✓	✓
long long int llrintf ( float x ) Round input to nearest integer value.	✓	✓
long long int llroundf ( float x ) Round to nearest integer value.	✓	✓
long int lrintf ( float x ) Round input to nearest integer value.	✓	✓
long int lroundf ( float x ) Round to nearest integer value.	✓	✓
float modff ( float x, float *iptr ) Break down the input argument into fractional and integral parts.	✓	✓
float nextafterf ( float x, float y ) Returns next representable single-precision floating-point value after argument.	✓	✓
float norm3df ( float a, float b, float c ) Calculate the square root of the sum of squares of three coordinates of the argument.	✓	✓
float norm4df ( float a, float b, float c, float d ) Calculate the square root of the sum of squares of four coordinates of the argument.	✓	✓
loat normcdff ( float y ) Calculate the standard normal cumulative distribution function.	✓	✓
float normcdfinvf ( float y ) Calculate the inverse of the standard normal cumulative distribution function.	✓	✓
float normf ( int dim, const float *a ) Calculate the square root of the sum of squares of any number of coordinates.	✓	✓
float rcbrtf ( float x ) Calculate the reciprocal cube root function.	✓	✓
float remquof ( float x, float y, int *quo ) Compute single-precision floating-point remainder and part of quotient.	✓	✓
float rhypotf ( float x, float y ) Calculate one over the square root of the sum of squares of two arguments.	✓	✓
float rintf ( float x ) Round input to nearest integer value in floating-point.	✓	✓
float rnorm3df ( float a, float b, float c ) Calculate one over the square root of the sum of squares of three coordinates of the argument.	✓	✓
float rnorm4df ( float a, float b, float c, float d ) Calculate one over the square root of the sum of squares of four coordinates of the argument.	✓	✓
float rnormf ( int dim, const float *a ) Calculate the reciprocal of square root of the sum of squares of any number of coordinates.	✓	✓
float scalblnf ( float x, long int n ) Scale floating-point input by integer power of two.	✓	✓
void sincosf ( float x, float sptr, float cptr) Calculate the sine and cosine of the first input argument.	✓	✓
void sincospif ( float x, float sptr, float cptr) Calculate the sine and cosine of the first input argument multiplied by PI.	✓	✓
float y0f ( float x ) Calculate the value of the Bessel function of the second kind of order 0 for the input argument.	✓	✓
float y1f ( float x ) Calculate the value of the Bessel function of the second kind of order 1 for the input argument.	✓	✓
float ynf ( int n, float x ) Calculate the value of the Bessel function of the second kind of order n for the input argument.	✓	✓

Double precision mathematical functions

Following is the list of supported double precision mathematical functions.

