nvrtcResult nvrtcCompileProgram ( nvrtcProgram prog, int numOptions, const char** options ) nvrtcCompileProgram compiles the given program. Functions nvrtcResult nvrtcAddNameExpression ( nvrtcProgram prog, const char* name_expression ) nvrtcAddNameExpression notes the given name expression denoting the address of a _global_ function or _device_/_constant_ Typedefs typedef _nvrtcProgram * nvrtcProgram nvrtcProgram is the unit of compilation, and an opaque handle for a program. NVRTC defines the following type and functions for actual compilation. įrom the figure, the 13th block maps to the coordinates and the 17th thread maps to the coordinates. With respect to 0-indexing, the 17th thread of the 13th block is thread. This number has to be expressed in terms of the block size. Here are the steps to find the indices for a particular thread, say thread. Each block has threads for a total of threads. Randomly completed threads and blocks are shown as green to highlight the fact that the order of execution for threads is undefined. , and below the 3D index is the 1D index of each block, e.g.Īt the block level (on the right), a similar indexing scheme applies, where the tuple is the 3D index of the thread within the block and the number in the square bracket is the 1D index of the thread within the block.ĭuring execution, the CUDA threads are mapped to the problem in an undefined manner. The grid (on the left) has size, that is, it has blocks in the direction, blocks in the direction, and block in the direction.Įach block (on the right) is of size with threads along the and directions, and thread along the direction.Īt the grid level (on the left), the tuple for each block is the 3D index, e.g. Here is an example indexing scheme based on the mapping defined above. Thread index within the block (zero-based)Įach of the above are dim3 structures and can be read in the kernel to assign particular workloads to any thread. MappingĮvery thread in CUDA is associated with a particular index so that it can calculate and access memory locations in an array. One can initialise as many of the three coordinates as they like dim3 threads(256) // Initialise with x as 256, y and z will both be 1ĭim3 blocks(100, 100) // Initialise x and y, z will be 1ĭim3 anotherOne(10, 54, 32) // Initialises all three values, x will be 10, y gets 54 and z will be 32.

Blocks can be organized into one- or two-dimensional grids (say up to 65,535 blocks) in each dimension.ĭim3 is a 3d structure or vector type with three integers,, and. The limitation on the number of threads in a block is actually imposed because the number of registers that can be allocated across all threads is limited. For example if the maximum, and dimensions of a block are 512, 512 and 64, it should be allocated such that 512, which is the maximum number of threads per block. When a kernel is launched the number of threads per thread block, and the number of thread blocks is specified, this, in turn, defines the total number of CUDA threads launched. The blocks in a grid must be able to be executed independently, as communication or cooperation between blocks in a grid is not possible. DimensionsĪs many parallel applications involve multidimensional data, it is convenient to organize thread blocks into 1D, 2D or 3D arrays of threads. In the chevrons we place the number of blocks and the number of threads per block.ġ00, 256 would launch 100 blocks of 256 threads each (total of 25600 threads).ĥ0, 1024 would launch 50 blocks of 1024 threads each (51200 threads in total). The host calls a kernel using a triple chevron.

Cuda dim3 example