There is no way to compute the maximum of a set of values without looking at every value in the list. In this sense, there is no faster sequential algorithm than a for loop over the elements.I know I could do a for loop, but for an array with 100,000+ elements (smallest cases) it takes a lot of time (plus it has to continue through the loop even once its found what will eventually be the max value).
AIR&SPACE said:I was wondering if anyone knows a fast method of finding the maximum value in a Cuda C array. I can't get max_element to work, but not sure its very fast anyways. I know I could do a for loop, but for an array with 100,000+ elements (smallest cases) it takes a lot of time (plus it has to continue through the loop even once its found what will eventually be the max value).
Anyone aware of any tricks?
aegrisomnia said:Sorry if this answer is way too elementary...
Finding the maximum value in an array is a classic reduction problem; it's the same as finding the sum, average, etc., just using a different "binary operator" (one which returns the maximum of the two arguments). That being said, given a fast (or the fastest) way to compute the sum of an array of arguments in CUDA, the same algorithm should be a fast (or the fastest) way to compute the maximum value in the array.
So I'm seeing this as a multi-phase solution. In the first phase, each block should compute the maximum value of the array corresponding to that block. In the second phase, some subset of the blocks should compute the maximum values from the computed maxima of all the blocks that did work in the first phase. Repeat until only one block is considering all the maximal values, and the result of this is guaranteed to be the maximum array value. You can consider things like shared memory, data distribution, etc. to increase performance.
Example: A = {3, 10, 1, 9, 2, 8, 3, 4, 8, 2, 1, 7, 2, 5, 6, 1, 2, 5, 3, 2}
Using 5 blocks to handle 4 elements each:
Phase 1:
{3, 10, 1, 9} {2, 8, 3, 4} {8, 2, 1, 7} {2, 5, 6, 1} {2, 5, 3, 2}
{10, 10, 9, 9} {8, 8, 4, 4} {8, 2, 7, 7} {5, 5, 6, 1} {5, 5, 3, 2}
{10, 10, 9, 9} {8, 8, 4, 4} {8, 2, 7, 7} {6, 5, 6, 1} {5, 5, 3, 2}
Phase 2:
{10, 8, 8, 6} {5}
{10, 8, 8, 6} {5}
{10, 8, 8, 6} {5}
Phase 3:
{10, 5}
{10, 5}
Maximum is 10
An array in CUDA C is a collection of data elements that are stored in a contiguous block of memory. It is a fundamental data structure used for storing and accessing data in parallel computations on a GPU.
To find the maximum element in an array using CUDA C, you can use the built-in function "max" provided by the CUDA C library. This function takes in two parameters - the first one being the current maximum value and the second one being the value to be compared. By looping through all the elements in the array and repeatedly using the "max" function, you can find the maximum element in the array.
The main advantage of using CUDA C for finding the max element in an array is its ability to perform parallel computations on a GPU. This allows for faster execution of the code compared to using traditional CPU-based programming. Additionally, CUDA C also provides built-in functions and data types specifically designed for GPU programming, making it easier and more efficient to work with large arrays of data.
Yes, it is possible to find the max element in an array using a single thread in CUDA C. However, this would not take advantage of the parallel computing capabilities of a GPU and would be less efficient compared to using multiple threads. It is recommended to use multiple threads to fully utilize the power of CUDA C and achieve faster execution times.
To optimize the code for finding the max element in an array using CUDA C, you can use techniques such as shared memory, which allows for faster access to data by multiple threads. Additionally, you can also consider using CUDA streams to overlap data transfers and computations, reducing the overall execution time. Profiling tools provided by CUDA C can also help identify any bottlenecks in the code and suggest optimizations for better performance.