CUDA syntax

Declaring functions

__global__	declares kernel, which is called on host and executed on device
__device__	declares device function, which is called and executed on device
__host__	declares host function, which is called and executed on host
__noinline__	to avoid inlining
__forceinline__	to force inlining

Declaring variables

__device__	declares device variable in global memory, accessible from all threads, with lifetime of application
__constant__	declares device variable in constant memory, accessible from all threads, with lifetime of application
__shared__	declares device varibale in block's shared memory, accessible from all threads within a block, with lifetime of block
__restrict__	standard C definition that pointers are not aliased

Types

Most routines return an error code of type cudaError_t.

Vector types

char1, uchar1, short1, ushort1, int1, uint1, long1, ulong1, float1
char2, uchar2, short2, ushort2, int2, uint2, long2, ulong2, float2
char3, uchar3, short3, ushort3, int3, uint3, long3, ulong3, float3
char4, uchar4, short4, ushort4, int4, uint4, long4, ulong4, float4

longlong1, ulonglong1, double1
longlong2, ulonglong2, double2

dim3

Components are accessible as variable.x,  variable.y,  variable.z,  variable.w.
Constructor is make_<type>( x, ... ), for example:

float2 xx = make_float2( 1., 2. );

dim3 can take 1, 2, or 3 argumetns:

dim3 blocks1D( 5       );
dim3 blocks2D( 5, 5    );
dim3 blocks3D( 5, 5, 5 );

Pre-defined variables

dim3 gridDim	dimensions of grid
dim3 blockDim	dimensions of block
uint3 blockIdx	block index within grid
uint3 threadIdx	thread index within block
int warpSize	number of threads in warp

Kernel invocation

__global__ void kernel( ... ) { ... }

dim3 blocks( nx, ny, nz );           // cuda 1.x has 1D and 2D grids, cuda 2.x adds 3D grids
dim3 threadsPerBlock( mx, my, mz );  // cuda 1.x has 1D, 2D, and 3D blocks

kernel<<< blocks, threadsPerBlock >>>( ... );

Thread management

__threadfence_block();	wait until memory accesses are visible to block
__threadfence();	wait until memory accesses are visible to block and device
__threadfence_system();	wait until memory accesses are visible to block and device and host (2.x)
__syncthreads();	wait until all threads reach sync

Memory management

__device__ float* pointer;
cudaMalloc( &pointer, size );
cudaFree( pointer );

// direction is one of cudaMemcpyHostToDevice or cudaMemcpyDeviceToHost
cudaMemcpy( dst_pointer, src_pointer, size, direction );

__constant__ float dev_data[n];
float host_data[n];
cudaMemcpyToSymbol  ( dev_data,  host_data, sizeof(host_data) );  // dev_data  = host_data
cudaMemcpyFromSymbol( host_data, dev_data,  sizeof(host_data) );  // host_data = dev_data

Also, malloc and free work inside a kernel (2.x), but memory allocated in a kernel must be deallocated in a kernel (not the host). It can be freed in a different kernel, though.

Atomic functions

old = atomicAdd ( &addr, value );  // old = *addr;  *addr += value
old = atomicSub ( &addr, value );  // old = *addr;  *addr –= value
old = atomicExch( &addr, value );  // old = *addr;  *addr  = value

old = atomicMin ( &addr, value );  // old = *addr;  *addr = min( old, value )
old = atomicMax ( &addr, value );  // old = *addr;  *addr = max( old, value )

// increment up to value, then reset to 0  
// decrement down to 0, then reset to value
old = atomicInc ( &addr, value );  // old = *addr;  *addr = ((old >= value) ? 0 : old+1 )
old = atomicDec ( &addr, value );  // old = *addr;  *addr = ((old == 0) or (old > val) ? val : old–1 )

old = atomicAnd ( &addr, value );  // old = *addr;  *addr &= value
old = atomicOr  ( &addr, value );  // old = *addr;  *addr |= value
old = atomicXor ( &addr, value );  // old = *addr;  *addr ^= value

// compare-and-store
old = atomicCAS ( &addr, compare, value );  // old = *addr;  *addr = ((old == compare) ? value : old)

Warp vote

int __all   ( predicate );
int __any   ( predicate );
int __ballot( predicate );  // nth thread sets nth bit to predicate

Timer

wall clock cycle counter

clock_t clock();

Texture

can also return float2 or float4, depending on texRef.

// integer index
float tex1Dfetch( texRef, ix );

// float index
float tex1D( texRef, x       );
float tex2D( texRef, x, y    );
float tex3D( texRef, x, y, z );

float tex1DLayered( texRef, x    );
float tex2DLayered( texRef, x, y );

---

Low-level Driver API

#include <cuda.h>

CUdevice dev;
CUdevprop properties;
char name[n];
int major, minor;
size_t bytes;

cuInit( 0 );  // takes flags for future use
cuDeviceGetCount         ( &cnt );
cuDeviceGet              ( &dev, index );
cuDeviceGetName          ( name, sizeof(name), dev );
cuDeviceComputeCapability( &major, &minor,     dev );
cuDeviceTotalMem         ( &bytes,             dev );
cuDeviceGetProperties    ( &properties,        dev );  // max threads, etc.