Single precision mathematical functions

Function	Supported on Host	Supported on Device
double acos ( double x ) Calculate the arc cosine of the input argument.	✓	✓
double acosh ( double x ) Calculate the nonnegative arc hyperbolic cosine of the input argument.	✓	✓
double asin ( double x ) Calculate the arc sine of the input argument.	✓	✓
double asinh ( double x ) Calculate the arc hyperbolic sine of the input argument.	✓	✓
double atan ( double x ) Calculate the arc tangent of the input argument.	✓	✓
double atan2 ( double y, double x ) Calculate the arc tangent of the ratio of first and second input arguments.	✓	✓
double atanh ( double x ) Calculate the arc hyperbolic tangent of the input argument.	✓	✓
double cbrt ( double x ) Calculate the cube root of the input argument.	✓	✓
double ceil ( double x ) Calculate ceiling of the input argument.	✓	✓
double copysign ( double x, double y ) Create value with given magnitude, copying sign of second value.	✓	✓
double cos ( double x ) Calculate the cosine of the input argument.	✓	✓
double cosh ( double x ) Calculate the hyperbolic cosine of the input argument.	✓	✓
double erf ( double x ) Calculate the error function of the input argument.	✓	✓
double erfc ( double x ) Calculate the complementary error function of the input argument.	✓	✓
double exp ( double x ) Calculate the base e exponential of the input argument.	✓	✓
double exp10 ( double x ) Calculate the base 10 exponential of the input argument.	✓	✓
double exp2 ( double x ) Calculate the base 2 exponential of the input argument.	✓	✓
double expm1 ( double x ) Calculate the base e exponential of the input argument, minus 1.	✓	✓
double fabs ( double x ) Calculate the absolute value of the input argument.	✓	✓
double fdim ( double x, double y ) Compute the positive difference between x and y.	✓	✓
double floor ( double x ) Calculate the largest integer less than or equal to x.	✓	✓
double fma ( double x, double y, double z ) Compute x × y + z as a single operation.	✓	✓
double fmax ( double , double ) Determine the maximum numeric value of the arguments.	✓	✓
double fmin ( double x, double y ) Determine the minimum numeric value of the arguments.	✓	✓
double fmod ( double x, double y ) Calculate the floating-point remainder of x / y.	✓	✓
double frexp ( double x, int* nptr ) Extract mantissa and exponent of a floating-point value.	✓	✗
double hypot ( double x, double y ) Calculate the square root of the sum of squares of two arguments.	✓	✓
int ilogb ( double x ) Compute the unbiased integer exponent of the argument.	✓	✓
__RETURN_TYPE isfinite ( double a ) Determine whether argument is finite.	✓	✓
__RETURN_TYPE isinf ( double a ) Determine whether argument is infinite.	✓	✓
__RETURN_TYPE isnan ( double a ) Determine whether argument is a NaN.	✓	✓
double ldexp ( double x, int exp ) Calculate the value of x ⋅ 2 exp.	✓	✓
double log ( double x ) Calculate the base e logarithm of the input argument.	✓	✓
double log10 ( double x ) Calculate the base 10 logarithm of the input argument.	✓	✓
double log1p ( double x ) Calculate the value of logarithm of exp ( 1 + x ).	✓	✓
double log2 ( double x ) Calculate the base 2 logarithm of the input argument.	✓	✓
double logb ( double x ) Calculate the floating point representation of the exponent of the input argument.	✓	✓
double modf ( double x, double* iptr ) Break down the input argument into fractional and integral parts.	✓	✗
double nan ( const char* tagp) Returns `Not a Number` value.	✗	✓
double nearbyint ( double x ) Round the input argument to the nearest integer.	✓	✓
double pow ( double x, double y ) Calculate the value of first argument to the power of second argument.	✓	✓
double remainder ( double x, double y ) Compute double-precision floating-point remainder.	✓	✓
double remquo ( double x, double y, int* quo ) Compute double-precision floating-point remainder and part of quotient.	✓	✗
double round ( double x ) Round to nearest integer value in floating-point.	✓	✓
double scalbn ( double x, int n ) Scale floating-point input by integer power of two.	✓	✓
__RETURN_TYPE signbit ( double a ) Return the sign bit of the input.	✓	✓
double sin ( double x ) Calculate the sine of the input argument.	✓	✓
void sincos ( double x, double* sptr, double* cptr ) Calculate the sine and cosine of the first input argument.	✓	✗
double sinh ( double x ) Calculate the hyperbolic sine of the input argument.	✓	✓
double sqrt ( double x ) Calculate the square root of the input argument.	✓	✓
double tan ( double x ) Calculate the tangent of the input argument.	✓	✓
double tanh ( double x ) Calculate the hyperbolic tangent of the input argument.	✓	✓
double tgamma ( double x ) Calculate the gamma function of the input argument.	✓	✓
double trunc ( double x ) Truncate input argument to the integral part.	✓	✓
double erfcinv ( double y ) Calculate the inverse complementary function of the input argument.	✓	✓
double erfcx ( double x ) Calculate the scaled complementary error function of the input argument.	✓	✓
double erfinv ( double y ) Calculate the inverse error function of the input argument.	✓	✓
double frexp ( float x, int *nptr ) Extract mantissa and exponent of a floating-point value.	✓	✓
double j0 ( double x ) Calculate the value of the Bessel function of the first kind of order 0 for the input argument.	✓	✓
double j1 ( double x ) Calculate the value of the Bessel function of the first kind of order 1 for the input argument.	✓	✓
double jn ( int n, double x ) Calculate the value of the Bessel function of the first kind of order n for the input argument.	✓	✓
double lgamma ( double x ) Calculate the natural logarithm of the absolute value of the gamma function of the input argument.	✓	✓
long long int llrint ( double x ) Round input to nearest integer value.	✓	✓
long long int llround ( double x ) Round to nearest integer value.	✓	✓
long int lrint ( double x ) Round input to nearest integer value.	✓	✓
long int lround ( double x ) Round to nearest integer value.	✓	✓
double modf ( double x, double *iptr ) Break down the input argument into fractional and integral parts.	✓	✓
double nextafter ( double x, double y ) Returns next representable single-precision floating-point value after argument.	✓	✓
double norm3d ( double a, double b, double c ) Calculate the square root of the sum of squares of three coordinates of the argument.	✓	✓
float norm4d ( double a, double b, double c, double d ) Calculate the square root of the sum of squares of four coordinates of the argument.	✓	✓
double normcdf ( double y ) Calculate the standard normal cumulative distribution function.	✓	✓
double normcdfinv ( double y ) Calculate the inverse of the standard normal cumulative distribution function.	✓	✓
double rcbrt ( double x ) Calculate the reciprocal cube root function.	✓	✓
double remquo ( double x, double y, int *quo ) Compute single-precision floating-point remainder and part of quotient.	✓	✓
double rhypot ( double x, double y ) Calculate one over the square root of the sum of squares of two arguments.	✓	✓
double rint ( double x ) Round input to nearest integer value in floating-point.	✓	✓
double rnorm3d ( double a, double b, double c ) Calculate one over the square root of the sum of squares of three coordinates of the argument.	✓	✓
double rnorm4d ( double a, double b, double c, double d ) Calculate one over the square root of the sum of squares of four coordinates of the argument.	✓	✓
double rnorm ( int dim, const double *a ) Calculate the reciprocal of square root of the sum of squares of any number of coordinates.	✓	✓
double scalbln ( double x, long int n ) Scale floating-point input by integer power of two.	✓	✓
void sincos ( double x, double sptr, double cptr ) Calculate the sine and cosine of the first input argument.	✓	✓
void sincospi ( double x, double sptr, double cptr ) Calculate the sine and cosine of the first input argument multiplied by PI.	✓	✓
double y0f ( double x ) Calculate the value of the Bessel function of the second kind of order 0 for the input argument.	✓	✓
double y1 ( double x ) Calculate the value of the Bessel function of the second kind of order 1 for the input argument.	✓	✓
double yn ( int n, double x ) Calculate the value of the Bessel function of the second kind of order n for the input argument.	✓	✓

Integer intrinsics

Following is the list of supported integer intrinsics. Note that intrinsics are supported on device only.

