So one of the advantages for using static scheduling is that it improves locality in memory access. threads 2 and 3 access non-local memory and it will take them about twice as much time to finish during which time threads 0 and 1 will remain idle.threads 4 to 7 remain idle and half of the compute capability is lost.#pragma omp parallel for schedule(static,1) num_threads(8) You run something like this: char *a = (char *)malloc(8*4096) Then threads 0, 1, 2, and 3 will reside on node 0 and threads 4, 5, 6, and 7 will reside on node 1: | | core 0 | thread 0 |Įach core can access memory from each NUMA node, but remote access is slower (1.5x - 1.9x slower on Intel) than local node access. a two-socket Intel Nehalem board with 4-core CPUs in both sockets. Imagine there are two NUMA nodes: node 0 and node 1, e.g. Then in the second loop the same thread could access the same memory location faster since it will reside on the same NUMA node. This is quite important on NUMA systems: if you touch some memory in the first loop, it will reside on the NUMA node where the executing thread was. The nice thing with static scheduling is that OpenMP run-time guarantees that if you have two separate loops with the same number of iterations and execute them with the same number of threads using static scheduling, then each thread will receive exactly the same iteration range(s) in both parallel regions.
Static schedule means that iterations blocks are mapped statically to the execution threads in a round-robin fashion. Choosing the right schedule can have great impact on the speed of the application. Schedule controls how loop iterations are divided among threads. Others have since answered most of the question but I would like to point to some specific cases where a particular scheduling type is more suited than the others.
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