---

cuBLAS

Matrices are column-major. Indices are 1-based; this affects result of i<t>amax and i<t>amin.

#include <cublas_v2.h>

cublasHandle_t handle;
cudaStream_t   stream;

cublasCreate( &handle );
cublasDestroy( handle );
cublasGetVersion( handle, &version );
cublasSetStream( handle,  stream );
cublasGetStream( handle, &stream );
cublasSetPointerMode( handle,  mode );
cublasGetPointerMode( handle, &mode );

// copy x => y
cublasSetVector     ( n, elemSize, x_src_host, incx, y_dst_dev,  incy );
cublasGetVector     ( n, elemSize, x_src_dev,  incx, y_dst_host, incy );
cublasSetVectorAsync( n, elemSize, x_src_host, incx, y_dst_dev,  incy, stream );
cublasGetVectorAsync( n, elemSize, x_src_dev,  incx, y_dst_host, incy, stream );

// copy A => B
cublasSetMatrix     ( rows, cols, elemSize, A_src_host, lda, B_dst_dev,  ldb );
cublasGetMatrix     ( rows, cols, elemSize, A_src_dev,  lda, B_dst_host, ldb );
cublasSetMatrixAsync( rows, cols, elemSize, A_src_host, lda, B_dst_dev,  ldb, stream );
cublasGetMatrixAsync( rows, cols, elemSize, A_src_dev,  lda, B_dst_host, ldb, stream );

Constants

argument	constants	description (Fortran letter)
trans	CUBLAS_OP_N	non-transposed ('N')
	CUBLAS_OP_T	transposed ('T')
	CUBLAS_OP_C	conjugate transposed ('C')
uplo	CUBLAS_FILL_MODE_LOWER	lower part filled ('L')
	CUBLAS_FILL_MODE_UPPER	upper part filled ('U')
side	CUBLAS_SIDE_LEFT	matrix on left ('L')
	CUBLAS_SIDE_RIGHT	matrix on right ('R')
mode	CUBLAS_POINTER_MODE_HOST	alpha and beta scalars passed on host
	CUBLAS_POINTER_MODE_DEVICE	alpha and beta scalars passed on device

• All functions add handle as first argument.
• All functions return cublasStatus_t error code.
• Constants alpha and beta are passed by pointer. All other scalars (n, incx, etc.) are bassed by value.
• Functions that return a value, such as ddot, add result as last argument, and save value to result.
• Constants are given in table above, instead of using characters.

cublasDdot ( handle, n, x, incx, y, incy, &result );  // result = ddot( n, x, incx, y, incy );
cublasDaxpy( handle, n, &alpha, x, incx, y, incy );   // daxpy( n, alpha, x, incx, y, incy );

---

Compiler

Flags common with cc

Short flag	Long flag	Output or Description
-c	--compile	.o object file
-E	--preprocess	on standard output
-M	--generate-dependencies	on standard output
-o file	--output-file file
-I directory	--include-path directory	header search path
-L directory	--library-path directory	library search path
-l lib	--library lib	link with library
-lib		generate library
-shared		generate shared library
-pg	--profile	for gprof
-g level	--debug level
-G	--device-debug
-O level	--optimize level
Undocumented (but in sample makefiles)
-m64		compile x86_64 host CPU code

Flags specific to nvcc

-v	list compilation commands as they are executed
-dryrun	list compilation commands, without executing
-keep	saves intermediate files (e.g., pre-processed) for debugging
-clean	removes output files (with same exact compiler options)
-arch=<compute_xy>	generate PTX for capability x.y
-code=<sm_xy>	generate binary for capability x.y, by default same as -arch
-gencode arch=...,code=...	same as -arch and -code, but may be repeated

Argumenents for -arch and -code

	Virtual architecture	Real architecture	Features
Tesla	compute_10	sm_10	Basic features
	compute_11	sm_11	+ atomic memory ops on global memory
	compute_12	sm_12	+ atomic memory ops on shared memory + vote instructions
	compute_13	sm_13	+ double precision
Fermi	compute_20	sm_20	+ Fermi

---

Some hardware constraints

	1.x	2.x
max x- or y-dimension of block	512	1024
max z-dimension of block	64	64
max threads per block	512	1024
warp size	32	32
max blocks per MP	8	8
max warps per MP	32	48
max threads per MP	1024	1536
max 32-bit registers per MP	16k	32k
max shared memory per MP	16 KB	48 KB
shared memory banks	16	32
local memory per thread	16 KB	512 KB
const memory	64 KB	64 KB
const cache	8 KB	8 KB
texture cache	8 KB	8 KB