Single precision mathematical functions

Function

double acos ( double x )
Calculate the arc cosine of the input argument.

unsigned int __brev ( unsigned int x )
Reverse the bit order of a 32 bit unsigned integer.

unsigned long long int __brevll ( unsigned long long int x )
Reverse the bit order of a 64 bit unsigned integer.

int __clz ( int x )
Return the number of consecutive high-order zero bits in a 32 bit integer.

unsigned int __clz(unsigned int x)
Return the number of consecutive high-order zero bits in 32 bit unsigned integer.

int __clzll ( long long int x )
Count the number of consecutive high-order zero bits in a 64 bit integer.

unsigned int __clzll(long long int x)
Return the number of consecutive high-order zero bits in 64 bit signed integer.

unsigned int __ffs(unsigned int x)
Find the position of least signigicant bit set to 1 in a 32 bit unsigned integer.

unsigned int __ffs(int x)
Find the position of least signigicant bit set to 1 in a 32 bit signed integer.

unsigned int __ffsll(unsigned long long int x)
Find the position of least signigicant bit set to 1 in a 64 bit unsigned integer.

unsigned int __ffsll(long long int x)
Find the position of least signigicant bit set to 1 in a 64 bit signed integer.

unsigned int __popc ( unsigned int x )
Count the number of bits that are set to 1 in a 32 bit integer.

unsigned int __popcll ( unsigned long long int x )
Count the number of bits that are set to 1 in a 64 bit integer.

int __mul24 ( int x, int y )
Multiply two 24bit integers.

unsigned int __umul24 ( unsigned int x, unsigned int y )
Multiply two 24bit unsigned integers.

The HIP-Clang implementation of __ffs() and __ffsll() contains code to add a constant +1 to produce the ffs result format. For the cases where this overhead is not acceptable and programmer is willing to specialize for the platform, HIP-Clang provides __lastbit_u32_u32(unsigned int input) and __lastbit_u32_u64(unsigned long long int input). The index returned by __lastbit_ instructions starts at -1, while for ffs the index starts at 0.

Floating-point Intrinsics

Following is the list of supported floating-point intrinsics. Note that intrinsics are supported on device only.

Single precision mathematical functions

Function

float __cosf ( float x )
Calculate the fast approximate cosine of the input argument.

float __expf ( float x )
Calculate the fast approximate base e exponential of the input argument.

float __frsqrt_rn ( float x )
Compute 1 / √x in round-to-nearest-even mode.

float __fsqrt_rn ( float x )
Compute √x in round-to-nearest-even mode.

float __log10f ( float x )
Calculate the fast approximate base 10 logarithm of the input argument.

float __log2f ( float x )
Calculate the fast approximate base 2 logarithm of the input argument.

float __logf ( float x )
Calculate the fast approximate base e logarithm of the input argument.

float __powf ( float x, float y )
Calculate the fast approximate of x<sup>y</sup>.

float __sinf ( float x )
Calculate the fast approximate sine of the input argument.

float __tanf ( float x )
Calculate the fast approximate tangent of the input argument.

double __dsqrt_rn ( double x )
Compute √x in round-to-nearest-even mode.

Texture functions

The supported texture functions are listed in texture_fetch_functions.h and texture_indirect_functions.h header files in the HIP-AMD backend repository.

Texture functions are not supported on some devices. To determine if texture functions are supported on your device, use Macro __HIP_NO_IMAGE_SUPPORT == 1. You can query the attribute hipDeviceAttributeImageSupport to check if texture functions are supported in the host runtime code.

Surface functions

Surface functions are not supported.

Timer functions

To read a high-resolution timer from the device, HIP provides the following built-in functions:

Returning the incremental counter value for every clock cycle on a device:
```
clock_t clock()
long long int clock64()
```
The difference between the values that are returned represents the cycles used.
Returning the wall clock count at a constant frequency on the device:
```
long long int wall_clock64()
```
This can be queried using the HIP API with the hipDeviceAttributeWallClockRate attribute of the device in HIP application code. For example:
```
int wallClkRate = 0; //in kilohertz
HIPCHECK(hipDeviceGetAttribute(&wallClkRate, hipDeviceAttributeWallClockRate, deviceId));
```
Where hipDeviceAttributeWallClockRate is a device attribute. Note that wall clock frequency is a per-device attribute.

Atomic functions

Atomic functions are run as read-modify-write (RMW) operations that reside in global or shared memory. No other device or thread can observe or modify the memory location during an atomic operation. If multiple instructions from different devices or threads target the same memory location, the instructions are serialized in an undefined order.

To support system scope atomic operations, you can use the HIP APIs that contain the _system suffix. For example:

atomicAnd: This function is atomic and coherent within the GPU device running the function
atomicAnd_system: This function extends the atomic operation from the GPU device to other CPUs and GPU devices in the system.

