It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. This sample implements matrix multiplication and is exactly the same as Chapter 6 of the programming guide. fp16ScalarProductĬalculates scalar product of two vectors of FP16 numbers. This sample demonstrates how to use OpenMP API to write an application for multiple GPUs.
This sample demonstrates how to use C++ function overloading on the GPU. It also demonstrates that vector types can be used from cpp. the CUDA entry point on host side is only a function which is called from C++ code and only the file containing this function is compiled with nvcc. This example demonstrates how to integrate CUDA into an existing C++ application, i.e. It also illustrates how to introduce dependencies between CUDA streams with the new cudaStreamWaitEvent function. This sample demonstrates the use of CUDA streams for concurrent execution of several kernels on GPU device. This example shows how to use the clock function using libNVRTC to measure the performance of block of threads of a kernel accurately. This example shows how to use the clock function to measure the performance of block of threads of a kernel accurately.
It scans a input text file and prints no. This sample demonstrates C++11 feature support in CUDA. CPU can query CUDA events to determine whether GPU has completed tasks. Since CUDA stream calls are asynchronous, the CPU can perform computations while GPU is executing (including DMA memcopies between the host and device). Events are inserted into a stream of CUDA calls.
This sample illustrates the usage of CUDA events for both GPU timing and overlapping CPU and GPU execution.
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