Divide and conquer algorithm10/3/2023 Here are some of the easiest questions you might get asked in a coding interview. Don’t get stuck in a loop of reading as many solutions as possible! We’ve analysed dozens of questions and selected ones that are commonly asked and have clear and high quality answers. If you get stuck, go ahead and look at the solution, but then try the next one alone again. Begin by writing your own solution without external resources in a fixed amount of time. Instead, you should use these questions to practice the fundamental concepts of divide and conquer.Īs you consider each question, try to replicate the conditions you’ll encounter in your interview. Interviewers will always try to find new questions, or ones that are not available online. You might be tempted to try to read all of the possible questions and memorize the solutions, but this is not feasible. Easy divide and conquer interview questions Click here to practice coding interviews with ex-FAANG interviewers 1. Hard divide and conquer interview questions.Medium divide and conquer interview questions. Easy divide and conquer interview questions.Given an integer array nums, return the number of reverse pairs in the array. A reverse pair is a pair (i, j) where 0 2 * numsīelow, we take a look at 50 divide and conquer questions and provide you with links to high quality solutions to them. Given an integer array nums and an integer k, return the kth largest element in the array. Given the head of a linked list, return the list after sorting it in ascending order. Write an efficient algorithm that searches for a target value in an m x n integer matrix. Merge all the linked-lists into one sorted linked-list and return it. You are given an array of k linked-lists lists, each linked-list is sorted in ascending order. I'd say just go with Intel TBB it is mostly cross-platform and provides various high-level parallel algorithms like parallel sorting.5 typical divide and conquer interview questions Write you own working-steal scheduler on top the standard thread primitives that comes with C++11, it is doable but not so simple to implement correctly. Try using std::async, some implementations like VC++ will use a work-stealing scheduler underneath but there are no guarantees and the C++ standard does not enforce this.ī. This is still a much better situation than using threads/thread-pools because you are not dividing up based on the optimal thread-count anymore.Īnyways there is nothing like this standardized in C++11, if you want a pure standard library solution without adding third-party dependencies the best you can do is either:Ī. This is a much better situation for nested/divide and conquer/fork-join parallel algorithms.įor (nested) data-parallel algorithms it best to avoid spawning a task per element because typically an operation on a single element the granularity of work is far too small to gain any benefits and outweighed by the overhead of scheduler management so on top of the lower-level work-stealing scheduler you have a higher-level management that deals with dividing up a container into chunks. Typically the number of threads is equal to the number of hardware threads available on the system, so it does not matter so much if you spawn/queue hundreds/thousands tasks (well it does in some cases but depends on the context). Instead of spawning threads or re-using threads from a pool what happens is a "task" (typically a closure + some bookkeeping data) is put onto work-stealing queue(s) to be run at some point by one of X number of worker threads. Nowadays almost all modern parallel frameworks are based on top of a task based work-stealing scheduler, such examples are Intel TBB, Microsoft concurrency run-time (concert)/PPL. Thread-pools can help with the latter but not the former without writing extra code. Using threads directly for writing parallel algorithms, especially divide-and-conquer type algorithms is a bad idea, you will have poor scaling, poor load-balancing and as you know the cost of thread-creation is expensive. This thread limit can be process wide so parallel calls to quicksort will back off co-operatively from creating too many threads. Quicksort(pivot + 1, end, depth+1) // <- HEREĪlternatively to using depth, you can set a global thread limit, and then only create a new thread if the limit hasn't been reached - if it has, than do it sequentially. Void quicksort(IteratorType begin, IteratorType end)Ĭonst IteratorType pivot = partition(begin, end) I wrote a basic implementation of quicksort, and I wanted to boost its performance by parallelizing its execution. I have been refreshing my memory about sorting algorithms the past few days and I've come across a situation where I can't find what the best solution is.
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