HIP supports the following atomic operations.

Atomic operations

Function	Supported in HIP	Supported in CUDA
int atomicAdd(int* address, int val)	✓	✓
int atomicAdd_system(int* address, int val)	✓	✓
unsigned int atomicAdd(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicAdd_system(unsigned int* address, unsigned int val)	✓	✓
unsigned long long atomicAdd(unsigned long long* address,unsigned long long val)	✓	✓
unsigned long long atomicAdd_system(unsigned long long* address, unsigned long long val)	✓	✓
float atomicAdd(float* address, float val)	✓	✓
float atomicAdd_system(float* address, float val)	✓	✓
double atomicAdd(double* address, double val)	✓	✓
double atomicAdd_system(double* address, double val)	✓	✓
float unsafeAtomicAdd(float* address, float val)	✓	✗
float safeAtomicAdd(float* address, float val)	✓	✗
double unsafeAtomicAdd(double* address, double val)	✓	✗
double safeAtomicAdd(double* address, double val)	✓	✗
int atomicSub(int* address, int val)	✓	✓
int atomicSub_system(int* address, int val)	✓	✓
unsigned int atomicSub(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicSub_system(unsigned int* address, unsigned int val)	✓	✓
int atomicExch(int* address, int val)	✓	✓
int atomicExch_system(int* address, int val)	✓	✓
unsigned int atomicExch(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicExch_system(unsigned int* address, unsigned int val)	✓	✓
unsigned long long atomicExch(unsigned long long int* address,unsigned long long int val)	✓	✓
unsigned long long atomicExch_system(unsigned long long* address, unsigned long long val)	✓	✓
unsigned long long atomicExch_system(unsigned long long* address, unsigned long long val)	✓	✓
float atomicExch(float* address, float val)	✓	✓
int atomicMin(int* address, int val)	✓	✓
int atomicMin_system(int* address, int val)	✓	✓
unsigned int atomicMin(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicMin_system(unsigned int* address, unsigned int val)	✓	✓
unsigned long long atomicMin(unsigned long long* address,unsigned long long val)	✓	✓
int atomicMax(int* address, int val)	✓	✓
int atomicMax_system(int* address, int val)	✓	✓
unsigned int atomicMax(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicMax_system(unsigned int* address, unsigned int val)	✓	✓
unsigned long long atomicMax(unsigned long long* address,unsigned long long val)	✓	✓
unsigned int atomicInc(unsigned int* address)	✗	✓
unsigned int atomicDec(unsigned int* address)	✗	✓
int atomicCAS(int* address, int compare, int val)	✓	✓
int atomicCAS_system(int* address, int compare, int val)	✓	✓
unsigned int atomicCAS(unsigned int* address,unsigned int compare,unsigned int val)	✓	✓
unsigned int atomicCAS_system(unsigned int* address, unsigned int compare, unsigned int val)	✓	✓
unsigned long long atomicCAS(unsigned long long* address,unsigned long long compare,unsigned long long val)	✓	✓
unsigned long long atomicCAS_system(unsigned long long* address, unsigned long long compare, unsigned long long val)	✓	✓
int atomicAnd(int* address, int val)	✓	✓
int atomicAnd_system(int* address, int val)	✓	✓
unsigned int atomicAnd(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicAnd_system(unsigned int* address, unsigned int val)	✓	✓
unsigned long long atomicAnd(unsigned long long* address,unsigned long long val)	✓	✓
unsigned long long atomicAnd_system(unsigned long long* address, unsigned long long val)	✓	✓
int atomicOr(int* address, int val)	✓	✓
int atomicOr_system(int* address, int val)	✓	✓
unsigned int atomicOr(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicOr_system(unsigned int* address, unsigned int val)	✓	✓
unsigned int atomicOr_system(unsigned int* address, unsigned int val)	✓	✓
unsigned long long atomicOr(unsigned long long int* address,unsigned long long val)	✓	✓
unsigned long long atomicOr_system(unsigned long long* address, unsigned long long val)	✓	✓
int atomicXor(int* address, int val)	✓	✓
int atomicXor_system(int* address, int val)	✓	✓
unsigned int atomicXor(unsigned int* address,unsigned int val)	✓	✓
unsigned int atomicXor_system(unsigned int* address, unsigned int val)	✓	✓
unsigned long long atomicXor(unsigned long long* address,unsigned long long val)	✓	✓
unsigned long long atomicXor_system(unsigned long long* address, unsigned long long val)	✓	✓

Unsafe floating-point atomic RMW operations

Some HIP devices support fast atomic RMW operations on floating-point values. For example, atomicAdd on single- or double-precision floating-point values may generate a hardware RMW instruction that is faster than emulating the atomic operation using an atomic compare-and-swap (CAS) loop.

On some devices, fast atomic RMW instructions can produce results that differ from the same functions implemented with atomic CAS loops. For example, some devices will use different rounding or denormal modes, and some devices produce incorrect answers if fast floating-point atomic RMW instructions target fine-grained memory allocations.

The HIP-Clang compiler offers a compile-time option, so you can choose fast--but potentially unsafe--atomic instructions for your code. On devices that support these instructions, you can include the -munsafe-fp-atomics option. This flag indicates to the compiler that all floating-point atomic function calls are allowed to use an unsafe version, if one exists. For example, on some devices, this flag indicates to the compiler that no floating-point atomicAdd function can target fine-grained memory.

