When things are bit more complicated than this, then GPU implementations are easily outperformed the CPU counter parts. Since it was very straightforward calculation, so modern day optimized CPUs are able to perform these calculations very fast. This shows how much of memory overload happen in a program. Most of the time in GPU implementation is consumed by the memory transferring operations between host and device. Printf ( "GPU kernel execution time multiplication time : %4.6f \n ", ( double )(( double )( gpu_end - gpu_start ) / CLOCKS_PER_SEC )) printf ( "Mem transfer host to device : %4.6f \n ", ( double )(( double )( mem_htod_end - mem_htod_start ) / CLOCKS_PER_SEC )) printf ( "Mem transfer device to host : %4.6f \n ", ( double )(( double )( mem_dtoh_end - mem_dtoh_start ) / CLOCKS_PER_SEC )) printf ( "Total GPU time : %4.6f \n ", ( double )(( double )(( mem_htod_end - mem_htod_start ) + ( gpu_end - gpu_start ) + ( mem_dtoh_end - mem_dtoh_start )) / CLOCKS_PER_SEC )) ĬPU implementation execution time is less than the total GPU execution time but if we look the kernel execution time, it is lower than the CPU execution time. Most of our program will have execution time in millisecond or micro second range Then we can divide that value by clock cycles per second value and get the number of seconds elapsed during the operation. We will note the CPU clock cycle before and after the operation (function calls), then the difference between those two will give us the elapsed clock cycles between the operations. In CUDA program, we usually wants to compare the performance between GPU implementation with CPU implementation and also in case of we have multiple solutions to solve same problem then we want to find out the best performing or fastest solution as well. Specifically, it brings us to the variable blockIdx.//compare_vectors printf ( "After Multiplication Validity Chaeck : \n " ) for ( int i = 0 i < size i ++ ) printf ( "Resultant vectors of CPU and GPU are same \n " )
CUDA VECTOR ADD DIM3 CODE
Parallel programming has never been easier.īut this raises an excellent question: The GPU runs N copies of our kernel code, but how can we tell from within the code which block is currently running? This question brings us to the second new feature of the example, the kernel code itself.
With kernel> (), you would get 256 blocks running on the GPU. We call each of these parallel invocations a block. In this case, we're passing the value N for this parameter.įor example, if we launch with kernel>(), you can think of the runtime creating two copies of the kernel and running them in parallel. Well, the first number in those parameters represents the number of parallel blocks in which we would like the device to execute our kernel.
CUDA VECTOR ADD DIM3 HOW TO
Recall that we left those two numbers in the angle brackets unexplained we stated vaguely that they were parameters to the runtime that describe how to launch the kernel. ) īut in this example we are launching with a number in the angle brackets that is not 1: add>( dev_a, dev_b, dev_c ) display the results for ( int i=0 i>( param1, param2. HANDLE_ERROR( cudaMemcpy( c, dev_c, N * sizeof( int), copy the array 'c' back from the GPU to the CPU Int tid = 0 // this is CPU zero, so we start at zero while (tid >( dev_a, dev_b, dev_c )
If you have any background in linear algebra, you will recognize this operation as summing two vectors.įigure 4.1 Summing two vectors CPU Vector Sumsįirst we'll look at one way this addition can be accomplished with traditional C code: #include"./common/book.h" #define N 10 Imagine having two lists of numbers where we want to sum corresponding elements of each list and store the result in a third list. We will contrive a simple example to illustrate threads and how we use them to code with CUDA C. In this chapter, we see how straightforward it is to launch a device kernel that performs its computations in parallel. However, thus far we have only ever launched a kernel that runs serially on the GPU. Although this was extremely simple, it was also extremely inefficient because NVIDIA's hardware engineering minions have optimized their graphics processors to perform hundreds of computations in parallel. By adding the _global_ qualifier to the function and by calling it using a special angle bracket syntax, we executed the function on our GPU. Previously, we saw how easy it was to get a standard C function to start running on a device. Learn More Buy 4.2 CUDA Parallel Programming CUDA by Example: An Introduction to General-Purpose GPU Programming
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