If you want to avoid using unsafe use a floating-point atomic RMW operations, you can use the -mno-unsafe-fp-atomics option. Note that the compiler default is to not produce unsafe floating-point atomic RMW instructions, so the -mno-unsafe-fp-atomics option is not necessarily required. However, passing this option to the compiler is good practice.

When you pass -munsafe-fp-atomics or -mno-unsafe-fp-atomics to the compiler's command line, the option is applied globally for the entire compilation. Note that if some of the atomic RMW function calls cannot safely use the faster floating-point atomic RMW instructions, you must use -mno-unsafe-fp-atomics in order to ensure that your atomic RMW function calls produce correct results.

HIP has four extra functions that you can use to more precisely control which floating-point atomic RMW functions produce unsafe atomic RMW instructions:

float unsafeAtomicAdd(float* address, float val)
double unsafeAtomicAdd(double* address, double val) (Always produces fast atomic RMW instructions on devices that have them, even when -mno-unsafe-fp-atomics is used)
float safeAtomicAdd(float* address, float val)
double safeAtomicAdd(double* address, double val) (Always produces safe atomic RMW operations, even when -munsafe-fp-atomics is used)

Warp cross-lane functions

Threads in a warp are referred to as lanes and are numbered from 0 to warpSize - 1. Warp cross-lane functions operate across all lanes in a warp. The hardware guarantees that all warp lanes will execute in lockstep, so additional synchronization is unnecessary, and the instructions use no shared memory.

Note that NVIDIA and AMD devices have different warp sizes. You can use warpSize built-ins in you portable code to query the warp size.

Tip

Be sure to review HIP code generated from the CUDA path to ensure that it doesn't assume a waveSize of 32. "Wave-aware" code that assumes a waveSize of 32 can run on a wave-64 machine, but it only utilizes half of the machine's resources.

To get the default warp size of a GPU device, use hipGetDeviceProperties in you host functions.

cudaDeviceProp props;
cudaGetDeviceProperties(&props, deviceID);
int w = props.warpSize;
  // implement portable algorithm based on w (rather than assume 32 or 64)

Only use warpSize built-ins in device functions, and don't assume warpSize to be a compile-time constant.

Note that assembly kernels may be built for a warp size that is different from the default. All mask values either returned or accepted by these builtins are 64-bit unsigned integer values, even when compiled for a wave-32 device, where all the higher bits are unused. CUDA code ported to HIP requires changes to ensure that the correct type is used.

Note that the __sync variants are made available in ROCm 6.2, but disabled by default to help with the transition to 64-bit masks. They can be enabled by setting the preprocessor macro HIP_ENABLE_WARP_SYNC_BUILTINS. These builtins will be enabled unconditionally in ROCm 6.3. Wherever possible, the implementation includes a static assert to check that the program source uses the correct type for the mask.

Warp vote and ballot functions

int __all(int predicate)
int __any(int predicate)
unsigned long long __ballot(int predicate)
unsigned long long __activemask()

int __all_sync(unsigned long long mask, int predicate)
int __any_sync(unsigned long long mask, int predicate)
int __ballot(unsigned long long mask, int predicate)

You can use __any and __all to get a summary view of the predicates evaluated by the participating lanes.

__any(): Returns 1 if the predicate is non-zero for any participating lane, otherwise it returns 0.
__all(): Returns 1 if the predicate is non-zero for all participating lanes, otherwise it returns 0.

To determine if the target platform supports the any/all instruction, you can use the hasWarpVote device property or the HIP_ARCH_HAS_WARP_VOTE compiler definition.

__ballot returns a bit mask containing the 1-bit predicate value from each lane. The nth bit of the result contains the 1 bit contributed by the nth warp lane.

__activemask() returns a bit mask of currently active warp lanes. The nth bit of the result is 1 if the nth warp lane is active.

Note that the __ballot and __activemask builtins in HIP have a 64-bit return value (unlike the 32-bit value returned by the CUDA builtins). Code ported from CUDA should be adapted to support the larger warp sizes that the HIP version requires.

Applications can test whether the target platform supports the __ballot or __activemask instructions using the hasWarpBallot device property in host code or the HIP_ARCH_HAS_WARP_BALLOT macro defined by the compiler for device code.

The _sync variants require a 64-bit unsigned integer mask argument that specifies the lanes in the warp that will participate in cross-lane communication with the calling lane. Each participating thread must have its own bit set in its mask argument, and all active threads specified in any mask argument must execute the same call with the same mask, otherwise the result is undefined.

Warp match functions

unsigned long long __match_any(T value)
unsigned long long __match_all(T value, int *pred)

unsigned long long __match_any_sync(unsigned long long mask, T value)
unsigned long long __match_all_sync(unsigned long long mask, T value, int *pred)

T can be a 32-bit integer type, 64-bit integer type or a single precision or double precision floating point type.

__match_any returns a bit mask containing a 1-bit for every participating lane if and only if that lane has the same value in value as the current lane, and a 0-bit for all other lanes.

__match_all returns a bit mask containing a 1-bit for every participating lane if and only if they all have the same value in value as the current lane, and a 0-bit for all other lanes. The predicate pred is set to true if and only if all participating threads have the same value in value.

The _sync variants require a 64-bit unsigned integer mask argument that specifies the lanes in the warp that will participate in cross-lane communication with the calling lane. Each participating thread must have its own bit set in its mask argument, and all active threads specified in any mask argument must execute the same call with the same mask, otherwise the result is undefined.

Warp shuffle functions

The default width is warpSize (see warp-cross-lane). Half-float shuffles are not supported.

int   __shfl      (T var,   int srcLane, int width=warpSize);
int   __shfl_up   (T var,   unsigned int delta, int width=warpSize);
int   __shfl_down (T var,   unsigned int delta, int width=warpSize);
int   __shfl_xor  (T var,   int laneMask, int width=warpSize);

int   __shfl_sync      (unsigned long long mask, T var,   int srcLane, int width=warpSize);
int   __shfl_up_sync   (unsigned long long mask, T var,   unsigned int delta, int width=warpSize);
int   __shfl_down_sync (unsigned long long mask, T var,   unsigned int delta, int width=warpSize);
int   __shfl_xor_sync  (unsigned long long mask, T var,   int laneMask, int width=warpSize);

T can be a 32-bit integer type, 64-bit integer type or a single precision or double precision floating point type.

The _sync variants require a 64-bit unsigned integer mask argument that specifies the lanes in the warp that will participate in cross-lane communication with the calling lane. Each participating thread must have its own bit set in its mask argument, and all active threads specified in any mask argument must execute the same call with the same mask, otherwise the result is undefined.

Cooperative groups functions

You can use cooperative groups to synchronize groups of threads. Cooperative groups also provide a way of communicating between groups of threads at a granularity that is different from the block.

HIP supports the following kernel language cooperative groups types and functions:

Cooperative groups functions

Function	Supported in HIP	Supported in CUDA
void thread_group.sync();	✓	✓
unsigned thread_group.size();	✓	✓
unsigned thread_group.thread_rank()	✓	✓
bool thread_group.is_valid();	✓	✓
grid_group this_grid()	✓	✓
void grid_group.sync()	✓	✓
unsigned grid_group.size()	✓	✓
unsigned grid_group.thread_rank()	✓	✓
bool grid_group.is_valid()	✓	✓
multi_grid_group this_multi_grid()	✓	✓
void multi_grid_group.sync()	✓	✓
unsigned multi_grid_group.size()	✓	✓
unsigned multi_grid_group.thread_rank()	✓	✓
bool multi_grid_group.is_valid()	✓	✓
unsigned multi_grid_group.num_grids()	✓	✓
unsigned multi_grid_group.grid_rank()	✓	✓
thread_block this_thread_block()	✓	✓
multi_grid_group this_multi_grid()	✓	✓
void multi_grid_group.sync()	✓	✓
void thread_block.sync()	✓	✓
unsigned thread_block.size()	✓	✓
unsigned thread_block.thread_rank()	✓	✓
bool thread_block.is_valid()	✓	✓
dim3 thread_block.group_index()	✓	✓
dim3 thread_block.thread_index()	✓	✓

Warp matrix functions

Warp matrix functions allow a warp to cooperatively operate on small matrices that have elements spread over lanes in an unspecified manner.

HIP does not support kernel language warp matrix types or functions.

Warp matrix functions

Function	Supported in HIP	Supported in CUDA
void load_matrix_sync(fragment<...> &a, const T* mptr, unsigned lda)	✗	✓
void load_matrix_sync(fragment<...> &a, const T* mptr, unsigned lda, layout_t layout)	✗	✓
void store_matrix_sync(T* mptr, fragment<...> &a, unsigned lda, layout_t layout)	✗	✓
void fill_fragment(fragment<...> &a, const T &value)	✗	✓
void mma_sync(fragment<...> &d, const fragment<...> &a, const fragment<...> &b, const fragment<...> &c , bool sat)	✗	✓

Independent thread scheduling

Certain architectures that support CUDA allow threads to progress independently of each other. This independent thread scheduling makes intra-warp synchronization possible.

HIP does not support this type of scheduling.

Profiler Counter Function

The CUDA __prof_trigger() instruction is not supported.

Assert

The assert function is supported in HIP. Assert function is used for debugging purpose, when the input expression equals to zero, the execution will be stopped. .. code:: cpp

void assert(int input)

There are two kinds of implementations for assert functions depending on the use sceneries, - One is for the host version of assert, which is defined in assert.h, - Another is the device version of assert, which is implemented in hip/hip_runtime.h. Users need to include assert.h to use assert. For assert to work in both device and host functions, users need to include "hip/hip_runtime.h".

HIP provides the function abort() which can be used to terminate the application when terminal failures are detected. It is implemented using the __builtin_trap() function.

This function produces a similar effect of using asm("trap") in the CUDA code.

Note

In HIP, the function terminates the entire application, while in CUDA, asm("trap") only terminates the dispatch and the application continues to run.

Printf

Printf function is supported in HIP. The following is a simple example to print information in the kernel.

#include <hip/hip_runtime.h>

__global__ void run_printf() { printf("Hello World\n"); }

int main() {
  run_printf<<<dim3(1), dim3(1), 0, 0>>>();
}

Device-Side Dynamic Global Memory Allocation

Device-side dynamic global memory allocation is under development. HIP now includes a preliminary implementation of malloc and free that can be called from device functions.

__launch_bounds__

GPU multiprocessors have a fixed pool of resources (primarily registers and shared memory) which are shared by the actively running warps. Using more resources can increase IPC of the kernel but reduces the resources available for other warps and limits the number of warps that can be simulaneously running. Thus GPUs have a complex relationship between resource usage and performance.

__global__ void __launch_bounds__(MAX_THREADS_PER_BLOCK, MIN_WARPS_PER_EXECUTION_UNIT)
MyKernel(hipGridLaunch lp, ...)
...

- MAX_THREADS_PER_BLOCK - The programmers guarantees that kernel will be launched with threads less than MAX_THREADS_PER_BLOCK. (On NVCC this maps to the .maxntid PTX directive). If no launch_bounds is specified, MAX_THREADS_PER_BLOCK is the maximum block size supported by the device (typically 1024 or larger). Specifying MAX_THREADS_PER_BLOCK less than the maximum effectively allows the compiler to use more resources than a default unconstrained compilation that supports all possible block sizes at launch time. The threads-per-block is the product of (blockDim.x * blockDim.y * blockDim.z). - MIN_WARPS_PER_EXECUTION_UNIT - directs the compiler to minimize resource usage so that the requested number of warps can be simultaneously active on a multi-processor. Since active warps compete for the same fixed pool of resources, the compiler must reduce resources required by each warp(primarily registers). MIN_WARPS_PER_EXECUTION_UNIT is optional and defaults to 1 if not specified. Specifying a MIN_WARPS_PER_EXECUTION_UNIT greater than the default 1 effectively constrains the compiler's resource usage.

When launch kernel with HIP APIs, for example, hipModuleLaunchKernel(), HIP will do validation to make sure input kernel dimension size is not larger than specified launch_bounds. In case exceeded, HIP would return launch failure, if AMD_LOG_LEVEL is set with proper value (for details, please refer to docs/markdown/hip_logging.md), detail information will be shown in the error log message, including launch parameters of kernel dim size, launch bounds, and the name of the faulting kernel. It's helpful to figure out which is the faulting kernel, besides, the kernel dim size and launch bounds values will also assist in debugging such failures.

Compiler Impact

The compiler uses these parameters as follows: - The compiler uses the hints only to manage register usage, and does not automatically reduce shared memory or other resources. - Compilation fails if compiler cannot generate a kernel which meets the requirements of the specified launch bounds. - From MAX_THREADS_PER_BLOCK, the compiler derives the maximum number of warps/block that can be used at launch time. Values of MAX_THREADS_PER_BLOCK less than the default allows the compiler to use a larger pool of registers : each warp uses registers, and this hint constains the launch to a warps/block size which is less than maximum. - From MIN_WARPS_PER_EXECUTION_UNIT, the compiler derives a maximum number of registers that can be used by the kernel (to meet the required #simultaneous active blocks). If MIN_WARPS_PER_EXECUTION_UNIT is 1, then the kernel can use all registers supported by the multiprocessor. - The compiler ensures that the registers used in the kernel is less than both allowed maximums, typically by spilling registers (to shared or global memory), or by using more instructions. - The compiler may use hueristics to increase register usage, or may simply be able to avoid spilling. The MAX_THREADS_PER_BLOCK is particularly useful in this cases, since it allows the compiler to use more registers and avoid situations where the compiler constrains the register usage (potentially spilling) to meet the requirements of a large block size that is never used at launch time.

CU and EU Definitions

A compute unit (CU) is responsible for executing the waves of a work-group. It is composed of one or more execution units (EU) which are responsible for executing waves. An EU can have enough resources to maintain the state of more than one executing wave. This allows an EU to hide latency by switching between waves in a similar way to symmetric multithreading on a CPU. In order to allow the state for multiple waves to fit on an EU, the resources used by a single wave have to be limited. Limiting such resources can allow greater latency hiding, but can result in having to spill some register state to memory. This attribute allows an advanced developer to tune the number of waves that are capable of fitting within the resources of an EU. It can be used to ensure at least a certain number will fit to help hide latency, and can also be used to ensure no more than a certain number will fit to limit cache thrashing.

Porting from CUDA __launch_bounds

CUDA defines a __launch_bounds which is also designed to control occupancy:

__launch_bounds(MAX_THREADS_PER_BLOCK, MIN_BLOCKS_PER_MULTIPROCESSOR)

The second parameter __launch_bounds parameters must be converted to the format used __hip_launch_bounds, which uses warps and execution-units rather than blocks and multi-processors (this conversion is performed automatically by HIPIFY tools).

MIN_WARPS_PER_EXECUTION_UNIT = (MIN_BLOCKS_PER_MULTIPROCESSOR * MAX_THREADS_PER_BLOCK) / 32

The key differences in the interface are: - Warps (rather than blocks): The developer is trying to tell the compiler to control resource utilization to guarantee some amount of active Warps/EU for latency hiding. Specifying active warps in terms of blocks appears to hide the micro-architectural details of the warp size, but makes the interface more confusing since the developer ultimately needs to compute the number of warps to obtain the desired level of control. - Execution Units (rather than multiProcessor): The use of execution units rather than multiprocessors provides support for architectures with multiple execution units/multi-processor. For example, the AMD GCN architecture has 4 execution units per multiProcessor. The hipDeviceProps has a field executionUnitsPerMultiprocessor. Platform-specific coding techniques such as #ifdef can be used to specify different launch_bounds for NVCC and HIP-Clang platforms, if desired.

maxregcount

Unlike nvcc, HIP-Clang does not support the "--maxregcount" option. Instead, users are encouraged to use the hip_launch_bounds directive since the parameters are more intuitive and portable than micro-architecture details like registers, and also the directive allows per-kernel control rather than an entire file. hip_launch_bounds works on both HIP-Clang and nvcc targets.

Asynchronous Functions

Memory stream

Stream

StreamO

Peer to peer

PeerToPeer

Memory management

Memory

External Resource Interoperability

External

Register Keyword

The register keyword is deprecated in C++, and is silently ignored by both nvcc and HIP-Clang. You can pass the option -Wdeprecated-register the compiler warning message.

Pragma Unroll

Unroll with a bounds that is known at compile-time is supported. For example:

#pragma unroll 16 /* hint to compiler to unroll next loop by 16 */
for (int i=0; i<16; i++) ...

#pragma unroll 1  /* tell compiler to never unroll the loop */
for (int i=0; i<16; i++) ...

#pragma unroll /* hint to compiler to completely unroll next loop. */
for (int i=0; i<16; i++) ...

In-Line Assembly

GCN ISA In-line assembly, is supported. For example:

asm volatile ("v_mac_f32_e32 %0, %2, %3" : "=v" (out[i]) : "0"(out[i]), "v" (a), "v" (in[i]));

We insert the GCN isa into the kernel using asm() Assembler statement. volatile keyword is used so that the optimizers must not change the number of volatile operations or change their order of execution relative to other volatile operations. v_mac_f32_e32 is the GCN instruction, for more information please refer - [AMD GCN3 ISA architecture manual](http://gpuopen.com/compute-product/amd-gcn3-isa-architecture-manual/) Index for the respective operand in the ordered fashion is provided by % followed by position in the list of operands "v" is the constraint code (for target-specific AMDGPU) for 32-bit VGPR register, for more info please refer - [Supported Constraint Code List for AMDGPU](https://llvm.org/docs/LangRef.html#supported-constraint-code-list) Output Constraints are specified by an "=" prefix as shown above ("=v"). This indicate that assemby will write to this operand, and the operand will then be made available as a return value of the asm expression. Input constraints do not have a prefix - just the constraint code. The constraint string of "0" says to use the assigned register for output as an input as well (it being the 0'th constraint).

## C++ Support The following C++ features are not supported: - Run-time-type information (RTTI) - Try/catch - Virtual functions Virtual functions are not supported if objects containing virtual function tables are passed between GPU's of different offload arch's, e.g. between gfx906 and gfx1030. Otherwise virtual functions are supported.

Kernel Compilation

hipcc now supports compiling C++/HIP kernels to binary code objects. The file format for binary is .co which means Code Object. The following command builds the code object using hipcc.

hipcc --genco --offload-arch=[TARGET GPU] [INPUT FILE] -o [OUTPUT FILE]

[TARGET GPU] = GPU architecture
[INPUT FILE] = Name of the file containing kernels
[OUTPUT FILE] = Name of the generated code object file

Note

When using binary code objects is that the number of arguments to the kernel is different on HIP-Clang and NVCC path. Refer to the HIP module_api sample for differences in the arguments to be passed to the kernel.

gfx-arch-specific-kernel

Clang defined '__gfx*__' macros can be used to execute gfx arch specific codes inside the kernel. Refer to the sample in HIP 14_gpu_arch sample.

Files

kernel_language.rst

Latest commit

History

kernel_language.rst

File metadata and controls

C++ Language Extensions

Function-type qualifiers

__device__

__global__

__host__

Calling __global__ functions

Kernel launch example

Variable type qualifiers

__constant__

__shared__

__managed__

__restrict__

Built-in variables

Coordinate built-ins

warpSize

Vector types

Short vector types

dim3

Memory fence instructions

Synchronization functions

Math functions

Single precision mathematical functions

Double precision mathematical functions

Integer intrinsics

Floating-point Intrinsics

Texture functions

Surface functions

Timer functions

Atomic functions

Unsafe floating-point atomic RMW operations

Warp cross-lane functions

Warp vote and ballot functions

Warp match functions

Warp shuffle functions

Cooperative groups functions

Warp matrix functions

Independent thread scheduling

Profiler Counter Function

Assert

Printf

Device-Side Dynamic Global Memory Allocation

__launch_bounds__

Compiler Impact

CU and EU Definitions

Porting from CUDA __launch_bounds

maxregcount

Asynchronous Functions

Memory stream

Peer to peer

Memory management

External Resource Interoperability

Register Keyword

Pragma Unroll

In-Line Assembly

Kernel Compilation

gfx-arch-specific-kernel

`device`

`global`

`host`

Calling `global` functions

`constant`

`shared`

`managed`

`restrict`